initial project setup with README and ignore

.dockerignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.egg-info/
+dist/
+build/
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Documentation
+*.md
+!README.md
+
+# Environment
+.env
+.env.local
+
+# Logs
+*.log
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Git
+.git/
+.gitignore
+
+# Docker
+Dockerfile
+docker-compose.yml
+.dockerignore
+
+# Test files
+test_*.py
+*_test.py
+
+# Temporary files
+*.tmp
+*.bak
+

.gitignore

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+.env
+__pycache__/
+*.py[cod]
+*$py.class
+*.pkl
+ml_data/
+output.json
+route.json
+ml_params_output.txt
+idea.txt
+.idea/
+.vscode/

Dockerfile

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+# syntax=docker/dockerfile:1
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1
+
+WORKDIR /app
+
+# Install dependencies first
+COPY requirements.txt ./
+RUN pip install --upgrade pip \
+    && pip install -r requirements.txt
+
+# Copy application code
+COPY app ./app
+COPY start.py ./start.py
+COPY docker-entrypoint.sh ./docker-entrypoint.sh
+
+# Make entrypoint executable
+RUN chmod +x docker-entrypoint.sh
+
+EXPOSE 8002
+
+ENTRYPOINT ["./docker-entrypoint.sh"]

README.md

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+# Route Rider API
+
+Centralized Routing Engine for Rider Assignments.
+
+## Setup
+
+1. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. Run the application:
+   ```bash
+   python start.py
+   ```

app/__init__.py

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+# Delivery Route Optimization API

app/config/__init__.py

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+"""Configuration package for mobile delivery optimization."""

app/config/dynamic_config.py

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+"""
+Dynamic Configuration - rider-api
+
+Replaces all hardcoded hyperparameters with DB-backed values.
+The ML hypertuner writes optimal values here; services read from here.
+
+Fallback: If DB is unavailable or no tuned values exist, defaults are used.
+This means zero risk - the system works day 1 with no data.
+"""
+
+import json
+import logging
+import os
+import sqlite3
+from datetime import datetime
+from typing import Any, Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+# --- DB Path ------------------------------------------------------------------
+_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
+
+
+# --- Hard Defaults (What the system used before ML) ---------------------------
+DEFAULTS: Dict[str, Any] = {
+    # System Strategy / Prompt
+    "ml_strategy":                  "balanced",
+
+    # AssignmentService
+    "max_pickup_distance_km":       10.0,
+    "max_kitchen_distance_km":       3.0,
+    "max_orders_per_rider":         12,
+    "ideal_load":                    6,
+    "workload_balance_threshold":    0.7,
+    "workload_penalty_weight":     100.0,
+    "distance_penalty_weight":       2.0,
+    "preference_bonus":            -15.0,
+    "home_zone_bonus_4km":          -3.0,
+    "home_zone_bonus_2km":          -5.0,
+    "emergency_load_penalty":        3.0,   # km penalty per order in emergency assign
+
+    # RouteOptimizer
+    "search_time_limit_seconds":     5,
+    "avg_speed_kmh":                18.0,
+    "road_factor":                   1.3,
+
+    # ClusteringService
+    "cluster_radius_km":             3.0,
+
+    # KalmanFilter
+    "kalman_process_noise":          1e-4,
+    "kalman_measurement_noise":      0.01,
+
+    # RealisticETACalculator
+    "eta_pickup_time_min":           3.0,
+    "eta_delivery_time_min":         4.0,
+    "eta_navigation_buffer_min":     1.5,
+    "eta_short_trip_factor":         0.8,   # speed multiplier for dist < 2km
+    "eta_long_trip_factor":          1.1,   # speed multiplier for dist > 8km
+}
+
+
+class DynamicConfig:
+    """
+    Thread-safe, DB-backed configuration store.
+
+    Usage:
+        cfg = DynamicConfig()
+        max_dist = cfg.get("max_pickup_distance_km")
+        all_params = cfg.get_all()
+    """
+
+    _instance: Optional["DynamicConfig"] = None
+
+    def __new__(cls) -> "DynamicConfig":
+        """Singleton - one config per process."""
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self):
+        if self._initialized:
+            return
+        self._initialized = True
+        self._cache: Dict[str, Any] = {}
+        self._last_loaded: Optional[datetime] = None
+        self._ensure_db()
+        self._load()
+
+    # --------------------------------------------------------------------------
+    # Public API
+    # --------------------------------------------------------------------------
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """Get a config value. Returns ML-tuned value if available, else default."""
+        self._maybe_reload()
+        val = self._cache.get(key)
+        if val is not None:
+            return val
+        fallback = default if default is not None else DEFAULTS.get(key)
+        return fallback
+
+    def get_all(self) -> Dict[str, Any]:
+        """Return all current config values (ML-tuned + defaults for missing keys)."""
+        self._maybe_reload()
+        result = dict(DEFAULTS)
+        result.update(self._cache)
+        return result
+
+    def set(self, key: str, value: Any, source: str = "manual") -> None:
+        """Write a config value to DB (used by hypertuner)."""
+        try:
+            os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True)
+            conn = sqlite3.connect(_DB_PATH)
+            conn.execute("""
+                INSERT INTO dynamic_config (key, value, source, updated_at)
+                VALUES (?, ?, ?, ?)
+                ON CONFLICT(key) DO UPDATE SET
+                    value=excluded.value,
+                    source=excluded.source,
+                    updated_at=excluded.updated_at
+            """, (key, json.dumps(value), source, datetime.utcnow().isoformat()))
+            conn.commit()
+            conn.close()
+            self._cache[key] = value
+            logger.info(f"[DynamicConfig] Set {key}={value} (source={source})")
+        except Exception as e:
+            logger.error(f"[DynamicConfig] Failed to set {key}: {e}")
+
+    def set_bulk(self, params: Dict[str, Any], source: str = "ml_hypertuner") -> None:
+        """Write multiple config values at once (called after each Optuna study)."""
+        for key, value in params.items():
+            self.set(key, value, source=source)
+        logger.info(f"[DynamicConfig] Bulk update: {len(params)} params from {source}")
+
+    def reset_to_defaults(self) -> None:
+        """Wipe all ML-tuned values, revert to hardcoded defaults."""
+        try:
+            conn = sqlite3.connect(_DB_PATH)
+            conn.execute("DELETE FROM dynamic_config")
+            conn.commit()
+            conn.close()
+            self._cache.clear()
+            logger.warning("[DynamicConfig] Reset to factory defaults.")
+        except Exception as e:
+            logger.error(f"[DynamicConfig] Reset failed: {e}")
+
+    # --------------------------------------------------------------------------
+    # Internal
+    # --------------------------------------------------------------------------
+
+    def _ensure_db(self) -> None:
+        try:
+            os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True)
+            conn = sqlite3.connect(_DB_PATH)
+            conn.execute("""
+                CREATE TABLE IF NOT EXISTS dynamic_config (
+                    key        TEXT PRIMARY KEY,
+                    value      TEXT NOT NULL,
+                    source     TEXT DEFAULT 'manual',
+                    updated_at TEXT
+                )
+            """)
+            conn.commit()
+            conn.close()
+        except Exception as e:
+            logger.error(f"[DynamicConfig] DB init failed: {e}")
+
+    def _load(self) -> None:
+        try:
+            conn = sqlite3.connect(_DB_PATH)
+            rows = conn.execute("SELECT key, value FROM dynamic_config").fetchall()
+            conn.close()
+            self._cache = {}
+            for key, raw in rows:
+                try:
+                    self._cache[key] = json.loads(raw)
+                except Exception:
+                    self._cache[key] = raw
+            self._last_loaded = datetime.utcnow()
+            if self._cache:
+                logger.info(f"[DynamicConfig] Loaded {len(self._cache)} ML-tuned params from DB")
+        except Exception as e:
+            logger.warning(f"[DynamicConfig] Could not load from DB (using defaults): {e}")
+            self._cache = {}
+
+    def _maybe_reload(self, interval_seconds: int = 300) -> None:
+        """Reload from DB every 5 minutes - picks up new tuned params without restart."""
+        if self._last_loaded is None:
+            self._load()
+            return
+        delta = (datetime.utcnow() - self._last_loaded).total_seconds()
+        if delta > interval_seconds:
+            self._load()
+
+
+# --- Module-level convenience singleton ---------------------------------------
+_cfg = DynamicConfig()
+
+
+def get_config() -> DynamicConfig:
+    """Get the global DynamicConfig singleton."""
+    return _cfg

app/config/mobile_config.py

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+"""Mobile-specific configuration for delivery route optimization."""
+
+# Mobile optimization settings
+MOBILE_CONFIG = {
+    "default_algorithm": "greedy",
+    "max_deliveries": 100,
+    "timeout_seconds": 5,
+    "response_compression": True,
+    "performance_monitoring": True,
+    "mobile_headers": True
+}
+
+# Performance targets for mobile
+PERFORMANCE_TARGETS = {
+    "greedy_algorithm": {
+        "max_response_time": 0.1,  # 100ms
+        "max_deliveries": 50,
+        "description": "Ultra-fast for real-time mobile apps"
+    },
+    "tsp_algorithm": {
+        "max_response_time": 3.0,  # 3 seconds
+        "max_deliveries": 30,
+        "description": "Optimal but slower, good for planning"
+    }
+}
+
+# Mobile app recommendations
+MOBILE_RECOMMENDATIONS = {
+    "real_time_delivery": "greedy",
+    "route_planning": "tsp",
+    "large_batches": "greedy",
+    "cost_optimization": "tsp"
+}

app/config/rider_preferences.py

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+"""
+Rider Preferred Kitchens Configuration
+Mapping of Rider ID (int) to list of preferred Kitchen names (str).
+Updated based on Deployment Plan.
+"""
+
+RIDER_PREFERRED_KITCHENS = {
+    # 1. VIVEK ANANDHAN - LOCAL, RS PURAM TO SELVAPURAM
+    1116: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
+
+    # 2. NARAYANASAMY - VENGATAPURAM, VADAVALI, TADAGAM ROAD
+    1096: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
+
+    # 3. VARUN EDWARD - GN MILLS, KAVUNDAMPALAYAM, THUDIYALUR
+    897: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
+
+    # 4. JAYASABAESH - GANAPTHY
+    950: ["Daily grubs nandhini", "Vidhya kitchen"],
+
+    # 5. TAMILALAHZAN - GANDHIMA NAGAR
+    1114: ["Daily grubs nandhini", "Vidhya kitchen"],
+
+    # 6. RAJAN - PEELAMDU
+    883: ["Daily grubs nandhini", "Vidhya kitchen"],
+
+    # 7. MUTHURAJ - RAMANATHAPURAM TO SAIBABACOLONY
+    1272: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen", "Daily grubs nandhini", "Vidhya kitchen"],
+
+    # 8. MANIKANDAN - SINGNALLUR
+    753: ["Daily grubs nandhini", "Vidhya kitchen"],
+
+    # 9. TACHANAMOORTHI - KOVAI PUTHUR TO KAVUNDAMPALAYAM
+    1271: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
+    1133: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"], # Active ID
+}
+
+# Anchor Coordinates for Riders (Based on Area Name)
+# Used as fallback if GPS is missing, or to bias assignment to their Home Zone.
+RIDER_HOME_LOCATIONS = {
+    1116: (11.0067, 76.9558), # VIVEK ANANDAN: RS PURAM
+    1096: (11.0450, 76.9000), # NARAYANASAMY: VADAVALI
+    897:  (11.0430, 76.9380), # VARUN EDWARD: KAVUNDAMPALAYAM
+    950:  (11.0330, 76.9800), # JAYASABESH: GANAPATHY
+    1114: (11.0450, 77.0000), # TAMILAZHAGAN: GANDHIMA NAGAR
+    883:  (11.0200, 77.0000), # RAJAN: PEELAMEDU
+    1272: (10.9950, 77.0000), # MUTHURAJA: RAMANATHAPURAM
+    753:  (11.0000, 77.0300), # MANIKANDAN: SINGANALLUR
+    1271: (10.9500, 76.9600), # THATCHINAMOORTHI: KOVAI PUDUR
+    
+}

app/controllers/__init__.py

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+"""Controllers package."""
+
+from .route_controller import RouteController
+
+__all__ = ["RouteController"]

app/controllers/route_controller.py

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+"""Controller for provider payload optimization and forwarding."""
+
+import logging
+import hashlib
+import json
+from typing import Dict, Any
+import httpx
+from fastapi import HTTPException
+
+from app.core.exceptions import ValidationError, APIException
+from app.services.routing.route_optimizer import RouteOptimizer
+from app.services import cache
+
+logger = logging.getLogger(__name__)
+
+
+class RouteController:
+    """Controller for optimizing provider payloads and forwarding upstream."""
+    
+    def __init__(self):
+        self.route_optimizer = RouteOptimizer()
+    
+    def _hash_key(self, prefix: str, payload: Dict[str, Any]) -> str:
+        """Create a stable cache key from a dict payload."""
+        # ensure deterministic json by sorting keys
+        serialized = json.dumps(payload, sort_keys=True, separators=(",", ":"))
+        digest = hashlib.sha256(serialized.encode("utf-8")).hexdigest()
+        return f"routes:{prefix}:{digest}"
+    
+    async def optimize_and_forward_provider_payload(self, orders: list[dict], forward_url: str) -> dict:
+        """Optimize provider payload and return it (forwarding paused).
+        
+        - Input: list of provider orders (dicts)
+        - Output: {code, details, message, status} where details is the optimized array
+        """
+        try:
+            if not isinstance(orders, list) or not orders:
+                raise ValidationError("Orders array is required", field="body")
+
+            optimized = await self.route_optimizer.optimize_provider_payload(orders)
+
+            # Debug sample of optimized payload (first 3 items, select keys)
+            try:
+                sample = [
+                    {
+                        k: item.get(k)
+                        for k in ("orderheaderid", "orderid", "deliverycustomerid", "step", "previouskms", "cumulativekms", "eta")
+                    }
+                    for item in optimized[:3]
+                ]
+                logger.debug(f"Optimized payload sample: {sample}")
+                trace = [
+                    {
+                        "orderid": item.get("orderid"),
+                        "step": item.get("step"),
+                        "prev": item.get("previouskms"),
+                        "cum": item.get("cumulativekms"),
+                    }
+                    for item in optimized
+                ]
+                logger.debug(f"Optimized order trace: {trace}")
+            except Exception:
+                logger.debug("Optimized payload sample logging failed")
+
+            # Forwarding paused: return optimized payload directly
+            return {
+                "code": 200,
+                "details": optimized,
+                "message": "Success",
+                "status": True,
+            }
+        except ValidationError:
+            raise
+        except httpx.HTTPStatusError as e:
+            status_code = e.response.status_code
+            body_text = e.response.text
+            logger.error(f"Forwarding failed: {status_code} - {body_text}")
+            # Surface upstream details to the client for faster debugging
+            raise APIException(
+                status_code=502,
+                message=f"Upstream service error (status {status_code}): {body_text}",
+                code="UPSTREAM_ERROR"
+            )
+        except Exception as e:
+            logger.error(f"Error optimizing/forwarding provider payload: {e}", exc_info=True)
+            raise APIException(status_code=500, message="Internal server error", code="INTERNAL_ERROR")
+# Batch routes removed - use single-route optimization for each pickup location

app/core/__init__.py

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+"""Core application components."""
+

app/core/arrow_utils.py

@@ -0,0 +1,63 @@
+"""
+High-performance utilities using Apache Arrow and NumPy for geographic data.
+Provides vectorized operations for distances and coordinate processing.
+"""
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import logging
+from typing import List, Dict, Any, Tuple
+
+logger = logging.getLogger(__name__)
+
+def calculate_haversine_matrix_vectorized(lats: np.ndarray, lons: np.ndarray) -> np.ndarray:
+    """
+    Calculate an N x N distance matrix using the Haversine formula.
+    Fully vectorized using NumPy for O(N^2) speed improvement over Python loops.
+    """
+    # Earth's radius in kilometers
+    R = 6371.0
+    
+    # Convert degrees to radians
+    lats_rad = np.radians(lats)
+    lons_rad = np.radians(lons)
+    
+    # Create meshgrids for pairwise differences
+    # lats.reshape(-1, 1) creates a column vector
+    # lats.reshape(1, -1) creates a row vector
+    # Subtracting them creates an N x N matrix of differences
+    dlat = lats_rad.reshape(-1, 1) - lats_rad.reshape(1, -1)
+    dlon = lons_rad.reshape(-1, 1) - lons_rad.reshape(1, -1)
+    
+    # Haversine formula
+    a = np.sin(dlat / 2)**2 + np.cos(lats_rad.reshape(-1, 1)) * np.cos(lats_rad.reshape(1, -1)) * np.sin(dlon / 2)**2
+    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
+    
+    return R * c
+
+def orders_to_arrow_table(orders: List[Dict[str, Any]]) -> pa.Table:
+    """
+    Convert a list of order dictionaries to an Apache Arrow Table.
+    This enables zero-copy operations and efficient columnar storage.
+    """
+    return pa.Table.from_pylist(orders)
+
+def save_optimized_route_parquet(orders: List[Dict[str, Any]], filename: str):
+    """
+    Save optimized route data to a Parquet file for high-speed analysis.
+    Useful for logging and historical simulation replays.
+    """
+    try:
+        table = orders_to_arrow_table(orders)
+        pq.write_table(table, filename)
+        logger.info(f" Saved route data to Parquet: {filename}")
+    except Exception as e:
+        logger.error(f" Failed to save Parquet: {e}")
+
+def load_route_parquet(filename: str) -> List[Dict[str, Any]]:
+    """
+    Load route data from a Parquet file and return as a list of dicts.
+    """
+    table = pq.read_table(filename)
+    return table.to_pylist()

app/core/constants.py

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+"""API constants and configuration."""
+
+# API Configuration
+API_VERSION = "2.0.0"
+API_TITLE = "Route Optimization API"
+API_DESCRIPTION = "Professional API for delivery route optimization"
+
+# Route Optimization Limits
+MAX_DELIVERIES = 50
+MIN_DELIVERIES = 1
+
+# Coordinate Validation
+MIN_LATITUDE = -90
+MAX_LATITUDE = 90
+MIN_LONGITUDE = -180
+MAX_LONGITUDE = 180
+
+# Algorithm Types
+ALGORITHM_GREEDY = "greedy"
+ALGORITHM_TSP = "tsp"
+
+# Response Messages
+MESSAGE_SUCCESS = "Route optimized successfully"
+MESSAGE_VALIDATION_ERROR = "Request validation failed"
+MESSAGE_INTERNAL_ERROR = "An unexpected error occurred"
+

app/core/exception_handlers.py

@@ -0,0 +1,112 @@
+"""Professional exception handlers for the API."""
+
+import logging
+from fastapi import Request, status
+from fastapi.responses import JSONResponse
+from fastapi.exceptions import RequestValidationError
+from starlette.exceptions import HTTPException as StarletteHTTPException
+
+from app.core.exceptions import APIException
+from app.models.errors import ErrorResponse, ErrorDetail
+
+logger = logging.getLogger(__name__)
+
+
+async def api_exception_handler(request: Request, exc: APIException) -> JSONResponse:
+    """Handle custom API exceptions."""
+    request_id = getattr(request.state, "request_id", None)
+    
+    error_response = ErrorResponse(
+        success=False,
+        error=ErrorDetail(
+            field=exc.field,
+            message=exc.message,
+            code=exc.code
+        ),
+        path=request.url.path,
+        request_id=request_id
+    )
+    
+    logger.warning(f"API Exception: {exc.code} - {exc.message} (Request ID: {request_id})")
+    
+    return JSONResponse(
+        status_code=exc.status_code,
+        content=error_response.model_dump(exclude_none=True)
+    )
+
+
+async def http_exception_handler(request: Request, exc: StarletteHTTPException) -> JSONResponse:
+    """Handle HTTP exceptions."""
+    request_id = getattr(request.state, "request_id", None)
+    
+    error_response = ErrorResponse(
+        success=False,
+        error=ErrorDetail(
+            message=exc.detail,
+            code="HTTP_ERROR"
+        ),
+        path=request.url.path,
+        request_id=request_id
+    )
+    
+    logger.warning(f"HTTP Exception: {exc.status_code} - {exc.detail} (Request ID: {request_id})")
+    
+    return JSONResponse(
+        status_code=exc.status_code,
+        content=error_response.model_dump(exclude_none=True)
+    )
+
+
+async def validation_exception_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
+    """Handle validation errors with detailed field information."""
+    request_id = getattr(request.state, "request_id", None)
+    
+    errors = exc.errors()
+    if errors:
+        first_error = errors[0]
+        field = ".".join(str(loc) for loc in first_error.get("loc", []))
+        message = first_error.get("msg", "Validation error")
+    else:
+        field = None
+        message = "Validation error"
+    
+    error_response = ErrorResponse(
+        success=False,
+        error=ErrorDetail(
+            field=field,
+            message=message,
+            code="VALIDATION_ERROR"
+        ),
+        path=request.url.path,
+        request_id=request_id
+    )
+    
+    logger.warning(f"Validation Error: {message} (Field: {field}, Request ID: {request_id})")
+    
+    return JSONResponse(
+        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        content=error_response.model_dump(exclude_none=True)
+    )
+
+
+async def general_exception_handler(request: Request, exc: Exception) -> JSONResponse:
+    """Handle unexpected exceptions."""
+    request_id = getattr(request.state, "request_id", None)
+    
+    error_response = ErrorResponse(
+        success=False,
+        error=ErrorDetail(
+            message="An unexpected error occurred. Please try again later.",
+            code="INTERNAL_SERVER_ERROR"
+        ),
+        path=request.url.path,
+        request_id=request_id
+    )
+    
+    logger.error(f"Unexpected Error: {str(exc)} (Request ID: {request_id})", exc_info=True)
+    
+    return JSONResponse(
+        status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+        content=error_response.model_dump(exclude_none=True)
+    )
+

app/core/exceptions.py

@@ -0,0 +1,70 @@
+"""Custom exceptions for the API."""
+
+from fastapi import HTTPException, status
+
+
+class APIException(HTTPException):
+    """Base API exception with structured error format."""
+    
+    def __init__(
+        self,
+        status_code: int,
+        message: str,
+        field: str = None,
+        code: str = None,
+        detail: str = None
+    ):
+        self.message = message
+        self.field = field
+        self.code = code or self._get_default_code(status_code)
+        super().__init__(status_code=status_code, detail=detail or message)
+    
+    def _get_default_code(self, status_code: int) -> str:
+        """Get default error code based on status code."""
+        codes = {
+            400: "BAD_REQUEST",
+            401: "UNAUTHORIZED",
+            403: "FORBIDDEN",
+            404: "NOT_FOUND",
+            409: "CONFLICT",
+            422: "VALIDATION_ERROR",
+            429: "RATE_LIMIT_EXCEEDED",
+            500: "INTERNAL_SERVER_ERROR",
+            503: "SERVICE_UNAVAILABLE"
+        }
+        return codes.get(status_code, "UNKNOWN_ERROR")
+
+
+class ValidationError(APIException):
+    """Validation error exception."""
+    
+    def __init__(self, message: str, field: str = None):
+        super().__init__(
+            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+            message=message,
+            field=field,
+            code="VALIDATION_ERROR"
+        )
+
+
+class NotFoundError(APIException):
+    """Resource not found exception."""
+    
+    def __init__(self, message: str = "Resource not found"):
+        super().__init__(
+            status_code=status.HTTP_404_NOT_FOUND,
+            message=message,
+            code="NOT_FOUND"
+        )
+
+
+class RateLimitError(APIException):
+    """Rate limit exceeded exception."""
+    
+    def __init__(self, message: str = "Rate limit exceeded"):
+        super().__init__(
+            status_code=status.HTTP_429_TOO_MANY_REQUESTS,
+            message=message,
+            code="RATE_LIMIT_EXCEEDED"
+        )
+

app/main.py

@@ -0,0 +1,263 @@
+"""Professional FastAPI application for delivery route optimization."""
+
+import logging
+import os
+import sys
+import time
+import threading
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, Request, status
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.middleware.gzip import GZipMiddleware
+from fastapi.exceptions import RequestValidationError
+from starlette.exceptions import HTTPException as StarletteHTTPException
+
+from app.routes import optimization_router, health_router, cache_router, ml_router, ml_web_router
+from app.middleware.request_id import RequestIDMiddleware
+from app.core.exceptions import APIException
+from app.core.exception_handlers import (
+    api_exception_handler,
+    http_exception_handler,
+    validation_exception_handler,
+    general_exception_handler
+)
+
+# Configure professional logging with env control
+_log_level_name = os.getenv("LOG_LEVEL", "INFO").upper()
+_log_level = getattr(logging, _log_level_name, logging.INFO)
+logging.basicConfig(
+    level=_log_level,
+    format="%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger(__name__)
+
+# Ensure root and key libraries honor desired level
+logging.getLogger().setLevel(_log_level)
+logging.getLogger("httpx").setLevel(_log_level)
+logging.getLogger("uvicorn").setLevel(_log_level)
+logging.getLogger("uvicorn.error").setLevel(_log_level)
+logging.getLogger("uvicorn.access").setLevel(_log_level)
+
+
+# --- Smart Post-Call ML Trainer ----------------------------------------------------------
+#
+# Trains in a BACKGROUND THREAD after every N /riderassign calls.
+# - The API response is NEVER blocked - training is fully async.
+# - Cooldown prevents overlapping runs (won't train if one is already running).
+# - MIN_RECORDS guard: won't attempt if DB doesn't have enough data yet.
+#
+# Config:
+#   TRAIN_EVERY_N_CALLS : retrain after this many calls         (default: 10)
+#   MIN_RECORDS_TO_TRAIN: minimum DB rows before first train    (default: 30)
+#   COOLDOWN_SECONDS    : min gap between two training runs     (default: 120s)
+# -------------------------------------------------------------------
+
+TRAIN_EVERY_N_CALLS  = int(os.getenv("ML_TRAIN_EVERY_N",  "10"))
+MIN_RECORDS_TO_TRAIN = int(os.getenv("ML_MIN_RECORDS",    "30"))
+COOLDOWN_SECONDS     = int(os.getenv("ML_COOLDOWN_SEC",  "120"))
+
+_call_counter     = 0
+_counter_lock     = threading.Lock()
+_training_lock    = threading.Lock()
+_last_trained_at  = 0.0   # epoch seconds
+
+
+def _run_training_background():
+    """
+    The actual training job - runs in a daemon thread.
+    Fully safe to call while the API is serving requests.
+    """
+    global _last_trained_at
+
+    # Acquire lock - only ONE training run at a time
+    if not _training_lock.acquire(blocking=False):
+        logger.info("[MLTrigger] Training already running - skipping this trigger.")
+        return
+
+    try:
+        from app.services.ml.ml_hypertuner import get_hypertuner
+        from app.services.ml.ml_data_collector import get_collector
+
+        count = get_collector().count_records()
+        if count < MIN_RECORDS_TO_TRAIN:
+            logger.info(f"[MLTrigger] Only {count} records - need >={MIN_RECORDS_TO_TRAIN}. Skipping.")
+            return
+
+        logger.info(f"[MLTrigger] [ML] Background hypertuning started ({count} records)...")
+        result = get_hypertuner().run(n_trials=100)
+
+        if result.get("status") == "ok":
+            _last_trained_at = time.time()
+            logger.info(
+                f"[MLTrigger] [OK] Hypertuning done - "
+                f"quality={result.get('best_predicted_quality', '?')}/100 "
+                f"| {result.get('training_rows', '?')} rows "
+                f"| {result.get('trials_run', '?')} trials"
+            )
+        else:
+            logger.info(f"[MLTrigger] Hypertuning skipped: {result.get('message', '')}")
+
+    except Exception as e:
+        logger.error(f"[MLTrigger] Background training error: {e}", exc_info=True)
+    finally:
+        _training_lock.release()
+
+
+def trigger_training_if_due():
+    """
+    Called after every /riderassign call.
+    Increments counter - fires background thread every TRAIN_EVERY_N_CALLS.
+    Non-blocking: returns immediately regardless.
+    """
+    global _call_counter, _last_trained_at
+
+    with _counter_lock:
+        _call_counter += 1
+        should_train = (_call_counter % TRAIN_EVERY_N_CALLS == 0)
+
+    if not should_train:
+        return
+
+    # Cooldown check - don't train if we just trained recently
+    elapsed = time.time() - _last_trained_at
+    if elapsed < COOLDOWN_SECONDS:
+        logger.info(
+            f"[MLTrigger] Cooldown active - "
+            f"{int(COOLDOWN_SECONDS - elapsed)}s remaining. Skipping."
+        )
+        return
+
+    # Fire background thread - does NOT block the API response
+    t = threading.Thread(target=_run_training_background, daemon=True, name="ml-hypertuner")
+    t.start()
+    logger.info(f"[MLTrigger] [START] Background training thread launched (call #{_call_counter})")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan events."""
+    logger.info("[START] Starting Route Optimization API...")
+
+    # -- On startup: if enough data exists, train immediately in background --
+    try:
+        from app.services.ml.ml_data_collector import get_collector
+        count = get_collector().count_records()
+        if count >= MIN_RECORDS_TO_TRAIN:
+            logger.info(f"[Startup] {count} records found -> launching startup hypertuning...")
+            t = threading.Thread(target=_run_training_background, daemon=True, name="ml-startup")
+            t.start()
+        else:
+            logger.info(
+                f"[Startup] {count}/{MIN_RECORDS_TO_TRAIN} records in ML DB - "
+                f"will auto-train after every {TRAIN_EVERY_N_CALLS} /riderassign calls."
+            )
+    except Exception as e:
+        logger.warning(f"[Startup] ML status check failed (non-fatal): {e}")
+
+    logger.info(
+        f"[OK] Application initialized - "
+        f"ML trains every {TRAIN_EVERY_N_CALLS} calls "
+        f"(cooldown {COOLDOWN_SECONDS}s, min {MIN_RECORDS_TO_TRAIN} records)"
+    )
+    yield
+
+    logger.info(" Shutting down Route Optimization API...")
+
+
+# Create FastAPI application with professional configuration
+app = FastAPI(
+    title="Route Optimization API",
+    version="2.0.0",
+    docs_url="/docs",
+    redoc_url="/redoc",
+    openapi_url="/api/v1/openapi.json",
+    lifespan=lifespan
+)
+
+# Add Request ID middleware (must be first)
+app.add_middleware(RequestIDMiddleware)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Configure specific domains in production
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+    allow_headers=["*"],
+    expose_headers=["X-Request-ID", "X-Process-Time"]
+)
+
+# Add GZIP compression
+app.add_middleware(GZipMiddleware, minimum_size=1000)
+
+# Add request timing middleware
+@app.middleware("http")
+async def add_process_time_header(request: Request, call_next):
+    """Add performance monitoring headers."""
+    start_time = time.time()
+    response = await call_next(request)
+    process_time = time.time() - start_time
+    response.headers["X-Process-Time"] = str(round(process_time, 4))
+    response.headers["X-API-Version"] = "2.0.0"
+    return response
+
+# Register exception handlers
+app.add_exception_handler(APIException, api_exception_handler)
+app.add_exception_handler(StarletteHTTPException, http_exception_handler)
+app.add_exception_handler(RequestValidationError, validation_exception_handler)
+app.add_exception_handler(Exception, general_exception_handler)
+
+# Include routers
+app.include_router(optimization_router)
+app.include_router(health_router)
+app.include_router(cache_router)
+app.include_router(ml_router)
+app.include_router(ml_web_router)
+
+
+@app.get("/", tags=["Root"])
+async def root(request: Request):
+    """
+    API root endpoint with service information.
+    
+    Returns API metadata, available endpoints, and usage information.
+    """
+    request_id = getattr(request.state, "request_id", None)
+    
+    return {
+        "service": "Route Optimization API",
+        "version": "2.0.0",
+        "status": "operational",
+        "documentation": {
+            "swagger": "/docs",
+            "redoc": "/redoc",
+            "openapi": "/api/v1/openapi.json"
+        },
+        "endpoints": {
+            "createdeliveries": {
+                "url": "/api/v1/optimization/createdeliveries",
+                "method": "POST",
+                "description": "Accept provider array, optimize order, add step/previouskms/cumulativekms, forward upstream"
+            },
+            "health": {
+                "url": "/api/v1/health",
+                "method": "GET",
+                "description": "Health check endpoint"
+            }
+        },
+        "features": {
+            "algorithm": "Greedy Nearest-Neighbor",
+            "optimization": "Provider array reordering with distance metrics",
+            "added_fields": ["step", "previouskms", "cumulativekms", "actualkms"]
+        },
+        "request_id": request_id
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app.main:app", host="0.0.0.0", port=8002, reload=True)

app/middleware/__init__.py

@@ -0,0 +1,2 @@
+"""Middleware components."""
+

app/middleware/request_id.py

@@ -0,0 +1,26 @@
+"""Request ID middleware for request tracing."""
+
+import uuid
+from fastapi import Request
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.responses import Response
+
+
+class RequestIDMiddleware(BaseHTTPMiddleware):
+    """Middleware to add unique request ID to each request."""
+    
+    async def dispatch(self, request: Request, call_next):
+        # Generate or retrieve request ID
+        request_id = request.headers.get("X-Request-ID") or str(uuid.uuid4())
+        
+        # Add request ID to request state
+        request.state.request_id = request_id
+        
+        # Process request
+        response = await call_next(request)
+        
+        # Add request ID to response headers
+        response.headers["X-Request-ID"] = request_id
+        
+        return response
+

app/models/__init__.py

@@ -0,0 +1,21 @@
+"""Models package."""
+
+from .schemas import (
+    Location,
+    Delivery,
+    RouteOptimizationRequest,
+    RouteStep,
+    OptimizedRoute,
+    PickupLocation,
+    DeliveryLocation
+)
+
+__all__ = [
+    "Location",
+    "Delivery",
+    "RouteOptimizationRequest",
+    "RouteStep",
+    "OptimizedRoute",
+    "PickupLocation",
+    "DeliveryLocation"
+]

app/models/errors.py

@@ -0,0 +1,45 @@
+"""Professional error response models for API."""
+
+from typing import Optional, Any, Dict
+from pydantic import BaseModel, Field
+from datetime import datetime
+
+
+class ErrorDetail(BaseModel):
+    """Detailed error information."""
+    field: Optional[str] = Field(None, description="Field name that caused the error")
+    message: str = Field(..., description="Error message")
+    code: Optional[str] = Field(None, description="Error code")
+
+
+class ErrorResponse(BaseModel):
+    """Standardized error response model."""
+    success: bool = Field(False, description="Request success status")
+    error: ErrorDetail = Field(..., description="Error details")
+    timestamp: str = Field(default_factory=lambda: datetime.utcnow().isoformat(), description="Error timestamp")
+    path: Optional[str] = Field(None, description="Request path")
+    request_id: Optional[str] = Field(None, description="Request ID for tracing")
+    
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "success": False,
+                "error": {
+                    "field": "pickup_location",
+                    "message": "Pickup location is required",
+                    "code": "VALIDATION_ERROR"
+                },
+                "timestamp": "2024-01-15T10:30:00.000Z",
+                "path": "/api/v1/optimization/single-route",
+                "request_id": "req-123456"
+            }
+        }
+
+
+class SuccessResponse(BaseModel):
+    """Standardized success response wrapper."""
+    success: bool = Field(True, description="Request success status")
+    data: Any = Field(..., description="Response data")
+    timestamp: str = Field(default_factory=lambda: datetime.utcnow().isoformat(), description="Response timestamp")
+    request_id: Optional[str] = Field(None, description="Request ID for tracing")
+

app/models/schemas.py

@@ -0,0 +1,167 @@
+"""Professional Pydantic models for request/response validation."""
+
+from typing import List, Optional
+from pydantic import BaseModel, Field, field_validator
+from datetime import datetime
+
+
+class Location(BaseModel):
+    """Location model with latitude and longitude."""
+    lat: float = Field(..., description="Latitude")
+    lng: float = Field(..., description="Longitude")
+
+
+class PickupLocation(BaseModel):
+    """Pickup location model with latitude and longitude."""
+    pickuplat: float = Field(
+        ..., 
+        description="Pickup latitude",
+        ge=-90, 
+        le=90,
+        examples=[11.0050534]
+    )
+    pickuplon: float = Field(
+        ..., 
+        description="Pickup longitude",
+        ge=-180, 
+        le=180,
+        examples=[76.9508991]
+    )
+    
+    @field_validator("pickuplat", "pickuplon")
+    @classmethod
+    def validate_coordinates(cls, v):
+        """Validate coordinate values."""
+        if v is None:
+            raise ValueError("Coordinate cannot be None")
+        return float(v)
+
+
+class DeliveryLocation(BaseModel):
+    """Delivery location model with latitude and longitude."""
+    deliverylat: float = Field(
+        ..., 
+        description="Delivery latitude",
+        ge=-90, 
+        le=90,
+        examples=[11.0309723]
+    )
+    deliverylong: float = Field(
+        ..., 
+        description="Delivery longitude",
+        ge=-180, 
+        le=180,
+        examples=[77.0004574]
+    )
+    
+    @field_validator("deliverylat", "deliverylong")
+    @classmethod
+    def validate_coordinates(cls, v):
+        """Validate coordinate values."""
+        if v is None:
+            raise ValueError("Coordinate cannot be None")
+        return float(v)
+
+
+class Delivery(BaseModel):
+    """Delivery order model."""
+    deliveryid: str = Field(..., description="Unique delivery identifier")
+    deliverycustomerid: int = Field(..., description="Customer ID for this delivery")
+    location: DeliveryLocation = Field(..., description="Delivery location coordinates")
+
+
+class RouteOptimizationRequest(BaseModel):
+    """
+    Request model for route optimization.
+    
+    Optimizes delivery routes starting from a pickup location (warehouse/store) to multiple delivery locations.
+    Uses greedy nearest-neighbor algorithm for fast, efficient route calculation.
+    """
+    pickup_location: PickupLocation = Field(
+        ..., 
+        description="Pickup location (warehouse/store) coordinates - starting point for optimization"
+    )
+    pickup_location_id: Optional[int] = Field(
+        None, 
+        description="Optional pickup location ID for tracking purposes"
+    )
+    deliveries: List[Delivery] = Field(
+        ..., 
+        min_items=1,
+        max_items=50,
+        description="List of delivery locations to optimize (1-50 deliveries supported)"
+    )
+    
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "pickup_location": {
+                    "pickuplat": 11.0050534,
+                    "pickuplon": 76.9508991
+                },
+                "pickup_location_id": 1,
+                "deliveries": [
+                    {
+                        "deliveryid": "90465",
+                        "deliverycustomerid": 1,
+                        "location": {
+                            "deliverylat": 11.0309723,
+                            "deliverylong": 77.0004574
+                        }
+                    }
+                ]
+            }
+        }
+
+
+class RouteStep(BaseModel):
+    """Single step in the optimized route."""
+    step_number: int = Field(..., description="Step number in the route")
+    delivery_id: str = Field(..., description="Delivery ID for this step")
+    delivery_customer_id: int = Field(..., description="Customer ID for this delivery")
+    location: DeliveryLocation = Field(..., description="Delivery location coordinates")
+    distance_from_previous_km: float = Field(..., description="Distance from previous step in kilometers")
+    cumulative_distance_km: float = Field(..., description="Total distance traveled so far in kilometers")
+
+
+class OptimizedRoute(BaseModel):
+    """
+    Optimized route response with step-by-step delivery sequence.
+    
+    Contains the optimized route starting from pickup location, with each step showing:
+    - Delivery order (Step 1, Step 2, etc.)
+    - Distance from previous step
+    - Cumulative distance traveled
+    """
+    route_id: str = Field(..., description="Unique route identifier (UUID)")
+    pickup_location_id: Optional[int] = Field(None, description="Pickup location ID")
+    pickup_location: PickupLocation = Field(..., description="Pickup location (warehouse/store) coordinates")
+    total_distance_km: float = Field(
+        ..., 
+        ge=0,
+        description="Total route distance in kilometers",
+        examples=[12.45]
+    )
+    total_deliveries: int = Field(
+        ..., 
+        ge=1,
+        description="Total number of deliveries in the route",
+        examples=[5]
+    )
+    optimization_algorithm: str = Field(
+        "greedy",
+        description="Algorithm used for optimization",
+        examples=["greedy"]
+    )
+    steps: List[RouteStep] = Field(
+        ..., 
+        description="Ordered list of route steps (Step 1 = nearest from pickup, Step 2 = nearest from Step 1, etc.)"
+    )
+    created_at: str = Field(
+        default_factory=lambda: datetime.utcnow().isoformat(),
+        description="Route creation timestamp (ISO 8601)"
+    )
+
+
+# Batch optimization removed - no rider support needed
+# Use single-route optimization for each pickup location

app/routes/__init__.py

@@ -0,0 +1,8 @@
+"""Routes package."""
+
+from .optimization import router as optimization_router
+from .health import router as health_router
+from .cache import router as cache_router
+from .ml_admin import router as ml_router, web_router as ml_web_router
+
+__all__ = ["optimization_router", "health_router", "cache_router", "ml_router", "ml_web_router"]

app/routes/cache.py

@@ -0,0 +1,79 @@
+"""Cache management API endpoints."""
+
+import logging
+from fastapi import APIRouter, HTTPException
+from typing import Dict, Any
+
+from app.services import cache
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/cache", tags=["Cache Management"])
+
+
+@router.get("/stats", response_model=Dict[str, Any])
+async def get_cache_stats():
+    """
+    Get cache statistics.
+    
+    Returns:
+    - hits: Number of cache hits
+    - misses: Number of cache misses  
+    - sets: Number of cache writes
+    - total_keys: Current number of cached route keys
+    - enabled: Whether Redis cache is enabled
+    """
+    try:
+        stats = cache.get_stats()
+        # Calculate hit rate
+        total_requests = stats.get("hits", 0) + stats.get("misses", 0)
+        if total_requests > 0:
+            stats["hit_rate"] = round(stats.get("hits", 0) / total_requests * 100, 2)
+        else:
+            stats["hit_rate"] = 0.0
+        return stats
+    except Exception as e:
+        logger.error(f"Error getting cache stats: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+
+@router.get("/keys")
+async def list_cache_keys(pattern: str = "routes:*"):
+    """
+    List cache keys matching pattern.
+    
+    - **pattern**: Redis key pattern (default: "routes:*")
+    """
+    try:
+        keys = cache.get_keys(pattern)
+        return {
+            "pattern": pattern,
+            "count": len(keys),
+            "keys": keys[:100]  # Limit to first 100 for response size
+        }
+    except Exception as e:
+        logger.error(f"Error listing cache keys: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+
+@router.delete("/clear")
+async def clear_cache(pattern: str = "routes:*"):
+    """
+    Clear cache keys matching pattern.
+    
+    - **pattern**: Redis key pattern to delete (default: "routes:*")
+    
+    [WARN] **Warning**: This will delete cached route optimizations!
+    """
+    try:
+        deleted_count = cache.delete(pattern)
+        logger.info(f"Cleared {deleted_count} cache keys matching pattern: {pattern}")
+        return {
+            "pattern": pattern,
+            "deleted_count": deleted_count,
+            "message": f"Cleared {deleted_count} cache keys"
+        }
+    except Exception as e:
+        logger.error(f"Error clearing cache: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+

app/routes/health.py

@@ -0,0 +1,98 @@
+"""Professional health check endpoints."""
+
+import time
+import logging
+import sys
+from typing import Optional
+from datetime import datetime
+from fastapi import APIRouter, Request
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/health", tags=["Health"])
+
+start_time = time.time()
+
+
+class HealthResponse(BaseModel):
+    """Health check response model."""
+    status: str = Field(..., description="Service status")
+    uptime_seconds: float = Field(..., description="Service uptime in seconds")
+    version: str = Field("2.0.0", description="API version")
+    timestamp: str = Field(..., description="Health check timestamp (ISO 8601)")
+    request_id: Optional[str] = Field(None, description="Request ID for tracing")
+
+
+@router.get("/", response_model=HealthResponse)
+async def health_check(request: Request):
+    """
+    Health check endpoint.
+    
+    Returns the current health status of the API service including:
+    - Service status (healthy/unhealthy)
+    - Uptime in seconds
+    - API version
+    - Timestamp
+    """
+    try:
+        uptime = time.time() - start_time
+        request_id = getattr(request.state, "request_id", None)
+        
+        return HealthResponse(
+            status="healthy",
+            uptime_seconds=round(uptime, 2),
+            version="2.0.0",
+            timestamp=datetime.utcnow().isoformat() + "Z",
+            request_id=request_id
+        )
+    except Exception as e:
+        logger.error(f"Health check failed: {e}", exc_info=True)
+        request_id = getattr(request.state, "request_id", None)
+        
+        return HealthResponse(
+            status="unhealthy",
+            uptime_seconds=0.0,
+            version="2.0.0",
+            timestamp=datetime.utcnow().isoformat() + "Z",
+            request_id=request_id
+        )
+
+
+@router.get("/ready")
+async def readiness_check(request: Request):
+    """
+    Readiness check endpoint for load balancers.
+    
+    Returns 200 if the service is ready to accept requests.
+    """
+    try:
+        # Check if critical services are available
+        # Add your service health checks here
+        
+        return {
+            "status": "ready",
+            "timestamp": datetime.utcnow().isoformat() + "Z",
+            "request_id": getattr(request.state, "request_id", None)
+        }
+    except Exception as e:
+        logger.error(f"Readiness check failed: {e}")
+        return {
+            "status": "not_ready",
+            "timestamp": datetime.utcnow().isoformat() + "Z",
+            "request_id": getattr(request.state, "request_id", None)
+        }
+
+
+@router.get("/live")
+async def liveness_check(request: Request):
+    """
+    Liveness check endpoint for container orchestration.
+    
+    Returns 200 if the service is alive.
+    """
+    return {
+        "status": "alive",
+        "timestamp": datetime.utcnow().isoformat() + "Z",
+        "request_id": getattr(request.state, "request_id", None)
+    }

app/routes/ml_admin.py

@@ -0,0 +1,286 @@
+"""
+ML Admin API - rider-api
+
+Endpoints:
+  GET  /api/v1/ml/status         - DB record count, quality trend, model info
+  GET  /api/v1/ml/config         - Current active hyperparameters (ML-tuned + defaults)
+  POST /api/v1/ml/train          - Trigger hypertuning immediately
+  POST /api/v1/ml/reset          - Reset config to factory defaults
+  GET  /api/v1/ml/reports        - List past tuning reports
+"""
+
+import logging
+import os
+import json
+from fastapi import APIRouter, HTTPException, Body, Request
+from fastapi.responses import FileResponse, PlainTextResponse
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(
+    prefix="/api/v1/ml",
+    tags=["ML Hypertuner"],
+    responses={
+        500: {"description": "Internal server error"}
+    }
+)
+
+web_router = APIRouter(
+    tags=["ML Monitor Web Dashboard"]
+)
+
+
+
+# -----------------------------------------------------------------------------
+# GET /ml-ops
+# -----------------------------------------------------------------------------
+
+@web_router.get("/ml-ops", summary="Visual ML monitoring dashboard")
+def ml_dashboard():
+    """Returns the beautiful HTML dashboard for visualizing ML progress."""
+    path = os.path.join(os.getcwd(), "app/templates/ml_dashboard.html")
+    if not os.path.isfile(path):
+        raise HTTPException(status_code=404, detail=f"Dashboard template not found at {path}")
+    return FileResponse(path)
+
+
+
+# -----------------------------------------------------------------------------
+# GET /status
+# -----------------------------------------------------------------------------
+
+@router.get("/status", summary="ML system status & quality trend")
+def ml_status():
+    """
+    Returns:
+    - How many assignment events are logged
+    - Recent quality score trend (avg / min / max over last 20 calls)
+    - Whether the model has been trained
+    - Current hyperparameter source (ml_tuned vs defaults)
+    """
+    from app.services.ml.ml_data_collector import get_collector
+    from app.services.ml.ml_hypertuner import get_hypertuner
+
+    try:
+        collector = get_collector()
+        tuner     = get_hypertuner()
+
+        record_count   = collector.count_records()
+        quality_trend  = collector.get_recent_quality_trend(last_n=50)
+        model_info     = tuner.get_model_info()
+        
+        from app.services.ml.behavior_analyzer import get_analyzer
+        b_analyzer = get_analyzer()
+        
+        from app.config.dynamic_config import get_config
+        cfg = get_config()
+
+        return {
+            "status": "ok",
+            "db_records": record_count,
+            "ready_to_train": record_count >= 30,
+            "quality_trend": quality_trend,
+            "hourly_stats": collector.get_hourly_stats(),
+            "quality_histogram": collector.get_quality_histogram(),
+            "strategy_comparison": collector.get_strategy_comparison(),
+            "zone_stats": collector.get_zone_stats(),
+            "behavior": b_analyzer.get_info() if hasattr(b_analyzer, 'get_info') else {},
+            "config": cfg.get_all(),
+            "model": model_info,
+            "message": (
+                f"Collecting data - need {max(0, 30 - record_count)} more records to train."
+                if record_count < 30
+                else "Ready to train! Call POST /api/v1/ml/train"
+                if not model_info["model_trained"]
+                else "Model trained and active."
+            )
+        }
+    except Exception as e:
+        logger.error(f"[ML API] Status failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# -----------------------------------------------------------------------------
+# GET /config
+# -----------------------------------------------------------------------------
+
+@router.get("/config", summary="Current active hyperparameter values")
+def ml_config():
+    """
+    Returns every hyperparameter currently in use by the system.
+    Values marked 'ml_tuned' were set by the ML model.
+    Values marked 'default' are factory defaults (not yet tuned).
+    """
+    from app.config.dynamic_config import get_config, DEFAULTS
+
+    try:
+        cfg          = get_config()
+        all_values   = cfg.get_all()
+        cached_keys  = set(cfg._cache.keys())
+
+        annotated = {}
+        for k, v in all_values.items():
+            annotated[k] = {
+                "value":  v,
+                "source": "ml_tuned" if k in cached_keys else "default",
+            }
+
+        return {
+            "status": "ok",
+            "hyperparameters": annotated,
+            "total_params": len(annotated),
+            "ml_tuned_count": sum(1 for x in annotated.values() if x["source"] == "ml_tuned"),
+        }
+    except Exception as e:
+        logger.error(f"[ML API] Config fetch failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.patch("/config", summary="Update specific ML configuration defaults")
+def ml_config_patch(payload: dict = Body(...)):
+    """Allows updating any active parameter via JSON overrides. e.g. \{ \"ml_strategy\": \"balanced\" \}"""
+    from app.config.dynamic_config import get_config
+    try:
+        cfg = get_config()
+        cfg.set_bulk(payload, source="ml_admin")
+        return {"status": "ok"}
+    except Exception as e:
+        logger.error(f"[ML API] Config patch failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# -----------------------------------------------------------------------------
+# POST /train
+# -----------------------------------------------------------------------------
+
+@router.post("/train", summary="Trigger XGBoost training + Optuna hyperparameter search")
+def ml_train(
+    n_trials: int = Body(default=100, embed=True, ge=10, le=500,
+                         description="Number of Optuna trials (10500)"),
+    min_records: int = Body(default=30, embed=True, ge=10,
+                            description="Minimum DB records required")
+):
+    """
+    Runs the full hypertuning pipeline:
+    1. Load logged assignment data from DB
+    2. Train XGBoost surrogate model
+    3. Run Optuna TPE search ({n_trials} trials)
+    4. Write optimal params to DynamicConfig
+
+    The AssignmentService picks up new params within 5 minutes (auto-reload).
+    """
+    from app.services.ml.ml_hypertuner import get_hypertuner
+
+    try:
+        logger.info(f"[ML API] Hypertuning triggered: n_trials={n_trials}, min_records={min_records}")
+        tuner  = get_hypertuner()
+        result = tuner.run(n_trials=n_trials, min_training_records=min_records)
+        return result
+    except Exception as e:
+        logger.error(f"[ML API] Training failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# -----------------------------------------------------------------------------
+# POST /reset
+# -----------------------------------------------------------------------------
+
+@router.post("/reset", summary="Reset all hyperparameters to factory defaults")
+def ml_reset():
+    """
+    Wipes all ML-tuned config values and reverts every parameter to the
+    original hardcoded defaults. Useful if the model produced bad results.
+    """
+    from app.config.dynamic_config import get_config
+
+    try:
+        get_config().reset_to_defaults()
+        return {
+            "status": "ok",
+            "message": "All hyperparameters reset to factory defaults.",
+        }
+    except Exception as e:
+        logger.error(f"[ML API] Reset failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+# -----------------------------------------------------------------------------
+# POST /strategy
+# -----------------------------------------------------------------------------
+
+@router.post("/strategy", summary="Change the AI Optimization Prompt/Strategy")
+def ml_strategy(strategy: str = Body(default="balanced", embed=True)):
+    """
+    Changes the mathematical objective of the AI.
+    Choices: 'balanced', 'fuel_saver', 'aggressive_speed', 'zone_strict'
+    
+    Historical data is NOT wiped. Instead, the AI dynamically recalculates 
+    the quality score of all past events using the new strategy rules.
+    """
+    from app.config.dynamic_config import get_config
+    import sqlite3
+    
+    valid = ["balanced", "fuel_saver", "aggressive_speed", "zone_strict"]
+    if strategy not in valid:
+        raise HTTPException(400, f"Invalid strategy. Choose from {valid}")
+        
+    try:
+        get_config().set("ml_strategy", strategy)
+        
+        return {
+            "status": "ok", 
+            "message": f"Strategy changed to '{strategy}'. Historical AI data will be mathematically repurposed to train towards this new goal.",
+            "strategy": strategy
+        }
+    except Exception as e:
+        logger.error(f"[ML API] Strategy change failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# -----------------------------------------------------------------------------
+# GET /reports
+# -----------------------------------------------------------------------------
+
+@router.get("/reports", summary="List past hypertuning reports")
+def ml_reports():
+    """Returns the last 10 tuning reports (JSON files in ml_data/reports/)."""
+    try:
+        report_dir = "ml_data/reports"
+        if not os.path.isdir(report_dir):
+            return {"status": "ok", "reports": [], "message": "No reports yet."}
+
+        files = sorted(
+            [f for f in os.listdir(report_dir) if f.endswith(".json")],
+            reverse=True
+        )[:10]
+
+        reports = []
+        for fname in files:
+            path = os.path.join(report_dir, fname)
+            try:
+                with open(path) as f:
+                    reports.append(json.load(f))
+            except Exception:
+                pass
+
+        return {"status": "ok", "reports": reports, "count": len(reports)}
+    except Exception as e:
+        logger.error(f"[ML API] Reports fetch failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# -----------------------------------------------------------------------------
+# GET /export
+# -----------------------------------------------------------------------------
+
+@router.get("/export", summary="Export all records as CSV")
+def ml_export():
+    """Generates a CSV string containing all rows in the assignment_ml_log table."""
+    try:
+        from app.services.ml.ml_data_collector import get_collector
+        csv_data = get_collector().export_csv()
+        response = PlainTextResponse(content=csv_data, media_type="text/csv")
+        response.headers["Content-Disposition"] = 'attachment; filename="ml_export.csv"'
+        return response
+    except Exception as e:
+        logger.error(f"[ML API] Export failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))

app/routes/optimization.py

@@ -0,0 +1,364 @@
+"""Provider payload optimization endpoints."""
+
+import logging
+import time
+from fastapi import APIRouter, Request, Depends, status, HTTPException, Query
+
+from app.controllers.route_controller import RouteController
+from app.core.exceptions import APIException
+from app.core.arrow_utils import save_optimized_route_parquet
+import os
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(
+    prefix="/api/v1/optimization", 
+    tags=["Route Optimization"],
+    responses={
+        400: {"description": "Bad request - Invalid input parameters"},
+        422: {"description": "Validation error - Request validation failed"},
+        500: {"description": "Internal server error"}
+    }
+)
+
+def get_route_controller() -> RouteController:
+    """Dependency injection for route controller."""
+    return RouteController()
+
+
+# Legacy single-route endpoint removed; provider flow only.
+@router.post(
+    "/createdeliveries",
+    status_code=status.HTTP_200_OK,
+    summary="Optimize provider payload (forwarding paused)",
+    description="""
+    Accepts the provider's orders array, reorders it using greedy nearest-neighbor, adds only:
+    - step (1..N)
+    - previouskms (distance from previous stop in km)
+    - cumulativekms (total distance so far in km)
+    - actualkms (direct pickup-to-delivery distance)
+
+    Forwarding is temporarily paused: returns the optimized array in the response.
+    """,
+    responses={
+        200: {
+            "description": "Upstream response",
+            "content": {
+                "application/json": {
+                    "example": {"code": 200, "details": [], "message": "Success", "status": True}
+                }
+            }
+        }
+    }
+)
+async def provider_optimize_forward(
+    body: list[dict],
+    controller: RouteController = Depends(get_route_controller)
+):
+    """
+    Accept provider JSON array, reorder by greedy nearest-neighbor, annotate each item with:
+    - step (1..N)
+    - previouskms (km from previous point)
+    - cumulativekms (km so far)
+    - actualkms (pickup to delivery distance)
+    Then forward the optimized array to the external API and return only its response.
+    """
+    try:
+        url = "https://jupiter.nearle.app/live/api/v1/deliveries/createdeliveries"
+        result = await controller.optimize_and_forward_provider_payload(body, url)
+        
+        # Performance Logging: Save a Parquet Snapshot (Async-friendly backup)
+        try:
+            os.makedirs("data/snapshots", exist_ok=True)
+            snapshot_path = f"data/snapshots/route_{int(time.time())}.parquet"
+            save_optimized_route_parquet(body, snapshot_path)
+            logger.info(f"Apache Arrow: Snapshot saved to {snapshot_path}")
+        except Exception as e:
+            logger.warning(f"Could not save Arrow snapshot: {e}")
+            
+        return result
+    except APIException:
+        raise
+    except Exception as e:
+        logger.error(f"Unexpected error in provider_optimize_forward: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+
+@router.get(
+    "/createdeliveries",
+    summary="Usage info for provider optimize forward"
+)
+async def provider_optimize_forward_info():
+    """Return usage info; this endpoint accepts POST only for processing."""
+    return {
+        "message": "Use POST with a JSON array of orders to optimize and forward.",
+        "method": "POST",
+        "path": "/api/v1/optimization/provider-optimize-forward"
+    }
+
+
+@router.post(
+    "/riderassign",
+    status_code=status.HTTP_200_OK,
+    summary="Assign created orders to active riders",
+    description="""
+    Assigns orders to riders based on kitchen preferences, proximity, and load.
+    
+    - If a payload of orders is provided, processes those.
+    - If payload is empty, fetches all 'created' orders from the external API.
+    - Fetches active riders and matches them.
+    """,
+    responses={
+        200: {
+            "description": "Assignment Result",
+            "content": {
+                "application/json": {
+                    "example": {"code": 200, "details": {"1234": [{"orderid": "..."}]}, "message": "Success", "status": True}
+                }
+            }
+        }
+    }
+)
+async def assign_orders_to_riders(
+    request: Request,
+    body: list[dict] = None,
+    resuffle: bool = Query(False),
+    reshuffle: bool = Query(False),
+    rehuffle: bool = Query(False),
+    hypertuning_params: str = None
+):
+    """
+    Smart assignment of orders to riders.
+    """
+    from app.services.rider.get_active_riders import fetch_active_riders, fetch_created_orders, fetch_rider_pricing
+    from app.services.core.assignment_service import AssignmentService
+    from app.services.routing.route_optimizer import RouteOptimizer
+    from app.services.routing.realistic_eta_calculator import RealisticETACalculator
+    from datetime import datetime, timedelta
+    from dateutil.parser import parse as parse_date
+    import asyncio
+    
+    eta_calculator = RealisticETACalculator()
+    
+    try:
+        # Check if any variant is present in query params (flag-style) or explicitly true
+        q_params = request.query_params
+        do_reshuffle = any(k in q_params for k in ["reshuffle", "resuffle", "rehuffle"]) or \
+                       resuffle or reshuffle or rehuffle
+        
+        # 1. Fetch Riders and Pricing
+        riders_task = fetch_active_riders()
+        pricing_task = fetch_rider_pricing()
+        
+        riders, pricing = await asyncio.gather(riders_task, pricing_task)
+        
+        # Determine pricing (Default: 30 base + 2.5/km)
+        fuel_charge = 2.5
+        base_pay = 30.0
+        if pricing:
+            shift_1 = next((p for p in pricing if p.get("shiftid") == 1), None)
+            if shift_1:
+                fuel_charge = float(shift_1.get("fuelcharge", 2.5))
+                base_pay = float(shift_1.get("basepay") or shift_1.get("base_pay") or 30.0)
+        
+        # 2. Determine Orders Source
+        orders = body
+        if not orders:
+            logger.info("No payload provided, fetching created orders from external API.")
+            orders = await fetch_created_orders()
+        else:
+            logger.info(f"Processing {len(orders)} orders from payload.")
+
+        if not orders:
+            return {
+                "code": 200, 
+                "details": {}, 
+                "message": "No orders found to assign.", 
+                "status": True,
+                "meta": {
+                    "active_riders_count": len(riders)
+                }
+            }
+            
+        # 3. Run Assignment (AssignmentService)
+        # -- Per-request strategy override --
+        from app.config.dynamic_config import get_config
+        _cfg = get_config()
+        _original_strategy = None
+        
+        valid_strategies = ["balanced", "fuel_saver", "aggressive_speed", "zone_strict"]
+        if hypertuning_params and hypertuning_params in valid_strategies:
+            _original_strategy = _cfg.get("ml_strategy", "balanced")
+            _cfg._cache["ml_strategy"] = hypertuning_params
+            logger.info(f"[HYPERTUNE] Per-request strategy override: {hypertuning_params}")
+        
+        service = AssignmentService()
+        assignments, unassigned_orders = await service.assign_orders(
+            riders=riders, 
+            orders=orders,
+            fuel_charge=fuel_charge,
+            base_pay=base_pay,
+            reshuffle=do_reshuffle
+        )
+        
+        # Restore original strategy after this call
+        if _original_strategy is not None:
+            _cfg._cache["ml_strategy"] = _original_strategy
+        
+        if do_reshuffle:
+            logger.info("[RESHUFFLE] Retry mode active - exploring alternative rider assignments.")
+        
+        # 4. Optimize Routes for Each Rider and Flatten Response
+        optimizer = RouteOptimizer()
+        flat_orders_list = []
+
+        # Prepare tasks for parallel execution
+        # We need to store context (rider_id) to map results back
+        optimization_tasks = []
+        task_contexts = []
+
+        for rider_id, rider_orders in assignments.items():
+            if not rider_orders:
+                continue
+                
+            # Align with createdeliveries model: Always optimize from the Pickup/Kitchen location.
+            # This prevents route reversal if the rider is on the "far" side of the deliveries.
+            # The rider's current location (rlat/rlon) is ignored for sequence optimization
+            # to ensure the logical flow (Kitchen -> Stop 1 -> Stop 2 -> Stop 3) is followed.
+            start_coords = None
+
+            # Add to task list
+            optimization_tasks.append(
+                optimizer.optimize_provider_payload(rider_orders, start_coords=start_coords)
+            )
+            task_contexts.append(rider_id)
+
+        total_assigned = 0
+
+        # Execute all optimizations in parallel
+        # This dramatically reduces time from Sum(RiderTimes) to Max(RiderTime)
+        if optimization_tasks:
+            results = await asyncio.gather(*optimization_tasks)
+            
+            # Create a lookup for rider details
+            rider_info_map = {}
+            for r in riders:
+                # Use string conversion for robust ID matching
+                r_id = str(r.get("userid") or r.get("_id", ""))
+                if r_id:
+                    rider_info_map[r_id] = {
+                        "name": r.get("username", ""),
+                        "contactno": r.get("contactno", "")
+                    }
+
+            # Process results matching them back to riders
+            for stored_rider_id, optimized_route in zip(task_contexts, results):
+                r_id_str = str(stored_rider_id)
+                r_info = rider_info_map.get(r_id_str, {})
+                rider_name = r_info.get("name", "")
+                rider_contact = r_info.get("contactno", "")
+                
+                # Calculate total distance for this rider
+                total_rider_kms = 0
+                if optimized_route:
+                    # Usually the last order has the max cumulative kms if steps are 1..N
+                    try:
+                        total_rider_kms = max([float(o.get("cumulativekms", 0)) for o in optimized_route])
+                    except:
+                        total_rider_kms = sum([float(o.get("actualkms", o.get("kms", 0))) for o in optimized_route])
+
+                for order in optimized_route:
+                    order["userid"] = stored_rider_id
+                    order["username"] = rider_name
+                    # Populate the specific fields requested by the user
+                    order["rider"] = rider_name
+                    order["ridercontactno"] = rider_contact
+                    order["riderkms"] = str(round(total_rider_kms, 2))
+                    
+                    # --- DYNAMIC ETA COMPUTATION -----------------------------
+                    # Try various cases and names for pickup slot
+                    pickup_slot_str = (
+                        order.get("pickupSlot") or 
+                        order.get("pickupslot") or 
+                        order.get("pickup_slot") or
+                        order.get("pickuptime")
+                    )
+                    
+                    if pickup_slot_str:
+                        # Find the actual travel distance for THIS specific order
+                        # cumulativekms represents distance from pickup to this delivery stop
+                        dist_km = float(order.get("cumulativekms") or order.get("actualkms", order.get("kms", 0)))
+                        step = int(order.get("step", 1))
+                        order_type = order.get("ordertype", "Economy")
+                        
+                        try:
+                            # Robust date parsing (handles almost any format magically)
+                            pickup_time = parse_date(str(pickup_slot_str))
+                            
+                            eta_mins = eta_calculator.calculate_eta(
+                                distance_km=dist_km,
+                                is_first_order=(step == 1),
+                                order_type=order_type,
+                                time_of_day="normal"
+                            )
+                            expected_time = pickup_time + timedelta(minutes=eta_mins)
+                            
+                            # Format output as requested: "2026-03-24 08:25 AM"
+                            order["expectedDeliveryTime"] = expected_time.strftime("%Y-%m-%d %I:%M %p")
+                            order["transitMinutes"] = eta_mins
+                            order["calculationDistanceKm"] = round(dist_km, 2)
+                        except Exception as e:
+                            logger.warning(f"Could not calculate ETA from pickupSlot '{pickup_slot_str}': {e}")
+                    # ---------------------------------------------------------
+                    
+                    flat_orders_list.append(order)
+                total_assigned += len(optimized_route)
+        
+        # 5. Zone Processing
+        from app.services.routing.zone_service import ZoneService
+        zone_service = ZoneService()
+        zone_data = zone_service.group_by_zones(flat_orders_list, unassigned_orders, fuel_charge=fuel_charge, base_pay=base_pay)
+        
+        zones_structure = zone_data["detailed_zones"]
+        zone_analysis = zone_data["zone_analysis"]
+
+        return {
+            "code": 200,
+            "zone_summary": zone_analysis,   # High-level zone metrics
+            "zones": zones_structure,         # Detailed data
+            "details": flat_orders_list,      # Flat list
+            "message": "Success",
+            "status": True,
+            "meta": {
+                "total_orders": len(orders),
+                "utilized_riders": len([rid for rid, rl in assignments.items() if rl]),
+                "active_riders_pool": len(riders),
+                "assigned_orders": total_assigned,
+                "unassigned_orders": len(unassigned_orders),
+                "total_profit": round(sum(z["total_profit"] for z in zone_analysis), 2),
+                "fuel_charge_base": fuel_charge,
+                "unassigned_details": [
+                    {
+                        "orderid": o.get("orderid") or o.get("_id"),
+                        "reason": o.get("unassigned_reason", "Unknown capacity/proximity issue")
+                    } for o in unassigned_orders
+                ],
+                "distribution_summary": {rid: len(rl) for rid, rl in assignments.items() if rl},
+                "resuffle_mode": do_reshuffle,
+                "hypertuning_params": hypertuning_params or "default"
+            }
+        }
+
+    except Exception as e:
+        logger.error(f"Error in rider assignment: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Internal server error during assignment")
+
+    finally:
+        # -- Fire ML training trigger (non-blocking) -----------------------
+        # Runs AFTER response is ready. Every 10th call kicks off a
+        # background thread that retrains the model. API is never blocked.
+        try:
+            from app.main import trigger_training_if_due
+            trigger_training_if_due()
+        except Exception:
+            pass  # Never crash the endpoint due to ML trigger

app/services/__init__.py

@@ -0,0 +1,124 @@
+"""Services package."""
+
+from __future__ import annotations
+
+import json
+import os
+import logging
+from typing import Any, Optional, Dict
+
+try:
+    import redis  # type: ignore
+except Exception:  # pragma: no cover
+    redis = None  # type: ignore
+
+logger = logging.getLogger(__name__)
+
+
+class RedisCache:
+    """Lightweight Redis cache wrapper with graceful fallback."""
+
+    def __init__(self, url_env: str = "REDIS_URL", default_ttl_seconds: Optional[int] = None) -> None:
+        # Allow TTL to be configurable via env var (default 300s = 5 min, or 86400 = 24h)
+        ttl_env = os.getenv("REDIS_CACHE_TTL_SECONDS")
+        if default_ttl_seconds is None:
+            default_ttl_seconds = int(ttl_env) if ttl_env else 300
+        
+        self.default_ttl_seconds = default_ttl_seconds
+        self._enabled = False
+        self._client = None
+        self._stats = {"hits": 0, "misses": 0, "sets": 0}
+
+        url = os.getenv(url_env)
+        if not url or redis is None:
+            logger.warning("Redis not configured or client unavailable; caching disabled")
+            return
+        try:
+            self._client = redis.Redis.from_url(url, decode_responses=True)
+            self._client.ping()
+            self._enabled = True
+            logger.info(f"Redis cache connected (TTL: {self.default_ttl_seconds}s)")
+        except Exception as exc:
+            logger.warning(f"Redis connection failed: {exc}; caching disabled")
+            self._enabled = False
+            self._client = None
+
+    @property
+    def enabled(self) -> bool:
+        return self._enabled and self._client is not None
+
+    def get_json(self, key: str) -> Optional[Any]:
+        if not self.enabled:
+            self._stats["misses"] += 1
+            return None
+        try:
+            raw = self._client.get(key)  # type: ignore[union-attr]
+            if raw:
+                self._stats["hits"] += 1
+                return json.loads(raw)
+            else:
+                self._stats["misses"] += 1
+                return None
+        except Exception as exc:
+            logger.debug(f"Redis get_json error for key={key}: {exc}")
+            self._stats["misses"] += 1
+            return None
+
+    def set_json(self, key: str, value: Any, ttl_seconds: Optional[int] = None) -> None:
+        if not self.enabled:
+            return
+        try:
+            payload = json.dumps(value, default=lambda o: getattr(o, "model_dump", lambda: o)())
+            ttl = ttl_seconds if ttl_seconds is not None else self.default_ttl_seconds
+            # Use -1 for no expiration, otherwise use setex
+            if ttl > 0:
+                self._client.setex(key, ttl, payload)  # type: ignore[union-attr]
+            else:
+                self._client.set(key, payload)  # type: ignore[union-attr]
+            self._stats["sets"] += 1
+        except Exception as exc:
+            logger.debug(f"Redis set_json error for key={key}: {exc}")
+
+    def delete(self, pattern: str) -> int:
+        """Delete keys matching pattern (e.g., 'routes:*'). Returns count deleted."""
+        if not self.enabled:
+            return 0
+        try:
+            keys = list(self._client.scan_iter(match=pattern))  # type: ignore[union-attr]
+            if keys:
+                return self._client.delete(*keys)  # type: ignore[union-attr]
+            return 0
+        except Exception as exc:
+            logger.error(f"Redis delete error for pattern={pattern}: {exc}")
+            return 0
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Get cache statistics."""
+        stats = self._stats.copy()
+        if self.enabled:
+            try:
+                # Count cache keys
+                route_keys = list(self._client.scan_iter(match="routes:*"))  # type: ignore[union-attr]
+                stats["total_keys"] = len(route_keys)
+                stats["enabled"] = True
+            except Exception:
+                stats["total_keys"] = 0
+                stats["enabled"] = True
+        else:
+            stats["total_keys"] = 0
+            stats["enabled"] = False
+        return stats
+
+    def get_keys(self, pattern: str = "routes:*") -> list[str]:
+        """Get list of cache keys matching pattern."""
+        if not self.enabled:
+            return []
+        try:
+            return list(self._client.scan_iter(match=pattern))  # type: ignore[union-attr]
+        except Exception as exc:
+            logger.error(f"Redis get_keys error for pattern={pattern}: {exc}")
+            return []
+
+
+# Singleton cache instance for app
+cache = RedisCache()

app/services/core/assignment_service.py

@@ -0,0 +1,515 @@
+
+import logging
+import random
+import time
+from math import radians, cos, sin, asin, sqrt
+from typing import List, Dict, Any, Optional
+from collections import defaultdict
+from app.config.rider_preferences import RIDER_PREFERRED_KITCHENS
+from app.services.routing.kalman_filter import smooth_rider_locations, smooth_order_coordinates
+from app.config.dynamic_config import get_config
+from app.services.ml.ml_data_collector import get_collector
+
+logger = logging.getLogger(__name__)
+
+class AssignmentService:
+    def __init__(self):
+        self.rider_preferences = RIDER_PREFERRED_KITCHENS
+        self.earth_radius_km = 6371
+        self._cfg = get_config()
+
+    def _load_config(self):
+        """Load ML-tuned hyperparams fresh on every assignment call."""
+        cfg = self._cfg
+        self.MAX_PICKUP_DISTANCE_KM      = cfg.get("max_pickup_distance_km")
+        self.MAX_KITCHEN_DISTANCE_KM     = cfg.get("max_kitchen_distance_km")
+        self.MAX_ORDERS_PER_RIDER        = int(cfg.get("max_orders_per_rider"))
+        self.IDEAL_LOAD                  = int(cfg.get("ideal_load"))
+        self.WORKLOAD_BALANCE_THRESHOLD  = cfg.get("workload_balance_threshold")
+        self.WORKLOAD_PENALTY_WEIGHT     = cfg.get("workload_penalty_weight")
+        self.DISTANCE_PENALTY_WEIGHT     = cfg.get("distance_penalty_weight")
+        self.PREFERENCE_BONUS            = cfg.get("preference_bonus")
+        self.HOME_ZONE_BONUS_4KM         = cfg.get("home_zone_bonus_4km")
+        self.HOME_ZONE_BONUS_2KM         = cfg.get("home_zone_bonus_2km")
+        self.EMERGENCY_LOAD_PENALTY      = cfg.get("emergency_load_penalty")
+
+    def haversine(self, lat1, lon1, lat2, lon2):
+        """Calculate the great circle distance between two points."""
+        lon1, lat1, lon2, lat2 = map(radians, [float(lon1), float(lat1), float(lon2), float(lat2)])
+        dlon = lon2 - lon1 
+        dlat = lat2 - lat1 
+        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
+        c = 2 * asin(min(1.0, sqrt(a)))  # Clamp to 1.0 to avoid domain errors 
+        return c * self.earth_radius_km
+
+    def get_lat_lon(self, obj: Dict[str, Any], prefix: str = "") -> tuple[float, float]:
+        """Generic helper to extract lat/lon from diversely named keys."""
+        # Try specific prefixes first
+        candidates = [
+            (f"{prefix}lat", f"{prefix}lon"),
+            (f"{prefix}lat", f"{prefix}long"),
+            (f"{prefix}latitude", f"{prefix}longitude"),
+        ]
+        # Also try standard keys if prefix fails
+        candidates.extend([
+            ("lat", "lon"), ("latitude", "longitude"),
+            ("pickuplat", "pickuplon"), ("pickuplat", "pickuplong"),
+            ("deliverylat", "deliverylong"), ("droplat", "droplon")
+        ])
+
+        for lat_key, lon_key in candidates:
+            if lat_key in obj and lon_key in obj and obj[lat_key] and obj[lon_key]:
+                 try:
+                     return float(obj[lat_key]), float(obj[lon_key])
+                 except: pass
+                 
+        # Special case: nested 'pickup_location'
+        if "pickup_location" in obj:
+            return self.get_lat_lon(obj["pickup_location"])
+            
+        return 0.0, 0.0
+
+    def get_order_kitchen(self, order: Dict[str, Any]) -> str:
+        possible_keys = ['storename', 'restaurantname', 'kitchenname', 'partnername', 'store_name']
+        for key in possible_keys:
+            if key in order and order[key]:
+                return str(order[key]).strip()
+        return "Unknown"
+
+    def assign_orders(self, orders: List[Dict[str, Any]], riders: List[Dict[str, Any]], reshuffle: bool = False) -> tuple[Dict[int, List[Dict[str, Any]]], List[Dict[str, Any]]]:
+        """
+        ENHANCED: Cluster-Based Load-Balanced Assignment.
+        
+        Strategy:
+        1. Cluster orders by kitchen proximity
+        2. Calculate rider workload (current capacity usage)
+        3. Assign clusters to best-fit riders (proximity + workload balance)
+        4. Rebalance if needed
+        
+        If reshuffle=True, controlled randomness is injected into rider scoring
+        so that retrying the same input can explore alternative assignments.
+        """
+        from app.services.rider.rider_history_service import RiderHistoryService
+        from app.services.rider.rider_state_manager import RiderStateManager
+        from app.services.routing.clustering_service import ClusteringService
+
+        # -- Load ML-tuned hyperparameters (or defaults on first run) ------
+        self._load_config()
+        _call_start = time.time()
+
+        # 0. Prep
+        assignments: Dict[int, List[Dict[str, Any]]] = defaultdict(list)
+        unassigned_orders: List[Dict[str, Any]] = []
+        rider_states = {} # Track live load
+
+        # 0a. KALMAN FILTER - Smooth rider GPS locations before scoring
+        riders = smooth_rider_locations(list(riders))
+
+        # 0b. KALMAN FILTER - Smooth order delivery coordinates before clustering
+        orders = smooth_order_coordinates(list(orders))
+        
+        # 1. Parse and Filter Riders
+        valid_riders = []
+        BLOCKED_RIDERS = [1242, 1266, 1245, 1232, 1240, 1007] # Test/Blocked IDs
+
+        # Load Existing State (Persistence)
+        state_mgr = RiderStateManager()
+        
+        for r in riders:
+            # Robust ID Extraction
+            rid_raw = r.get("userid") or r.get("riderid") or r.get("id") or r.get("_id")
+            try:
+                rid = int(rid_raw)
+            except (ValueError, TypeError):
+                continue
+
+            if rid in BLOCKED_RIDERS: continue
+            
+            # Robust Status Check
+            # Keep if: onduty (1, "1", True) OR status is active/idle/online
+            is_onduty = str(r.get("onduty")) in ["1", "True"] or r.get("onduty") is True
+            is_active = r.get("status") in ["active", "idle", "online"]
+            
+            if not (is_onduty or is_active):
+                continue
+
+            # Location
+            lat, lon = self.get_lat_lon(r)
+            
+            # Fetch previous state to know if they are already busy
+            p_state = state_mgr.get_rider_state(rid)
+            
+            # If rider has valid GPS, use it. If not, fallback to Last Drop or Home.
+            if lat == 0 or lon == 0:
+                if p_state['last_drop_lat']:
+                    lat, lon = p_state['last_drop_lat'], p_state['last_drop_lon']
+                else:
+                    # Home Location Fallback
+                    from app.config.rider_preferences import RIDER_HOME_LOCATIONS
+                    lat, lon = RIDER_HOME_LOCATIONS.get(rid, (0.0, 0.0))
+
+            valid_riders.append({
+                "id": rid,
+                "lat": lat,
+                "lon": lon,
+                "obj": r
+            })
+            
+            # Initialize rider state with existing workload
+            existing_load = p_state.get('minutes_remaining', 0) / 15  # Convert minutes to order estimate
+            
+            rider_states[rid] = {
+                'lat': lat,
+                'lon': lon,
+                'kitchens': set(), 
+                'count': int(existing_load),  # Start with existing workload
+                'workload_score': existing_load  # For prioritization
+            }
+
+        if not valid_riders:
+            logger.warning("No riders passed on-duty filter. Retrying with all available riders as emergency rescue...")
+            # If no on-duty riders, we take ANY rider provided by the API to ensure assignment
+            for r in riders:
+                rid = int(r.get("userid", 0))
+                if rid in BLOCKED_RIDERS: continue
+                
+                lat, lon = self.get_lat_lon(r)
+                if lat == 0 or lon == 0:
+                    from app.config.rider_preferences import RIDER_HOME_LOCATIONS
+                    lat, lon = RIDER_HOME_LOCATIONS.get(rid, (0.0, 0.0))
+                
+                if lat != 0:
+                    valid_riders.append({"id": rid, "lat": lat, "lon": lon, "obj": r})
+                    rider_states[rid] = {
+                        'lat': lat, 'lon': lon, 'kitchens': set(), 
+                        'count': 0, 'workload_score': 0
+                    }
+
+        if not valid_riders:
+            logger.error("DANGER: Absolutely no riders available for assignment.")
+            # Mark all as unassigned
+            for o in orders:
+                o["unassigned_reason"] = "No riders found (check partner online status)."
+                unassigned_orders.append(o)
+            return assignments, unassigned_orders
+
+        logger.info(f"Found {len(valid_riders)} active riders")
+
+        # 2. CLUSTER ORDERS BY KITCHEN PROXIMITY
+        clustering_service = ClusteringService()
+        clusters = clustering_service.cluster_orders_by_kitchen(orders, max_cluster_radius_km=self.MAX_KITCHEN_DISTANCE_KM)  # radius from ML
+        
+        logger.info(f"Created {len(clusters)} order clusters")
+
+        # 3. ASSIGN CLUSTERS TO RIDERS (Load-Balanced)
+        for cluster_idx, cluster in enumerate(clusters):
+            centroid_lat, centroid_lon = cluster['centroid']
+            cluster_orders = cluster['orders']
+            cluster_size = len(cluster_orders)
+            
+            logger.info(f"Assigning cluster {cluster_idx+1}/{len(clusters)}: {cluster_size} orders at ({centroid_lat:.4f}, {centroid_lon:.4f})")
+            
+            # Find best riders for this cluster
+            candidate_riders = []
+            
+            for r in valid_riders:
+                rid = r["id"]
+                r_state = rider_states[rid]
+                
+                # Calculate distance to cluster centroid
+                dist = self.haversine(r_state['lat'], r_state['lon'], centroid_lat, centroid_lon)
+                
+                # Preference bonus & Distance Bypass
+                prefs = self.rider_preferences.get(rid, [])
+                has_preference = False
+                for k_name in cluster['kitchen_names']:
+                    if any(p.lower() in k_name.lower() or k_name.lower() in p.lower() for p in prefs):
+                        has_preference = True
+                        break
+                
+                # Dynamic Limit: 6km default, 10km for preferred kitchens
+                allowed_dist = self.MAX_PICKUP_DISTANCE_KM
+                if has_preference:
+                    allowed_dist = max(allowed_dist, 10.0) 
+
+                # Skip if too far
+                if dist > allowed_dist:
+                    continue
+                
+                # Calculate workload utilization (0.0 to 1.0)
+                utilization = r_state['count'] / self.MAX_ORDERS_PER_RIDER
+
+                # Calculate score (lower is better) - weights from DynamicConfig
+                workload_penalty  = utilization * self.WORKLOAD_PENALTY_WEIGHT
+                distance_penalty  = dist * self.DISTANCE_PENALTY_WEIGHT
+
+                # Preference bonus (ML-tuned)
+                preference_bonus = self.PREFERENCE_BONUS if has_preference else 0
+
+                # Home zone bonus (ML-tuned)
+                from app.config.rider_preferences import RIDER_HOME_LOCATIONS
+                h_lat, h_lon = RIDER_HOME_LOCATIONS.get(rid, (0.0, 0.0))
+                home_bonus = 0
+                if h_lat != 0:
+                    home_dist = self.haversine(h_lat, h_lon, centroid_lat, centroid_lon)
+                    if home_dist <= 4.0:
+                        home_bonus = self.HOME_ZONE_BONUS_4KM
+                    if home_dist <= 2.0:
+                        home_bonus = self.HOME_ZONE_BONUS_2KM
+
+                score = workload_penalty + distance_penalty + preference_bonus + home_bonus
+                
+                # RESHUFFLE: Add controlled noise so retries explore different riders
+                if reshuffle:
+                    noise = random.uniform(-15.0, 15.0)
+                    score += noise
+                
+                candidate_riders.append({
+                    'id': rid,
+                    'score': score,
+                    'distance': dist,
+                    'utilization': utilization,
+                    'current_load': r_state['count']
+                })
+            
+            if not candidate_riders:
+                logger.warning(f"No riders available for cluster {cluster_idx+1}")
+                for o in cluster_orders:
+                    o["unassigned_reason"] = f"No riders within {self.MAX_PICKUP_DISTANCE_KM}km radius of kitchen."
+                    unassigned_orders.append(o)
+                continue
+            
+            # Sort by score (best first)
+            candidate_riders.sort(key=lambda x: x['score'])
+            
+            # SMART DISTRIBUTION: Split cluster if needed
+            remaining_orders = cluster_orders[:]
+            
+            while remaining_orders and candidate_riders:
+                best_rider = candidate_riders[0]
+                rid = best_rider['id']
+                r_state = rider_states[rid]
+                
+                # How many orders can this rider take?
+                available_capacity = self.MAX_ORDERS_PER_RIDER - r_state['count']
+                
+                if available_capacity <= 0:
+                    # Rider is full, remove from candidates
+                    candidate_riders.pop(0)
+                    continue
+                
+                # Decide batch size
+                # If rider is underutilized and cluster is small, give all
+                # If rider is busy or cluster is large, split it
+                if best_rider['utilization'] < self.WORKLOAD_BALANCE_THRESHOLD:
+                    # Rider has capacity, can take more
+                    batch_size = min(available_capacity, len(remaining_orders))
+                else:
+                    # Rider is getting busy, be conservative (IDEAL_LOAD from ML)
+                    batch_size = min(self.IDEAL_LOAD - r_state['count'], len(remaining_orders), available_capacity)
+                    batch_size = max(1, batch_size)  # At least 1 order
+                
+                # Assign batch
+                batch = remaining_orders[:batch_size]
+                remaining_orders = remaining_orders[batch_size:]
+                
+                assignments[rid].extend(batch)
+                
+                # Update rider state
+                r_state['count'] += len(batch)
+                r_state['lat'] = centroid_lat
+                r_state['lon'] = centroid_lon
+                r_state['kitchens'].update(cluster['kitchen_names'])
+                r_state['workload_score'] = r_state['count'] / self.MAX_ORDERS_PER_RIDER
+                
+                logger.info(f"  -> Assigned {len(batch)} orders to Rider {rid} (load: {r_state['count']}/{self.MAX_ORDERS_PER_RIDER})")
+                
+                # Re-sort candidates by updated scores
+                for candidate in candidate_riders:
+                    if candidate['id'] == rid:
+                        candidate['utilization'] = r_state['count'] / self.MAX_ORDERS_PER_RIDER
+                        candidate['current_load'] = r_state['count']
+                        # Recalculate score
+                        workload_penalty = candidate['utilization'] * 100
+                        distance_penalty = candidate['distance'] * 2
+                        candidate['score'] = workload_penalty + distance_penalty
+                
+                candidate_riders.sort(key=lambda x: x['score'])
+
+            # If any orders left in the cluster after exhaustion of candidates
+            if remaining_orders:
+                # Instead of giving up, keep them in a pool for mandatory assignment
+                unassigned_orders.extend(remaining_orders)
+
+        # 4. EMERGENCY MANDATORY ASSIGNMENT (Ensures 0 unassigned if riders exist)
+        if unassigned_orders and valid_riders:
+            logger.info(f"[ALERT] Starting Emergency Mandatory Assignment for {len(unassigned_orders)} orders...")
+            force_pool = unassigned_orders[:]
+            unassigned_orders.clear()
+            
+            for o in force_pool:
+                # Determine pickup location
+                o_lat, o_lon = self.get_lat_lon(o, prefix="pickup")
+                if o_lat == 0:
+                    o["unassigned_reason"] = "Could not geolocate order (0,0)."
+                    unassigned_orders.append(o)
+                    continue
+
+                # Find the 'least bad' rider (Closest + Balanced Load)
+                best_emergency_rider = None
+                best_emergency_score = float('inf')
+
+                for r in valid_riders:
+                    rid = r["id"]
+                    r_state = rider_states[rid]
+                    
+                    dist = self.haversine(r_state['lat'], r_state['lon'], o_lat, o_lon)
+                    # For emergency: Distance is important, but load prevents one rider taking EVERYTHING
+                    # Score = distance + ML-tuned penalty per existing order
+                    e_score = dist + (r_state['count'] * self.EMERGENCY_LOAD_PENALTY)
+
+                    if e_score < best_emergency_score:
+                        best_emergency_score = e_score
+                        best_emergency_rider = rid
+                
+                if best_emergency_rider:
+                    assignments[best_emergency_rider].append(o)
+                    rider_states[best_emergency_rider]['count'] += 1
+                    logger.info(f"   Force-Assigned order {o.get('orderid')} to Rider {best_emergency_rider} (Score: {best_emergency_score:.2f})")
+                else:
+                    unassigned_orders.append(o)
+
+        # 5. FINAL REBALANCING (Optional)
+        # Check if any rider is overloaded while others are idle
+        self._rebalance_workload(assignments, rider_states, valid_riders)
+
+        # 6. Commit State and History
+        self._post_process(assignments, rider_states)
+
+        # 7. -- ML DATA COLLECTION -----------------------------------------
+        try:
+            elapsed_ms = (time.time() - _call_start) * 1000
+            get_collector().log_assignment_event(
+                num_orders=len(orders),
+                num_riders=len(riders),
+                hyperparams=self._cfg.get_all(),
+                assignments=assignments,
+                unassigned_count=len(unassigned_orders),
+                elapsed_ms=elapsed_ms,
+            )
+        except Exception as _ml_err:
+            logger.debug(f"ML logging skipped: {_ml_err}")
+
+        # Log final distribution
+        logger.info("=" * 50)
+        logger.info("FINAL ASSIGNMENT DISTRIBUTION:")
+        for rid, orders in sorted(assignments.items()):
+            logger.info(f"  Rider {rid}: {len(orders)} orders")
+
+        if unassigned_orders:
+            logger.warning(f"  [ALERT] STILL UNASSIGNED: {len(unassigned_orders)} (Reason: No riders online or invalid coords)")
+        else:
+            logger.info("  [OK] ALL ORDERS ASSIGNED SUCCESSFULLY")
+        logger.info("=" * 50)
+
+        return assignments, unassigned_orders
+
+    def _rebalance_workload(self, assignments: Dict[int, List], rider_states: Dict, valid_riders: List):
+        """
+        Rebalance if workload is heavily skewed.
+        Move orders from overloaded riders to idle ones if possible.
+        """
+        if not assignments:
+            return
+        
+        # Calculate average load
+        total_orders = sum(len(orders) for orders in assignments.values())
+        avg_load = total_orders / len(valid_riders) if valid_riders else 0
+        
+        # Find overloaded and underutilized riders
+        overloaded = []
+        underutilized = []
+        
+        for r in valid_riders:
+            rid = r['id']
+            load = rider_states[rid]['count']
+            
+            if load > avg_load * 1.5 and load > self.IDEAL_LOAD:  # 50% above average
+                overloaded.append(rid)
+            elif load < avg_load * 0.5:  # 50% below average
+                underutilized.append(rid)
+        
+        if not overloaded or not underutilized:
+            return
+        
+        logger.info(f"Rebalancing: {len(overloaded)} overloaded, {len(underutilized)} underutilized riders")
+        
+        # Try to move orders from overloaded to underutilized
+        for over_rid in overloaded:
+            over_orders = assignments[over_rid]
+            over_state = rider_states[over_rid]
+            
+            # Try to offload some orders
+            for under_rid in underutilized:
+                under_state = rider_states[under_rid]
+                under_capacity = self.MAX_ORDERS_PER_RIDER - under_state['count']
+                
+                if under_capacity <= 0:
+                    continue
+                
+                # Find orders that are closer to underutilized rider
+                transferable = []
+                for order in over_orders:
+                    o_lat, o_lon = self.get_lat_lon(order, prefix="pickup")
+                    if o_lat == 0:
+                        continue
+                    
+                    dist_to_under = self.haversine(under_state['lat'], under_state['lon'], o_lat, o_lon)
+                    dist_to_over = self.haversine(over_state['lat'], over_state['lon'], o_lat, o_lon)
+                    
+                    # Transfer if underutilized rider is closer or similar distance
+                    if dist_to_under <= self.MAX_PICKUP_DISTANCE_KM and dist_to_under <= dist_to_over * 1.2:
+                        transferable.append(order)
+                
+                if transferable:
+                    # Transfer up to capacity
+                    transfer_count = min(len(transferable), under_capacity, over_state['count'] - self.IDEAL_LOAD)
+                    transfer_batch = transferable[:transfer_count]
+                    
+                    # Move orders
+                    for order in transfer_batch:
+                        over_orders.remove(order)
+                        assignments[under_rid].append(order)
+                    
+                    # Update states
+                    over_state['count'] -= len(transfer_batch)
+                    under_state['count'] += len(transfer_batch)
+                    
+                    logger.info(f"  Rebalanced: {len(transfer_batch)} orders from Rider {over_rid} -> {under_rid}")
+
+    def _post_process(self, assignments, rider_states):
+        """Update History and Persistence."""
+        from app.services.rider.rider_history_service import RiderHistoryService
+        from app.services.rider.rider_state_manager import RiderStateManager
+        
+        history_service = RiderHistoryService()
+        state_mgr = RiderStateManager()
+        
+        import time
+        ts = time.time()
+        
+        for rid, orders in assignments.items():
+            if not orders: continue
+            
+            history_service.update_rider_stats(rid, 5.0, len(orders)) 
+            
+            st = rider_states[rid]
+            state_mgr.states[rid] = {
+                'minutes_remaining': len(orders) * 15, 
+                'last_drop_lat': st['lat'],
+                'last_drop_lon': st['lon'],
+                'active_kitchens': st['kitchens'],
+                'last_updated_ts': ts
+            }
+
+        state_mgr._save_states()

app/services/ml/behavior_analyzer.py

@@ -0,0 +1,311 @@
+"""
+Behavior Analyzer - Production Grade
+======================================
+Analyzes historical assignment data using the ID3 decision tree to classify
+assignment outcomes as 'SUCCESS' or 'RISK'.
+
+Key fixes and upgrades over the original
+------------------------------------------
+1. BUG FIX: distance_band now uses `total_distance_km` (not `num_orders`).
+2. BUG FIX: time_band input is always normalized to uppercase before predict.
+3. Rich feature set: distance_band, time_band, load_band, order_density_band.
+4. Returns (label, confidence) from the classifier - exposes uncertainty.
+5. Trend analysis: tracks rolling success rate over recent N windows.
+6. Tree persistence: saves/loads trained tree as JSON to survive restarts.
+7. Feature importance proxy: logs which features drove the split.
+8. Thread-safe lazy training via a simple lock.
+"""
+
+import json
+import logging
+import os
+import sqlite3
+import threading
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Tuple
+
+from app.services.ml.id3_classifier import ID3Classifier, get_behavior_model
+
+logger = logging.getLogger(__name__)
+
+_DB_PATH   = os.getenv("ML_DB_PATH",   "ml_data/ml_store.db")
+_TREE_PATH = os.getenv("ML_TREE_PATH", "ml_data/behavior_tree.json")
+
+
+# ---------------------------------------------------------------------------
+# Band encoders (discrete labels for ID3)
+# ---------------------------------------------------------------------------
+
+def distance_band(km: float) -> str:
+    """Total route distance -> discrete band."""
+    if km <= 5.0:   return "SHORT"
+    if km <= 15.0:  return "MID"
+    if km <= 30.0:  return "LONG"
+    return "VERY_LONG"
+
+
+def time_band(ts_str: str) -> str:
+    """ISO timestamp -> time-of-day band."""
+    try:
+        hour = datetime.fromisoformat(ts_str).hour
+        if 6  <= hour < 10: return "MORNING_RUSH"
+        if 10 <= hour < 12: return "LATE_MORNING"
+        if 12 <= hour < 14: return "LUNCH_RUSH"
+        if 14 <= hour < 17: return "AFTERNOON"
+        if 17 <= hour < 20: return "EVENING_RUSH"
+        if 20 <= hour < 23: return "NIGHT"
+        return "LATE_NIGHT"
+    except Exception:
+        return "UNKNOWN"
+
+
+def load_band(avg_load: float) -> str:
+    """Average orders-per-rider -> load band."""
+    if avg_load <= 2.0:  return "LIGHT"
+    if avg_load <= 5.0:  return "MODERATE"
+    if avg_load <= 8.0:  return "HEAVY"
+    return "OVERLOADED"
+
+
+def order_density_band(num_orders: int, num_riders: int) -> str:
+    """Orders per available rider -> density band."""
+    if num_riders == 0:
+        return "NO_RIDERS"
+    ratio = num_orders / num_riders
+    if ratio <= 2.0:  return "SPARSE"
+    if ratio <= 5.0:  return "NORMAL"
+    if ratio <= 9.0:  return "DENSE"
+    return "OVERLOADED"
+
+
+# ---------------------------------------------------------------------------
+# Behavior Analyzer
+# ---------------------------------------------------------------------------
+
+class BehaviorAnalyzer:
+    """
+    Trains an ID3 tree on historical assignment logs and predicts whether
+    a new assignment context is likely to SUCCEED or be at RISK.
+
+    Features used
+    -------------
+    - distance_band      : total route distance bucket
+    - time_band          : time-of-day bucket
+    - load_band          : average load per rider bucket
+    - order_density_band : orders-per-rider ratio bucket
+
+    Target
+    ------
+    - is_success: "SUCCESS" if unassigned_count == 0, else "RISK"
+    """
+
+    TARGET = "is_success"
+    FEATURES = ["distance_band", "time_band", "load_band", "order_density_band"]
+
+    def __init__(self):
+        self._db_path   = _DB_PATH
+        self._tree_path = _TREE_PATH
+        self.model: ID3Classifier = get_behavior_model(max_depth=5)
+        self.is_trained: bool = False
+        self._lock = threading.Lock()
+        self._training_size: int = 0
+        self._success_rate: float = 0.0
+        self._rules: List[str] = []
+        self._recent_trend: List[float] = []
+
+        self._load_tree()
+
+    # ------------------------------------------------------------------
+    # Training
+    # ------------------------------------------------------------------
+
+    def train_on_history(self, limit: int = 2000) -> Dict[str, Any]:
+        """Fetch the most recent rows from SQLite and rebuild the tree."""
+        with self._lock:
+            try:
+                rows = self._fetch_rows(limit)
+                if len(rows) < 10:
+                    logger.warning(f"ID3 BehaviorAnalyzer: only {len(rows)} rows - need >=10.")
+                    return {"status": "insufficient_data", "rows": len(rows)}
+
+                training_data, successes = self._preprocess(rows)
+
+                if not training_data:
+                    return {"status": "preprocess_failed", "rows": len(rows)}
+
+                self.model.train(
+                    data=training_data,
+                    target=self.TARGET,
+                    features=self.FEATURES,
+                )
+                self.is_trained = True
+                self._training_size = len(training_data)
+                self._success_rate = successes / len(training_data)
+                self._rules = self.model.get_tree_rules()
+                self._compute_trend(rows)
+                self._save_tree()
+
+                summary = {
+                    "status": "ok",
+                    "training_rows": self._training_size,
+                    "success_rate": round(self._success_rate, 4),
+                    "n_rules": len(self._rules),
+                    "classes": self.model.classes,
+                    "feature_values": self.model.feature_values,
+                }
+                logger.info(
+                    f"ID3 BehaviorAnalyzer trained - rows={self._training_size}, "
+                    f"success_rate={self._success_rate:.1%}, rules={len(self._rules)}"
+                )
+                return summary
+
+            except Exception as e:
+                logger.error(f"ID3 BehaviorAnalyzer training failed: {e}", exc_info=True)
+                return {"status": "error", "message": str(e)}
+
+    # ------------------------------------------------------------------
+    # Prediction
+    # ------------------------------------------------------------------
+
+    def predict(self, distance_km: float, timestamp_or_band: str,
+                avg_load: float = 4.0, num_orders: int = 5,
+                num_riders: int = 2) -> Dict[str, Any]:
+        """Predict whether an assignment context will SUCCEED or be at RISK."""
+        if not self.is_trained:
+            return {
+                "label": "SUCCESS",
+                "confidence": 0.5,
+                "features_used": {},
+                "model_trained": False,
+            }
+
+        KNOWN_BANDS = {
+            "MORNING_RUSH", "LATE_MORNING", "LUNCH_RUSH",
+            "AFTERNOON", "EVENING_RUSH", "NIGHT", "LATE_NIGHT", "UNKNOWN"
+        }
+        t_band = (
+            timestamp_or_band.upper()
+            if timestamp_or_band.upper() in KNOWN_BANDS
+            else time_band(timestamp_or_band)
+        )
+
+        features_used = {
+            "distance_band":      distance_band(distance_km),
+            "time_band":          t_band,
+            "load_band":          load_band(avg_load),
+            "order_density_band": order_density_band(num_orders, num_riders),
+        }
+
+        label, confidence = self.model.predict(features_used)
+        return {
+            "label":         label,
+            "confidence":    round(confidence, 4),
+            "features_used": features_used,
+            "model_trained": True,
+        }
+
+    # ------------------------------------------------------------------
+    # Info / Diagnostics
+    # ------------------------------------------------------------------
+
+    def get_info(self) -> Dict[str, Any]:
+        return {
+            "is_trained":     self.is_trained,
+            "training_rows":  self._training_size,
+            "success_rate":   round(self._success_rate, 4),
+            "n_rules":        len(self._rules),
+            "rules":          self._rules[:20],
+            "recent_trend":   self._recent_trend,
+            "feature_names":  self.FEATURES,
+            "feature_values": self.model.feature_values if self.is_trained else {},
+            "classes":        self.model.classes if self.is_trained else [],
+        }
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _fetch_rows(self, limit: int) -> List[Dict]:
+        conn = sqlite3.connect(self._db_path)
+        conn.row_factory = sqlite3.Row
+        rows = conn.execute(
+            "SELECT * FROM assignment_ml_log ORDER BY id DESC LIMIT ?", (limit,)
+        ).fetchall()
+        conn.close()
+        return [dict(r) for r in rows]
+
+    def _preprocess(self, rows: List[Dict]) -> Tuple[List[Dict], int]:
+        training_data: List[Dict] = []
+        successes = 0
+        for r in rows:
+            try:
+                dist_km     = float(r.get("total_distance_km") or 0.0)
+                ts          = str(r.get("timestamp") or "")
+                avg_ld      = float(r.get("avg_load") or 0.0)
+                n_orders    = int(r.get("num_orders") or 0)
+                n_riders    = int(r.get("num_riders") or 1)
+                unassigned  = int(r.get("unassigned_count") or 0)
+
+                label = "SUCCESS" if unassigned == 0 else "RISK"
+                if label == "SUCCESS":
+                    successes += 1
+
+                training_data.append({
+                    "distance_band":      distance_band(dist_km),
+                    "time_band":          time_band(ts),
+                    "load_band":          load_band(avg_ld),
+                    "order_density_band": order_density_band(n_orders, n_riders),
+                    self.TARGET:          label,
+                })
+            except Exception:
+                continue
+        return training_data, successes
+
+    def _compute_trend(self, rows: List[Dict], window: int = 50) -> None:
+        trend = []
+        for i in range(0, len(rows), window):
+            chunk = rows[i:i + window]
+            if not chunk:
+                break
+            rate = sum(1 for r in chunk if int(r.get("unassigned_count", 1)) == 0) / len(chunk)
+            trend.append(round(rate, 4))
+        self._recent_trend = trend[-20:]
+
+    def _save_tree(self) -> None:
+        try:
+            os.makedirs(os.path.dirname(self._tree_path) or ".", exist_ok=True)
+            with open(self._tree_path, "w") as f:
+                f.write(self.model.to_json())
+            logger.info(f"ID3 tree persisted -> {self._tree_path}")
+        except Exception as e:
+            logger.warning(f"ID3 tree save failed: {e}")
+
+    def _load_tree(self) -> None:
+        try:
+            if not os.path.exists(self._tree_path):
+                return
+            with open(self._tree_path) as f:
+                self.model = ID3Classifier.from_json(f.read())
+            self.is_trained = True
+            self._rules = self.model.get_tree_rules()
+            logger.info(f"ID3 tree restored - rules={len(self._rules)}")
+        except Exception as e:
+            logger.warning(f"ID3 tree load failed (will retrain): {e}")
+
+
+# ---------------------------------------------------------------------------
+# Module-level singleton
+# ---------------------------------------------------------------------------
+
+_analyzer: Optional[BehaviorAnalyzer] = None
+_analyzer_lock = threading.Lock()
+
+
+def get_analyzer() -> BehaviorAnalyzer:
+    global _analyzer
+    with _analyzer_lock:
+        if _analyzer is None:
+            _analyzer = BehaviorAnalyzer()
+        if not _analyzer.is_trained:
+            _analyzer.train_on_history()
+        return _analyzer

app/services/ml/id3_classifier.py

@@ -0,0 +1,400 @@
+"""
+ID3 Classifier - Production Grade
+
+Improvements over v1:
+- Chi-squared pruning to prevent overfitting on sparse branches
+- Confidence scores on every prediction (Laplace smoothed)
+- Gain-ratio variant for high-cardinality features
+- Serialization (to_dict / from_dict / to_json / from_json)
+- Per-feature importance scores
+- Full prediction audit trail via explain()
+- min_samples_split and min_info_gain stopping criteria
+"""
+
+import math
+import json
+import logging
+from collections import Counter
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+class ID3Classifier:
+    """
+    ID3 decision tree (entropy / information-gain splitting).
+    All predict* methods work even if the model has never been trained -
+    they return safe defaults rather than raising.
+    """
+
+    def __init__(
+        self,
+        max_depth: int = 6,
+        min_samples_split: int = 5,
+        min_info_gain: float = 0.001,
+        use_gain_ratio: bool = False,
+        chi2_pruning: bool = True,
+    ):
+        self.max_depth = max_depth
+        self.min_samples_split = min_samples_split
+        self.min_info_gain = min_info_gain
+        self.use_gain_ratio = use_gain_ratio
+        self.chi2_pruning = chi2_pruning
+
+        self.tree: Any = None
+        self.features: List[str] = []
+        self.target: str = ""
+        self.classes_: List[str] = []
+        self.feature_importances_: Dict[str, float] = {}
+        self.feature_values: Dict[str, List[str]] = {}   # unique values seen per feature
+        self._n_samples: int = 0
+        self._total_gain: Dict[str, float] = {}
+
+    # ------------------------------------------------------------------ train
+
+    def train(self, data: List[Dict[str, Any]], target: str, features: List[str]) -> None:
+        if not data:
+            logger.warning("ID3: train() called with empty data.")
+            return
+
+        self.target = target
+        self.features = list(features)
+        self.classes_ = sorted({str(row.get(target)) for row in data})
+        self._total_gain = {f: 0.0 for f in features}
+        self._n_samples = len(data)
+
+        # Collect unique values per feature for dashboard display
+        self.feature_values = {
+            f: sorted({str(row.get(f)) for row in data if row.get(f) is not None})
+            for f in features
+        }
+
+        self.tree = self._build_tree(data, list(features), target, depth=0)
+
+        if self.chi2_pruning:
+            self.tree = self._prune(self.tree, data, target)
+
+        total_gain = sum(self._total_gain.values()) or 1.0
+        self.feature_importances_ = {
+            f: round(v / total_gain, 4) for f, v in self._total_gain.items()
+        }
+        logger.info(
+            f"ID3: trained on {len(data)} samples | "
+            f"classes={self.classes_} | importances={self.feature_importances_}"
+        )
+
+    # ----------------------------------------------------------- predict API
+
+    def predict(self, sample: Dict[str, Any]) -> Tuple[str, float]:
+        """Return (label, confidence 0-1). Safe to call before training."""
+        if self.tree is None:
+            return "Unknown", 0.0
+        label, proba = self._classify(self.tree, sample, [])
+        confidence = proba.get(str(label), 0.0) if isinstance(proba, dict) else 1.0
+        return str(label), round(confidence, 4)
+
+    def predict_proba(self, sample: Dict[str, Any]) -> Dict[str, float]:
+        """Full class probability distribution."""
+        if self.tree is None:
+            return {}
+        _, proba = self._classify(self.tree, sample, [])
+        return proba if isinstance(proba, dict) else {str(proba): 1.0}
+
+    def explain(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        """Human-readable decision path for audit / dashboard display."""
+        if self.tree is None:
+            return {"prediction": "Unknown", "confidence": 0.0, "decision_path": []}
+        path: List[str] = []
+        label, proba = self._classify(self.tree, sample, path)
+        return {
+            "prediction": str(label),
+            "confidence": round(proba.get(str(label), 1.0), 4),
+            "probabilities": proba,
+            "decision_path": path,
+        }
+
+    # ---------------------------------------------------------- serialisation
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "tree": self.tree,
+            "features": self.features,
+            "target": self.target,
+            "classes": self.classes_,
+            "feature_importances": self.feature_importances_,
+            "feature_values": self.feature_values,
+            "n_samples": self._n_samples,
+            "params": {
+                "max_depth": self.max_depth,
+                "min_samples_split": self.min_samples_split,
+                "min_info_gain": self.min_info_gain,
+                "use_gain_ratio": self.use_gain_ratio,
+                "chi2_pruning": self.chi2_pruning,
+            },
+        }
+
+    @classmethod
+    def from_dict(cls, d: Dict[str, Any]) -> "ID3Classifier":
+        p = d.get("params", {})
+        obj = cls(
+            max_depth=p.get("max_depth", 6),
+            min_samples_split=p.get("min_samples_split", 5),
+            min_info_gain=p.get("min_info_gain", 0.001),
+            use_gain_ratio=p.get("use_gain_ratio", False),
+            chi2_pruning=p.get("chi2_pruning", True),
+        )
+        obj.tree = d["tree"]
+        obj.features = d["features"]
+        obj.target = d["target"]
+        obj.classes_ = d["classes"]
+        obj.feature_importances_ = d.get("feature_importances", {})
+        obj.feature_values = d.get("feature_values", {})
+        obj._n_samples = d.get("n_samples", 0)
+        return obj
+
+    def to_json(self) -> str:
+        return json.dumps(self.to_dict(), indent=2)
+
+    @classmethod
+    def from_json(cls, s: str) -> "ID3Classifier":
+        return cls.from_dict(json.loads(s))
+
+    def summary(self) -> Dict[str, Any]:
+        return {
+            "n_samples": self._n_samples,
+            "n_classes": len(self.classes_),
+            "classes": self.classes_,
+            "n_features": len(self.features),
+            "feature_importances": self.feature_importances_,
+            "feature_values": self.feature_values,
+            "trained": self.tree is not None,
+        }
+
+    @property
+    def classes(self) -> List[str]:
+        """Alias for classes_ for compatibility."""
+        return self.classes_
+
+    def get_tree_rules(self) -> List[str]:
+        """Extract human-readable if/then rules from the trained tree."""
+        rules: List[str] = []
+        if self.tree is None:
+            return rules
+        self._extract_rules(self.tree, [], rules)
+        return rules
+
+    def _extract_rules(self, node: Any, conditions: List[str], rules: List[str]) -> None:
+        """Recursively walk the tree and collect decision paths as strings."""
+        if not isinstance(node, dict):
+            return
+        if node.get("__leaf__"):
+            label = node.get("__label__", "?")
+            proba = node.get("__proba__", {})
+            conf = proba.get(str(label), 0.0)
+            prefix = " AND ".join(conditions) if conditions else "(root)"
+            rules.append(f"{prefix} => {label} ({conf:.0%})")
+            return
+        feature = node.get("__feature__", "?")
+        for val, child in node.get("__branches__", {}).items():
+            self._extract_rules(child, conditions + [f"{feature}={val}"], rules)
+
+    # --------------------------------------------------------- tree building
+
+    def _build_tree(
+        self,
+        data: List[Dict[str, Any]],
+        features: List[str],
+        target: str,
+        depth: int,
+    ) -> Any:
+        counts = Counter(str(row.get(target)) for row in data)
+
+        # Pure node
+        if len(counts) == 1:
+            return self._make_leaf(data, target)
+
+        # Stopping criteria
+        if not features or depth >= self.max_depth or len(data) < self.min_samples_split:
+            return self._make_leaf(data, target)
+
+        best_f, best_gain = self._best_split(data, features, target)
+        if best_f is None or best_gain < self.min_info_gain:
+            return self._make_leaf(data, target)
+
+        self._total_gain[best_f] = self._total_gain.get(best_f, 0.0) + best_gain
+
+        remaining = [f for f in features if f != best_f]
+        node = {
+            "__feature__": best_f,
+            "__gain__": round(best_gain, 6),
+            "__n__": len(data),
+            "__branches__": {},
+        }
+        for val in {row.get(best_f) for row in data}:
+            subset = [r for r in data if r.get(best_f) == val]
+            node["__branches__"][str(val)] = self._build_tree(
+                subset, remaining, target, depth + 1
+            )
+        return node
+
+    def _make_leaf(self, data: List[Dict[str, Any]], target: str) -> Dict[str, Any]:
+        counts = Counter(str(row.get(target)) for row in data)
+        total = len(data)
+        k = len(self.classes_) or 1
+        # Laplace smoothing
+        proba = {
+            cls: round((counts.get(cls, 0) + 1) / (total + k), 4)
+            for cls in self.classes_
+        }
+        label = max(proba, key=proba.get)
+        return {"__leaf__": True, "__label__": label, "__proba__": proba, "__n__": total}
+
+    # ---------------------------------------------------------- splitting
+
+    def _best_split(
+        self, data: List[Dict[str, Any]], features: List[str], target: str
+    ) -> Tuple[Optional[str], float]:
+        base_e = self._entropy(data, target)
+        best_f, best_gain = None, -1.0
+        for f in features:
+            gain = self._info_gain(data, f, target, base_e)
+            if self.use_gain_ratio:
+                si = self._split_info(data, f)
+                gain = gain / si if si > 0 else 0.0
+            if gain > best_gain:
+                best_gain = gain
+                best_f = f
+        return best_f, best_gain
+
+    # ----------------------------------------------------------- pruning
+
+    def _prune(self, node: Any, data: List[Dict[str, Any]], target: str) -> Any:
+        if not isinstance(node, dict) or node.get("__leaf__"):
+            return node
+        feature = node["__feature__"]
+        # Recurse children first
+        for val in list(node["__branches__"].keys()):
+            subset = [r for r in data if str(r.get(feature)) == str(val)]
+            node["__branches__"][val] = self._prune(node["__branches__"][val], subset, target)
+        # Chi-squared test: if split is not significant, collapse to leaf
+        if not self._chi2_significant(data, feature, target):
+            return self._make_leaf(data, target)
+        return node
+
+    def _chi2_significant(
+        self, data: List[Dict[str, Any]], feature: str, target: str
+    ) -> bool:
+        classes = self.classes_
+        feature_vals = list({str(r.get(feature)) for r in data})
+        if not classes or len(feature_vals) < 2:
+            return False
+        total = len(data)
+        class_totals = Counter(str(r.get(target)) for r in data)
+        chi2 = 0.0
+        for val in feature_vals:
+            subset = [r for r in data if str(r.get(feature)) == val]
+            n_val = len(subset)
+            val_counts = Counter(str(r.get(target)) for r in subset)
+            for cls in classes:
+                observed = val_counts.get(cls, 0)
+                expected = (n_val * class_totals.get(cls, 0)) / total
+                if expected > 0:
+                    chi2 += (observed - expected) ** 2 / expected
+        df = (len(feature_vals) - 1) * (len(classes) - 1)
+        if df <= 0:
+            return False
+        # Critical values at p=0.05
+        crit_table = {1: 3.841, 2: 5.991, 3: 7.815, 4: 9.488, 5: 11.070, 6: 12.592}
+        crit = crit_table.get(df, 3.841 * df)
+        return chi2 > crit
+
+    # ---------------------------------------------------------- classify
+
+    def _classify(
+        self, node: Any, row: Dict[str, Any], path: List[str]
+    ) -> Tuple[Any, Any]:
+        if not isinstance(node, dict):
+            return node, {str(node): 1.0}
+        if node.get("__leaf__"):
+            label = node["__label__"]
+            proba = node["__proba__"]
+            path.append(f"predict={label} (p={proba.get(label, 0):.2f})")
+            return label, proba
+
+        feature = node["__feature__"]
+        value = str(row.get(feature, ""))
+        path.append(f"{feature}={value}")
+
+        branches = node["__branches__"]
+        if value in branches:
+            return self._classify(branches[value], row, path)
+
+        # Unseen value: weighted vote from all leaf children
+        all_proba: Counter = Counter()
+        total_n = 0
+        for child in branches.values():
+            if isinstance(child, dict) and child.get("__leaf__"):
+                n = child.get("__n__", 1)
+                total_n += n
+                for cls, p in child.get("__proba__", {}).items():
+                    all_proba[cls] += p * n
+
+        if not total_n:
+            fallback = self.classes_[0] if self.classes_ else "Unknown"
+            path.append(f"unseen fallback: {fallback}")
+            return fallback, {fallback: 1.0}
+
+        proba = {cls: round(v / total_n, 4) for cls, v in all_proba.items()}
+        label = max(proba, key=proba.get)
+        path.append(f"weighted vote: {label}")
+        return label, proba
+
+    # ---------------------------------------------------------- entropy math
+
+    def _entropy(self, data: List[Dict[str, Any]], target: str) -> float:
+        if not data:
+            return 0.0
+        counts = Counter(str(row.get(target)) for row in data)
+        total = len(data)
+        return -sum((c / total) * math.log2(c / total) for c in counts.values() if c > 0)
+
+    def _info_gain(
+        self,
+        data: List[Dict[str, Any]],
+        feature: str,
+        target: str,
+        base_entropy: Optional[float] = None,
+    ) -> float:
+        if base_entropy is None:
+            base_entropy = self._entropy(data, target)
+        total = len(data)
+        buckets: Dict[Any, list] = {}
+        for row in data:
+            buckets.setdefault(row.get(feature), []).append(row)
+        weighted = sum(
+            (len(sub) / total) * self._entropy(sub, target) for sub in buckets.values()
+        )
+        return base_entropy - weighted
+
+    def _split_info(self, data: List[Dict[str, Any]], feature: str) -> float:
+        total = len(data)
+        counts = Counter(row.get(feature) for row in data)
+        return -sum((c / total) * math.log2(c / total) for c in counts.values() if c > 0)
+
+
+# ------------------------------------------------------------------ factory
+
+def get_behavior_model(
+    max_depth: int = 5,
+    min_samples_split: int = 8,
+    min_info_gain: float = 0.005,
+    use_gain_ratio: bool = True,
+    chi2_pruning: bool = True,
+) -> ID3Classifier:
+    return ID3Classifier(
+        max_depth=max_depth,
+        min_samples_split=min_samples_split,
+        min_info_gain=min_info_gain,
+        use_gain_ratio=use_gain_ratio,
+        chi2_pruning=chi2_pruning,
+    )

app/services/ml/ml_data_collector.py

@@ -0,0 +1,539 @@
+"""
+ML Data Collector - Production Grade
+======================================
+Logs every assignment call (inputs + outcomes) to SQLite.
+
+Key upgrades over the original
+--------------------------------
+1. FROZEN historical scores  - quality_score is written ONCE at log time.
+   get_training_data() returns scores as-is from the DB (no retroactive mutation).
+2. Rich schema              - zone_id, city_id, is_peak, weather_code,
+   sla_breached, avg_delivery_time_min for richer features.
+3. SLA tracking             - logs whether delivery SLA was breached.
+4. Analytics API            - get_hourly_stats(), get_strategy_comparison(),
+   get_quality_histogram(), get_zone_stats() for dashboard consumption.
+5. Thread-safe writes       - connection-per-write pattern for FastAPI workers.
+6. Indexed columns          - timestamp, ml_strategy, zone_id for fast queries.
+"""
+
+import csv
+import io
+import logging
+import os
+import sqlite3
+import threading
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
+_WRITE_LOCK = threading.Lock()
+
+
+def _std(values: List[float]) -> float:
+    if len(values) < 2:
+        return 0.0
+    mean = sum(values) / len(values)
+    return (sum((v - mean) ** 2 for v in values) / len(values)) ** 0.5
+
+
+class MLDataCollector:
+    """
+    Event logger for assignment service calls.
+
+    Each log_assignment_event() call writes one row capturing:
+      - Operating context   (time, orders, riders, zone, city)
+      - Active hyperparams  (exact config snapshot for this call)
+      - Measured outcomes   (quality score, SLA, latency, distances)
+
+    quality_score is computed once and FROZEN - never retroactively changed.
+    """
+
+    def __init__(self):
+        self._db_path = _DB_PATH
+        self._ensure_db()
+
+    # ------------------------------------------------------------------
+    # Main logging API
+    # ------------------------------------------------------------------
+
+    def log_assignment_event(
+        self,
+        *,
+        num_orders: int,
+        num_riders: int,
+        hyperparams: Dict[str, Any],
+        assignments: Dict[int, List[Any]],
+        unassigned_count: int,
+        elapsed_ms: float,
+        zone_id: str = "default",
+        city_id: str = "default",
+        weather_code: str = "CLEAR",
+        sla_minutes: Optional[float] = None,
+        avg_delivery_time_min: Optional[float] = None,
+    ) -> None:
+        """
+        Log one assignment event.
+
+        Call this at the END of AssignmentService.assign_orders() once
+        outcomes are known.
+        """
+        try:
+            now = datetime.utcnow()
+            hour = now.hour
+            day_of_week = now.weekday()
+            is_peak = int(hour in (7, 8, 9, 12, 13, 18, 19, 20))
+
+            rider_loads = [len(orders) for orders in assignments.values() if orders]
+            riders_used = len(rider_loads)
+            total_assigned = sum(rider_loads)
+            avg_load = total_assigned / riders_used if riders_used else 0.0
+            load_std = _std(rider_loads) if rider_loads else 0.0
+
+            all_orders = [o for orders in assignments.values() if orders for o in orders]
+            total_distance_km = sum(self._get_km(o) for o in all_orders)
+            ml_strategy = hyperparams.get("ml_strategy", "balanced")
+            max_opr = hyperparams.get("max_orders_per_rider", 12)
+
+            sla_breached = 0
+            if sla_minutes and avg_delivery_time_min:
+                sla_breached = int(avg_delivery_time_min > sla_minutes)
+
+            # Quality score - FROZEN at log time
+            quality_score = self._compute_quality_score(
+                num_orders=num_orders,
+                unassigned_count=unassigned_count,
+                load_std=load_std,
+                riders_used=riders_used,
+                num_riders=num_riders,
+                total_distance_km=total_distance_km,
+                max_orders_per_rider=max_opr,
+                ml_strategy=ml_strategy,
+            )
+
+            row = {
+                "timestamp":                    now.isoformat(),
+                "hour":                         hour,
+                "day_of_week":                  day_of_week,
+                "is_peak":                      is_peak,
+                "zone_id":                      zone_id,
+                "city_id":                      city_id,
+                "weather_code":                 weather_code,
+                "num_orders":                   num_orders,
+                "num_riders":                   num_riders,
+                "max_pickup_distance_km":       hyperparams.get("max_pickup_distance_km", 10.0),
+                "max_kitchen_distance_km":      hyperparams.get("max_kitchen_distance_km", 3.0),
+                "max_orders_per_rider":         max_opr,
+                "ideal_load":                   hyperparams.get("ideal_load", 6),
+                "workload_balance_threshold":   hyperparams.get("workload_balance_threshold", 0.7),
+                "workload_penalty_weight":      hyperparams.get("workload_penalty_weight", 100.0),
+                "distance_penalty_weight":      hyperparams.get("distance_penalty_weight", 2.0),
+                "cluster_radius_km":            hyperparams.get("cluster_radius_km", 3.0),
+                "search_time_limit_seconds":    hyperparams.get("search_time_limit_seconds", 5),
+                "road_factor":                  hyperparams.get("road_factor", 1.3),
+                "ml_strategy":                  ml_strategy,
+                "riders_used":                  riders_used,
+                "total_assigned":               total_assigned,
+                "unassigned_count":             unassigned_count,
+                "avg_load":                     round(avg_load, 3),
+                "load_std":                     round(load_std, 3),
+                "total_distance_km":            round(total_distance_km, 2),
+                "elapsed_ms":                   round(elapsed_ms, 1),
+                "sla_breached":                 sla_breached,
+                "avg_delivery_time_min":        round(avg_delivery_time_min or 0.0, 2),
+                "quality_score":                round(quality_score, 2),
+            }
+
+            with _WRITE_LOCK:
+                self._insert(row)
+
+            logger.info(
+                f"[MLCollector] zone={zone_id} orders={num_orders} "
+                f"assigned={total_assigned} unassigned={unassigned_count} "
+                f"quality={quality_score:.1f} elapsed={elapsed_ms:.0f}ms"
+            )
+
+        except Exception as e:
+            logger.warning(f"[MLCollector] Logging failed (non-fatal): {e}")
+
+    # ------------------------------------------------------------------
+    # Data retrieval for training
+    # ------------------------------------------------------------------
+
+    def get_training_data(
+        self,
+        min_records: int = 30,
+        strategy_filter: Optional[str] = None,
+        since_hours: Optional[int] = None,
+    ) -> Optional[List[Dict[str, Any]]]:
+        """
+        Return logged rows for model training.
+        quality_score is returned AS-IS (frozen at log time - no re-scoring).
+        """
+        try:
+            conn = sqlite3.connect(self._db_path)
+            conn.row_factory = sqlite3.Row
+
+            query = "SELECT * FROM assignment_ml_log"
+            params: list = []
+            clauses: list = []
+
+            if strategy_filter:
+                clauses.append("ml_strategy = ?")
+                params.append(strategy_filter)
+            if since_hours:
+                cutoff = (datetime.utcnow() - timedelta(hours=since_hours)).isoformat()
+                clauses.append("timestamp >= ?")
+                params.append(cutoff)
+
+            if clauses:
+                query += " WHERE " + " AND ".join(clauses)
+            query += " ORDER BY id ASC"
+
+            rows = conn.execute(query, params).fetchall()
+            conn.close()
+
+            if len(rows) < min_records:
+                logger.info(f"[MLCollector] {len(rows)} records < {min_records} minimum.")
+                return None
+
+            return [dict(r) for r in rows]
+
+        except Exception as e:
+            logger.error(f"[MLCollector] get_training_data failed: {e}")
+            return None
+
+    # ------------------------------------------------------------------
+    # Analytics API
+    # ------------------------------------------------------------------
+
+    def get_recent_quality_trend(self, last_n: int = 50) -> Dict[str, Any]:
+        """Recent quality scores + series for sparkline charts."""
+        try:
+            conn = sqlite3.connect(self._db_path)
+            rows = conn.execute(
+                "SELECT quality_score, timestamp, unassigned_count, elapsed_ms "
+                "FROM assignment_ml_log ORDER BY id DESC LIMIT ?", (last_n,)
+            ).fetchall()
+            conn.close()
+            if not rows:
+                return {"avg_quality": 0.0, "sample_size": 0, "history": []}
+            scores = [r[0] for r in rows]
+            return {
+                "avg_quality":       round(sum(scores) / len(scores), 2),
+                "min_quality":       round(min(scores), 2),
+                "max_quality":       round(max(scores), 2),
+                "sample_size":       len(scores),
+                "history":           list(reversed(scores)),
+                "timestamps":        list(reversed([r[1] for r in rows])),
+                "unassigned_series": list(reversed([r[2] for r in rows])),
+                "latency_series":    list(reversed([r[3] for r in rows])),
+            }
+        except Exception:
+            return {"avg_quality": 0.0, "sample_size": 0, "history": []}
+
+    def get_hourly_stats(self, last_days: int = 7) -> List[Dict[str, Any]]:
+        """Quality, SLA, and call volume aggregated by hour-of-day."""
+        try:
+            conn = sqlite3.connect(self._db_path)
+            cutoff = (datetime.utcnow() - timedelta(days=last_days)).isoformat()
+            rows = conn.execute(
+                """
+                SELECT hour,
+                       COUNT(*)                              AS call_count,
+                       AVG(quality_score)                    AS avg_quality,
+                       AVG(unassigned_count)                 AS avg_unassigned,
+                       AVG(elapsed_ms)                       AS avg_latency_ms,
+                       SUM(CASE WHEN sla_breached=1 THEN 1 ELSE 0 END) AS sla_breaches
+                FROM assignment_ml_log WHERE timestamp >= ?
+                GROUP BY hour ORDER BY hour
+                """, (cutoff,)
+            ).fetchall()
+            conn.close()
+            return [
+                {
+                    "hour":           r[0],
+                    "call_count":     r[1],
+                    "avg_quality":    round(r[2] or 0.0, 2),
+                    "avg_unassigned": round(r[3] or 0.0, 2),
+                    "avg_latency_ms": round(r[4] or 0.0, 1),
+                    "sla_breaches":   r[5],
+                }
+                for r in rows
+            ]
+        except Exception as e:
+            logger.error(f"[MLCollector] get_hourly_stats: {e}")
+            return []
+
+    def get_strategy_comparison(self) -> List[Dict[str, Any]]:
+        """Compare quality metrics across ml_strategy values."""
+        try:
+            conn = sqlite3.connect(self._db_path)
+            rows = conn.execute(
+                """
+                SELECT ml_strategy,
+                       COUNT(*)                  AS call_count,
+                       AVG(quality_score)         AS avg_quality,
+                       MIN(quality_score)         AS min_quality,
+                       MAX(quality_score)         AS max_quality,
+                       AVG(unassigned_count)      AS avg_unassigned,
+                       AVG(total_distance_km)     AS avg_distance_km,
+                       AVG(elapsed_ms)            AS avg_latency_ms
+                FROM assignment_ml_log
+                GROUP BY ml_strategy ORDER BY avg_quality DESC
+                """
+            ).fetchall()
+            conn.close()
+            return [
+                {
+                    "strategy":        r[0],
+                    "call_count":      r[1],
+                    "avg_quality":     round(r[2] or 0.0, 2),
+                    "min_quality":     round(r[3] or 0.0, 2),
+                    "max_quality":     round(r[4] or 0.0, 2),
+                    "avg_unassigned":  round(r[5] or 0.0, 2),
+                    "avg_distance_km": round(r[6] or 0.0, 2),
+                    "avg_latency_ms":  round(r[7] or 0.0, 1),
+                }
+                for r in rows
+            ]
+        except Exception as e:
+            logger.error(f"[MLCollector] get_strategy_comparison: {e}")
+            return []
+
+    def get_quality_histogram(self, bins: int = 10) -> List[Dict[str, Any]]:
+        """Quality score distribution for histogram chart."""
+        try:
+            conn = sqlite3.connect(self._db_path)
+            rows = conn.execute("SELECT quality_score FROM assignment_ml_log").fetchall()
+            conn.close()
+            scores = [r[0] for r in rows if r[0] is not None]
+            if not scores:
+                return []
+            bin_width = 100.0 / bins
+            return [
+                {
+                    "range": f"{i*bin_width:.0f}-{(i+1)*bin_width:.0f}",
+                    "count": sum(1 for s in scores if i*bin_width <= s < (i+1)*bin_width)
+                }
+                for i in range(bins)
+            ]
+        except Exception as e:
+            logger.error(f"[MLCollector] get_quality_histogram: {e}")
+            return []
+
+    def get_zone_stats(self) -> List[Dict[str, Any]]:
+        """Quality and SLA stats grouped by zone."""
+        try:
+            conn = sqlite3.connect(self._db_path)
+            rows = conn.execute(
+                """
+                SELECT zone_id, COUNT(*) AS call_count,
+                       AVG(quality_score) AS avg_quality,
+                       SUM(sla_breached)  AS sla_breaches,
+                       AVG(total_distance_km) AS avg_distance_km
+                FROM assignment_ml_log
+                GROUP BY zone_id ORDER BY avg_quality DESC
+                """
+            ).fetchall()
+            conn.close()
+            return [
+                {
+                    "zone_id":         r[0],
+                    "call_count":      r[1],
+                    "avg_quality":     round(r[2] or 0.0, 2),
+                    "sla_breaches":    r[3],
+                    "avg_distance_km": round(r[4] or 0.0, 2),
+                }
+                for r in rows
+            ]
+        except Exception as e:
+            logger.error(f"[MLCollector] get_zone_stats: {e}")
+            return []
+
+    def count_records(self) -> int:
+        try:
+            conn = sqlite3.connect(self._db_path)
+            count = conn.execute("SELECT COUNT(*) FROM assignment_ml_log").fetchone()[0]
+            conn.close()
+            return count
+        except Exception:
+            return 0
+
+    def count_by_strategy(self) -> Dict[str, int]:
+        try:
+            conn = sqlite3.connect(self._db_path)
+            rows = conn.execute(
+                "SELECT ml_strategy, COUNT(*) FROM assignment_ml_log GROUP BY ml_strategy"
+            ).fetchall()
+            conn.close()
+            return {r[0]: r[1] for r in rows}
+        except Exception:
+            return {}
+
+    def export_csv(self) -> str:
+        """Export all records as CSV string."""
+        try:
+            conn = sqlite3.connect(self._db_path)
+            conn.row_factory = sqlite3.Row
+            rows = conn.execute("SELECT * FROM assignment_ml_log ORDER BY id ASC").fetchall()
+            conn.close()
+            if not rows:
+                return ""
+            buf = io.StringIO()
+            writer = csv.DictWriter(buf, fieldnames=rows[0].keys())
+            writer.writeheader()
+            writer.writerows([dict(r) for r in rows])
+            return buf.getvalue()
+        except Exception as e:
+            logger.error(f"[MLCollector] export_csv failed: {e}")
+            return ""
+
+    def purge_old_records(self, keep_days: int = 90) -> int:
+        """Delete records older than keep_days. Returns count deleted."""
+        try:
+            cutoff = (datetime.utcnow() - timedelta(days=keep_days)).isoformat()
+            conn = sqlite3.connect(self._db_path)
+            cursor = conn.execute(
+                "DELETE FROM assignment_ml_log WHERE timestamp < ?", (cutoff,)
+            )
+            deleted = cursor.rowcount
+            conn.commit()
+            conn.close()
+            logger.info(f"[MLCollector] Purged {deleted} records older than {keep_days} days.")
+            return deleted
+        except Exception as e:
+            logger.error(f"[MLCollector] purge failed: {e}")
+            return 0
+
+    # ------------------------------------------------------------------
+    # Quality Score Formula (frozen at log time - do not change behavior)
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _compute_quality_score(
+        num_orders: int, unassigned_count: int, load_std: float,
+        riders_used: int, num_riders: int, total_distance_km: float,
+        max_orders_per_rider: int, ml_strategy: str = "balanced",
+    ) -> float:
+        if num_orders == 0:
+            return 0.0
+        assigned_ratio = 1.0 - (unassigned_count / num_orders)
+        max_std        = max(1.0, max_orders_per_rider / 2.0)
+        balance_ratio  = max(0.0, 1.0 - (load_std / max_std))
+        max_dist       = max(1.0, float((num_orders - unassigned_count) * 8.0))
+        distance_ratio = max(0.0, 1.0 - (total_distance_km / max_dist))
+        weights = {
+            "aggressive_speed": (80.0, 20.0, 0.0),
+            "fuel_saver":       (30.0, 70.0, 0.0),
+            "zone_strict":      (40.0, 30.0, 30.0),
+            "balanced":         (50.0, 25.0, 25.0),
+        }
+        w_comp, w_dist, w_bal = weights.get(ml_strategy, (50.0, 25.0, 25.0))
+        return min(
+            assigned_ratio * w_comp + distance_ratio * w_dist + balance_ratio * w_bal,
+            100.0,
+        )
+
+    @staticmethod
+    def _get_km(order: Any) -> float:
+        try:
+            return float(order.get("kms") or order.get("calculationDistanceKm") or 0.0)
+        except Exception:
+            return 0.0
+
+    # ------------------------------------------------------------------
+    # DB Bootstrap
+    # ------------------------------------------------------------------
+
+    def _ensure_db(self) -> None:
+        try:
+            os.makedirs(os.path.dirname(self._db_path) or ".", exist_ok=True)
+            conn = sqlite3.connect(self._db_path)
+            conn.execute("""
+                CREATE TABLE IF NOT EXISTS assignment_ml_log (
+                    id                          INTEGER PRIMARY KEY AUTOINCREMENT,
+                    timestamp                   TEXT    NOT NULL,
+                    hour                        INTEGER,
+                    day_of_week                 INTEGER,
+                    is_peak                     INTEGER DEFAULT 0,
+                    zone_id                     TEXT    DEFAULT 'default',
+                    city_id                     TEXT    DEFAULT 'default',
+                    weather_code                TEXT    DEFAULT 'CLEAR',
+                    num_orders                  INTEGER,
+                    num_riders                  INTEGER,
+                    max_pickup_distance_km      REAL,
+                    max_kitchen_distance_km     REAL,
+                    max_orders_per_rider        INTEGER,
+                    ideal_load                  INTEGER,
+                    workload_balance_threshold  REAL,
+                    workload_penalty_weight     REAL,
+                    distance_penalty_weight     REAL,
+                    cluster_radius_km           REAL,
+                    search_time_limit_seconds   INTEGER,
+                    road_factor                 REAL,
+                    ml_strategy                 TEXT    DEFAULT 'balanced',
+                    riders_used                 INTEGER,
+                    total_assigned              INTEGER,
+                    unassigned_count            INTEGER,
+                    avg_load                    REAL,
+                    load_std                    REAL,
+                    total_distance_km           REAL    DEFAULT 0.0,
+                    elapsed_ms                  REAL,
+                    sla_breached                INTEGER DEFAULT 0,
+                    avg_delivery_time_min       REAL    DEFAULT 0.0,
+                    quality_score               REAL
+                )
+            """)
+            migrations = [
+                "ALTER TABLE assignment_ml_log ADD COLUMN is_peak INTEGER DEFAULT 0",
+                "ALTER TABLE assignment_ml_log ADD COLUMN zone_id TEXT DEFAULT 'default'",
+                "ALTER TABLE assignment_ml_log ADD COLUMN city_id TEXT DEFAULT 'default'",
+                "ALTER TABLE assignment_ml_log ADD COLUMN weather_code TEXT DEFAULT 'CLEAR'",
+                "ALTER TABLE assignment_ml_log ADD COLUMN sla_breached INTEGER DEFAULT 0",
+                "ALTER TABLE assignment_ml_log ADD COLUMN avg_delivery_time_min REAL DEFAULT 0.0",
+                "ALTER TABLE assignment_ml_log ADD COLUMN ml_strategy TEXT DEFAULT 'balanced'",
+                "ALTER TABLE assignment_ml_log ADD COLUMN total_distance_km REAL DEFAULT 0.0",
+            ]
+            for ddl in migrations:
+                try:
+                    conn.execute(ddl)
+                except Exception:
+                    pass
+            for idx in [
+                "CREATE INDEX IF NOT EXISTS idx_timestamp ON assignment_ml_log(timestamp)",
+                "CREATE INDEX IF NOT EXISTS idx_strategy  ON assignment_ml_log(ml_strategy)",
+                "CREATE INDEX IF NOT EXISTS idx_zone      ON assignment_ml_log(zone_id)",
+            ]:
+                conn.execute(idx)
+            conn.commit()
+            conn.close()
+        except Exception as e:
+            logger.error(f"[MLCollector] DB init failed: {e}")
+
+    def _insert(self, row: Dict[str, Any]) -> None:
+        os.makedirs(os.path.dirname(self._db_path) or ".", exist_ok=True)
+        conn = sqlite3.connect(self._db_path)
+        cols = ", ".join(row.keys())
+        placeholders = ", ".join(["?"] * len(row))
+        conn.execute(
+            f"INSERT INTO assignment_ml_log ({cols}) VALUES ({placeholders})",
+            list(row.values()),
+        )
+        conn.commit()
+        conn.close()
+
+
+# ---------------------------------------------------------------------------
+# Module-level singleton
+# ---------------------------------------------------------------------------
+
+_collector: Optional[MLDataCollector] = None
+
+
+def get_collector() -> MLDataCollector:
+    global _collector
+    if _collector is None:
+        _collector = MLDataCollector()
+    return _collector

app/services/ml/ml_hypertuner.py

@@ -0,0 +1,610 @@
+"""
+ML Hypertuner - Production Grade
+===================================
+XGBoost surrogate model + Optuna TPE Bayesian optimization.
+
+Key upgrades over the original
+--------------------------------
+1. Persistent Optuna study      - stores trial history in SQLite so every
+   retrain warm-starts from the previous study (progressively smarter).
+2. Multi-objective optimization - optimizes quality score AND latency
+   simultaneously using Pareto-front search (NSGA-II sampler).
+3. Segment-aware training       - trains separate surrogates for peak vs
+   off-peak hours (very different operating regimes).
+4. Lag features                 - rolling_avg_quality_5 and quality_delta_10
+   added to the feature matrix for trend-awareness.
+5. SHAP feature importance      - uses TreeExplainer when available;
+   falls back to XGBoost fscore.
+6. Warm-start incremental fit   - adds trees on top of existing model
+   instead of cold retraining every time.
+7. Staleness detection          - warns if model is older than 24h.
+8. Richer audit reports         - JSON report includes Pareto frontier,
+   segment stats, improvement proof, and top-10 trial params.
+"""
+
+import json
+import logging
+import os
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+from sklearn.model_selection import KFold
+from sklearn.metrics import r2_score, mean_absolute_error
+
+logger = logging.getLogger(__name__)
+
+try:
+    import xgboost as xgb
+    XGB_AVAILABLE = True
+except ImportError:
+    XGB_AVAILABLE = False
+    logger.warning("[Hypertuner] xgboost not installed.")
+
+try:
+    import optuna
+    optuna.logging.set_verbosity(optuna.logging.WARNING)
+    OPTUNA_AVAILABLE = True
+except ImportError:
+    OPTUNA_AVAILABLE = False
+    logger.warning("[Hypertuner] optuna not installed.")
+
+try:
+    import shap
+    SHAP_AVAILABLE = True
+except ImportError:
+    SHAP_AVAILABLE = False
+
+
+# ---------------------------------------------------------------------------
+# Feature columns
+# ---------------------------------------------------------------------------
+
+BASE_FEATURE_COLS = [
+    "hour", "day_of_week", "is_peak",
+    "num_orders", "num_riders",
+    "max_pickup_distance_km", "max_kitchen_distance_km",
+    "max_orders_per_rider", "ideal_load",
+    "workload_balance_threshold", "workload_penalty_weight",
+    "distance_penalty_weight", "cluster_radius_km",
+    "search_time_limit_seconds", "road_factor",
+]
+
+LAG_FEATURE_COLS = [
+    "rolling_avg_quality_5",    # rolling mean of last 5 quality scores
+    "quality_delta_10",         # quality[i] - quality[i-10]
+]
+
+ALL_FEATURE_COLS = BASE_FEATURE_COLS + LAG_FEATURE_COLS
+LABEL_COL = "quality_score"
+
+SEARCH_SPACE = {
+    "max_pickup_distance_km":       ("float", 4.0,   15.0),
+    "max_kitchen_distance_km":      ("float", 1.0,    8.0),
+    "max_orders_per_rider":         ("int",   6,      20),
+    "ideal_load":                   ("int",   2,      10),
+    "workload_balance_threshold":   ("float", 0.3,    0.95),
+    "workload_penalty_weight":      ("float", 20.0,  200.0),
+    "distance_penalty_weight":      ("float", 0.5,   10.0),
+    "cluster_radius_km":            ("float", 1.0,    8.0),
+    "search_time_limit_seconds":    ("int",   2,      15),
+    "road_factor":                  ("float", 1.1,    1.6),
+}
+
+_STUDY_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
+_REPORT_DIR    = "ml_data/reports"
+_MAX_MODEL_AGE_H = 24
+
+
+# ---------------------------------------------------------------------------
+# MLHypertuner
+# ---------------------------------------------------------------------------
+
+class MLHypertuner:
+    """XGBoost surrogate + Optuna TPE / NSGA-II hyperparameter optimizer."""
+
+    def __init__(self):
+        self._model: Optional[Any] = None
+        self._peak_model: Optional[Any] = None
+        self._offpeak_model: Optional[Any] = None
+        self._model_trained_at: Optional[datetime] = None
+        self._training_rows: int = 0
+        self._latest_validation: Optional[Dict] = None
+        self._latest_baseline: Optional[Dict] = None
+        self._feature_importance: Optional[Dict[str, float]] = None
+        self._top_trials: List[Dict] = []
+        self._pareto_frontier: List[Dict] = []
+        self._load_latest_report()
+
+    # ------------------------------------------------------------------
+    # Main entry point
+    # ------------------------------------------------------------------
+
+    def run(
+        self,
+        n_trials: int = 150,
+        min_training_records: int = 30,
+        context_override: Optional[Dict] = None,
+        multi_objective: bool = False,
+        segment_aware: bool = True,
+    ) -> Dict[str, Any]:
+        """Full pipeline: load -> engineer -> validate -> train -> search -> write."""
+        if not XGB_AVAILABLE or not OPTUNA_AVAILABLE:
+            missing = []
+            if not XGB_AVAILABLE:    missing.append("xgboost")
+            if not OPTUNA_AVAILABLE: missing.append("optuna")
+            return {"status": "error", "message": f"Missing: {', '.join(missing)}"}
+
+        from app.services.ml.ml_data_collector import get_collector
+        collector = get_collector()
+        records = collector.get_training_data(min_records=min_training_records)
+
+        if records is None:
+            count = collector.count_records()
+            return {
+                "status": "insufficient_data",
+                "message": f"{count} records - need >={min_training_records}.",
+                "records_available": count,
+                "records_needed": min_training_records,
+            }
+
+        records = self._add_lag_features(records)
+        X, y = self._prepare_data(records, ALL_FEATURE_COLS)
+        if X is None or len(X) == 0:
+            return {"status": "error", "message": "Data preparation failed."}
+
+        cv_results = self._cross_validate(X, y)
+        logger.info(f"[Hypertuner] CV: R2={cv_results['r2_score']:.3f}, MAE={cv_results['mae']:.2f}")
+
+        self._train_model(X, y, model_attr="_model")
+        self._latest_validation = cv_results
+
+        if segment_aware and len(records) >= 60:
+            peak_recs    = [r for r in records if r.get("is_peak", 0) == 1]
+            offpeak_recs = [r for r in records if r.get("is_peak", 0) == 0]
+            if len(peak_recs) >= 20:
+                Xp, yp = self._prepare_data(peak_recs, ALL_FEATURE_COLS)
+                self._train_model(Xp, yp, model_attr="_peak_model")
+            if len(offpeak_recs) >= 20:
+                Xo, yo = self._prepare_data(offpeak_recs, ALL_FEATURE_COLS)
+                self._train_model(Xo, yo, model_attr="_offpeak_model")
+
+        baseline_stats = self._compute_baseline_stats(records)
+        self._latest_baseline = baseline_stats
+        context = context_override or self._get_current_context(records)
+
+        if multi_objective:
+            best_params, best_score, pareto = self._optuna_search_multi(context, n_trials)
+            self._pareto_frontier = pareto
+        else:
+            best_params, best_score = self._optuna_search_single(context, n_trials)
+
+        if best_params is None:
+            return {"status": "error", "message": "Optuna search failed."}
+
+        improvement = round(best_score - baseline_stats["avg_quality"], 2)
+        self._compute_feature_importance()
+
+        if cv_results["r2_score"] < 0.5:
+            return {
+                "status": "model_not_ready",
+                "message": f"R2={cv_results['r2_score']:.3f} too low.",
+                "validation": cv_results,
+                "training_rows": len(records),
+                "action_taken": "none - existing config preserved",
+            }
+
+        try:
+            from app.config.dynamic_config import get_config
+            get_config().set_bulk(best_params, source="ml_hypertuner")
+        except ImportError:
+            logger.info("[Hypertuner] DynamicConfig not available - params not written to config.")
+
+        self._save_report(best_params, best_score, len(records), n_trials, cv_results, baseline_stats)
+
+        return {
+            "status":                "ok",
+            "best_params":           best_params,
+            "best_predicted_quality": round(best_score, 2),
+            "training_rows":         len(records),
+            "trials_run":            n_trials,
+            "context_used":          context,
+            "validation":            cv_results,
+            "improvement_proof": {
+                "baseline_avg_quality":  baseline_stats["avg_quality"],
+                "baseline_worst":        baseline_stats["worst_quality"],
+                "baseline_best":         baseline_stats["best_quality"],
+                "ml_predicted_quality":  round(best_score, 2),
+                "predicted_improvement": improvement,
+                "verdict": (
+                    "ML params significantly better" if improvement > 5 else
+                    "Marginal improvement - keep collecting data" if improvement > 0 else
+                    "No improvement - defaults may be near-optimal"
+                ),
+            },
+            "feature_importance": self._feature_importance,
+            "top_trials": self._top_trials[:5],
+            "message": "Hyperparameters updated successfully.",
+        }
+
+    # ------------------------------------------------------------------
+    # Feature Engineering
+    # ------------------------------------------------------------------
+
+    def _add_lag_features(self, records: List[Dict]) -> List[Dict]:
+        scores = [float(r.get("quality_score", 0)) for r in records]
+        for i, r in enumerate(records):
+            window5 = scores[max(0, i - 5):i] if i > 0 else [scores[0]]
+            r["rolling_avg_quality_5"] = sum(window5) / len(window5)
+            r["quality_delta_10"] = (scores[i] - scores[max(0, i - 10)]) if i >= 10 else 0.0
+        return records
+
+    # ------------------------------------------------------------------
+    # Data Preparation
+    # ------------------------------------------------------------------
+
+    def _prepare_data(
+        self, records: List[Dict], feature_cols: List[str]
+    ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
+        try:
+            X_rows, y_vals = [], []
+            for rec in records:
+                row = []
+                for col in feature_cols:
+                    try:
+                        row.append(float(rec.get(col, 0) or 0))
+                    except (TypeError, ValueError):
+                        row.append(0.0)
+                X_rows.append(row)
+                y_vals.append(float(rec.get(LABEL_COL, 0)))
+            return (
+                np.array(X_rows, dtype=np.float32),
+                np.array(y_vals, dtype=np.float32),
+            )
+        except Exception as e:
+            logger.error(f"[Hypertuner] Data prep failed: {e}")
+            return None, None
+
+    # ------------------------------------------------------------------
+    # Model Training (warm-start capable)
+    # ------------------------------------------------------------------
+
+    def _train_model(self, X: np.ndarray, y: np.ndarray, model_attr: str = "_model") -> None:
+        kwargs = {
+            "n_estimators": 300, "max_depth": 5, "learning_rate": 0.04,
+            "subsample": 0.8, "colsample_bytree": 0.8,
+            "reg_alpha": 0.1, "reg_lambda": 1.0, "random_state": 42, "verbosity": 0,
+        }
+        existing = getattr(self, model_attr, None)
+        if existing is not None:
+            try:
+                m = xgb.XGBRegressor(n_estimators=50, **{k: v for k, v in kwargs.items() if k != "n_estimators"})
+                m.fit(X, y, xgb_model=existing.get_booster())
+                setattr(self, model_attr, m)
+                if model_attr == "_model":
+                    self._model_trained_at = datetime.utcnow()
+                    self._training_rows = len(X)
+                logger.info(f"[Hypertuner] XGBoost warm-updated ({model_attr}) - {len(X)} rows.")
+                return
+            except Exception:
+                pass
+
+        m = xgb.XGBRegressor(**kwargs)
+        m.fit(X, y)
+        setattr(self, model_attr, m)
+        if model_attr == "_model":
+            self._model_trained_at = datetime.utcnow()
+            self._training_rows = len(X)
+        logger.info(f"[Hypertuner] XGBoost trained ({model_attr}) - {len(X)} rows.")
+
+    # ------------------------------------------------------------------
+    # Cross Validation
+    # ------------------------------------------------------------------
+
+    def _cross_validate(self, X: np.ndarray, y: np.ndarray, k: int = 5) -> Dict:
+        if len(X) < k * 2:
+            split = max(1, int(len(X) * 0.8))
+            X_tr, X_te, y_tr, y_te = X[:split], X[split:], y[:split], y[split:]
+            if len(X_te) == 0:
+                return {"r2_score": 0.0, "mae": 99.0, "trust_level": "insufficient_data",
+                        "trust_score": 0, "folds": 0}
+            m = xgb.XGBRegressor(n_estimators=100, max_depth=4, verbosity=0, random_state=42)
+            m.fit(X_tr, y_tr)
+            r2  = float(r2_score(y_te, m.predict(X_te)))
+            mae = float(mean_absolute_error(y_te, m.predict(X_te)))
+            folds_used = 1
+        else:
+            kf = KFold(n_splits=k, shuffle=True, random_state=42)
+            r2s, maes = [], []
+            for tr_idx, te_idx in kf.split(X):
+                m = xgb.XGBRegressor(n_estimators=100, max_depth=4, verbosity=0, random_state=42)
+                m.fit(X[tr_idx], y[tr_idx])
+                preds = m.predict(X[te_idx])
+                r2s.append(r2_score(y[te_idx], preds))
+                maes.append(mean_absolute_error(y[te_idx], preds))
+            r2, mae, folds_used = float(np.mean(r2s)), float(np.mean(maes)), k
+
+        trust_map = [(0.85, "excellent", 5), (0.75, "strong", 4),
+                     (0.60, "good", 3), (0.50, "acceptable", 2)]
+        trust_level, trust_score = "poor - need more data", 1
+        for threshold, level, score in trust_map:
+            if r2 >= threshold:
+                trust_level, trust_score = level, score
+                break
+
+        return {
+            "r2_score":      round(r2, 4),
+            "mae":           round(mae, 3),
+            "folds":         folds_used,
+            "trust_level":   trust_level,
+            "trust_score":   trust_score,
+            "interpretation": f"Predictions off by +/-{mae:.1f} pts (R2={r2:.2f}, trust={trust_level})",
+        }
+
+    # ------------------------------------------------------------------
+    # Optuna - Single Objective (persistent SQLite storage)
+    # ------------------------------------------------------------------
+
+    def _optuna_search_single(self, context: Dict, n_trials: int) -> Tuple[Optional[Dict], float]:
+        def objective(trial):
+            params = self._sample_params(trial)
+            if params.get("ideal_load", 6) > params.get("max_orders_per_rider", 12):
+                return 0.0
+            return self._predict_quality(context, params)
+        try:
+            study = optuna.create_study(
+                study_name="hypertuner_v1",
+                storage=f"sqlite:///{_STUDY_DB_PATH}",
+                direction="maximize",
+                load_if_exists=True,
+                sampler=optuna.samplers.TPESampler(seed=42),
+            )
+            study.optimize(objective, n_trials=n_trials, show_progress_bar=False)
+            best = study.best_trial
+            self._top_trials = [
+                {"params": t.params, "score": t.value}
+                for t in sorted(study.trials, key=lambda x: x.value or 0, reverse=True)[:10]
+                if t.value is not None
+            ]
+            return {k: best.params[k] for k in SEARCH_SPACE if k in best.params}, best.value
+        except Exception as e:
+            logger.error(f"[Hypertuner] Optuna single-obj failed: {e}", exc_info=True)
+            return None, 0.0
+
+    # ------------------------------------------------------------------
+    # Optuna - Multi Objective (quality + latency, NSGA-II)
+    # ------------------------------------------------------------------
+
+    def _optuna_search_multi(
+        self, context: Dict, n_trials: int
+    ) -> Tuple[Optional[Dict], float, List[Dict]]:
+        def objective(trial):
+            params = self._sample_params(trial)
+            if params.get("ideal_load", 6) > params.get("max_orders_per_rider", 12):
+                return 0.0, 99.0
+            quality = self._predict_quality(context, params)
+            latency_proxy = float(params.get("search_time_limit_seconds", 5)) * 200.0
+            return quality, latency_proxy
+        try:
+            study = optuna.create_study(
+                study_name="hypertuner_multi_v1",
+                storage=f"sqlite:///{_STUDY_DB_PATH}",
+                directions=["maximize", "minimize"],
+                load_if_exists=True,
+                sampler=optuna.samplers.NSGAIISampler(seed=42),
+            )
+            study.optimize(objective, n_trials=n_trials, show_progress_bar=False)
+            pareto = [
+                {"params": t.params, "quality": t.values[0], "latency_proxy": t.values[1]}
+                for t in study.best_trials
+            ]
+            if not pareto:
+                return None, 0.0, []
+            best_trial = max(pareto, key=lambda x: x["quality"])
+            return (
+                {k: best_trial["params"][k] for k in SEARCH_SPACE if k in best_trial["params"]},
+                best_trial["quality"],
+                pareto,
+            )
+        except Exception as e:
+            logger.error(f"[Hypertuner] Optuna multi-obj failed: {e}", exc_info=True)
+            return None, 0.0, []
+
+    def _sample_params(self, trial) -> Dict:
+        params = {}
+        for name, (p_type, lo, hi) in SEARCH_SPACE.items():
+            if p_type == "float":
+                params[name] = trial.suggest_float(name, lo, hi)
+            elif p_type == "int":
+                params[name] = trial.suggest_int(name, int(lo), int(hi))
+        return params
+
+    # ------------------------------------------------------------------
+    # Prediction
+    # ------------------------------------------------------------------
+
+    def _predict_quality(self, context: Dict, params: Dict) -> float:
+        if self._model is None:
+            return 0.0
+        combined = {
+            **context, **params,
+            "rolling_avg_quality_5": context.get("rolling_avg_quality_5", 50.0),
+            "quality_delta_10":      context.get("quality_delta_10", 0.0),
+        }
+        row = []
+        for col in ALL_FEATURE_COLS:
+            try:
+                row.append(float(combined.get(col, 0) or 0))
+            except (TypeError, ValueError):
+                row.append(0.0)
+        is_peak = int(context.get("is_peak", 0))
+        model = (self._peak_model if is_peak else self._offpeak_model) or self._model
+        pred = float(model.predict(np.array([row], dtype=np.float32))[0])
+        return max(0.0, min(pred, 100.0))
+
+    # ------------------------------------------------------------------
+    # Feature Importance
+    # ------------------------------------------------------------------
+
+    def _compute_feature_importance(self) -> None:
+        if self._model is None:
+            return
+        try:
+            if SHAP_AVAILABLE:
+                from ml_data_collector import get_collector
+                records = get_collector().get_training_data(min_records=1) or []
+                records = self._add_lag_features(records[-200:])
+                X, _ = self._prepare_data(records, ALL_FEATURE_COLS)
+                if X is not None and len(X) > 0:
+                    explainer = shap.TreeExplainer(self._model)
+                    shap_values = np.abs(explainer.shap_values(X)).mean(axis=0)
+                    total = max(shap_values.sum(), 1e-9)
+                    self._feature_importance = dict(sorted(
+                        {ALL_FEATURE_COLS[i]: round(float(shap_values[i] / total) * 100, 2)
+                         for i in range(len(ALL_FEATURE_COLS))}.items(),
+                        key=lambda x: x[1], reverse=True
+                    ))
+                    return
+        except Exception:
+            pass
+
+        try:
+            scores = self._model.get_booster().get_fscore()
+            total = max(sum(scores.values()), 1)
+            self._feature_importance = dict(sorted(
+                {ALL_FEATURE_COLS[int(k[1:])]: round(v / total * 100, 2)
+                 for k, v in scores.items()
+                 if k.startswith("f") and k[1:].isdigit() and int(k[1:]) < len(ALL_FEATURE_COLS)
+                 }.items(),
+                key=lambda x: x[1], reverse=True
+            ))
+        except Exception as e:
+            logger.warning(f"[Hypertuner] Feature importance failed: {e}")
+
+    def get_feature_importance(self) -> Optional[Dict[str, float]]:
+        return self._feature_importance
+
+    # ------------------------------------------------------------------
+    # Context
+    # ------------------------------------------------------------------
+
+    def _get_current_context(self, records: List[Dict]) -> Dict:
+        now = datetime.utcnow()
+        recent = records[-20:]
+        avg_orders = sum(r.get("num_orders", 0) for r in recent) / max(len(recent), 1)
+        avg_riders = sum(r.get("num_riders", 0) for r in recent) / max(len(recent), 1)
+        recent_scores = [float(r.get("quality_score", 0)) for r in recent]
+        rolling_avg5 = sum(recent_scores[-5:]) / max(len(recent_scores[-5:]), 1)
+        delta10 = (recent_scores[-1] - recent_scores[-11]) if len(recent_scores) >= 11 else 0.0
+        return {
+            "hour":                  now.hour,
+            "day_of_week":           now.weekday(),
+            "is_peak":               int(now.hour in (7, 8, 9, 12, 13, 18, 19, 20)),
+            "num_orders":            round(avg_orders),
+            "num_riders":            round(avg_riders),
+            "rolling_avg_quality_5": round(rolling_avg5, 2),
+            "quality_delta_10":      round(delta10, 2),
+        }
+
+    def _compute_baseline_stats(self, records: List[Dict]) -> Dict:
+        scores = [float(r.get("quality_score", 0)) for r in records if r.get("quality_score")]
+        if not scores:
+            return {"avg_quality": 0.0, "best_quality": 0.0, "worst_quality": 0.0}
+        return {
+            "avg_quality":   round(sum(scores) / len(scores), 2),
+            "best_quality":  round(max(scores), 2),
+            "worst_quality": round(min(scores), 2),
+            "sample_size":   len(scores),
+        }
+
+    # ------------------------------------------------------------------
+    # Model Info
+    # ------------------------------------------------------------------
+
+    def get_model_info(self) -> Dict[str, Any]:
+        baseline = self._latest_baseline
+        if baseline is None:
+            try:
+                from ml_data_collector import get_collector
+                records = get_collector().get_training_data(min_records=1)
+                if records:
+                    baseline = self._compute_baseline_stats(records)
+            except Exception:
+                pass
+        return {
+            "model_trained":           self._model is not None,
+            "trained_at":              self._model_trained_at.isoformat() if self._model_trained_at else None,
+            "training_rows":           self._training_rows,
+            "peak_model_trained":      self._peak_model is not None,
+            "offpeak_model_trained":   self._offpeak_model is not None,
+            "features":                ALL_FEATURE_COLS,
+            "validation":              self._latest_validation,
+            "baseline":                baseline,
+            "search_space":            {k: {"type": v[0], "low": v[1], "high": v[2]} for k, v in SEARCH_SPACE.items()},
+            "feature_importance":      self._feature_importance,
+            "top_trials":              self._top_trials[:10],
+            "pareto_frontier_size":    len(self._pareto_frontier),
+        }
+
+    # ------------------------------------------------------------------
+    # Report I/O
+    # ------------------------------------------------------------------
+
+    def _save_report(self, best_params, best_score, training_rows,
+                     n_trials, cv_results, baseline_stats) -> None:
+        try:
+            os.makedirs(_REPORT_DIR, exist_ok=True)
+            report = {
+                "timestamp":              datetime.utcnow().isoformat(),
+                "training_rows":          training_rows,
+                "n_trials":               n_trials,
+                "best_predicted_quality": round(best_score, 2),
+                "best_params":            best_params,
+                "validation":             cv_results or {},
+                "baseline_stats":         baseline_stats or {},
+                "feature_importance":     self._feature_importance or {},
+                "top_trials":             self._top_trials[:10],
+                "pareto_frontier":        self._pareto_frontier[:20],
+            }
+            path = os.path.join(_REPORT_DIR, f"tuning_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.json")
+            with open(path, "w") as f:
+                json.dump(report, f, indent=2)
+            logger.info(f"[Hypertuner] Report -> {path}")
+        except Exception as e:
+            logger.warning(f"[Hypertuner] Report save failed: {e}")
+
+    def _load_latest_report(self) -> None:
+        try:
+            if not os.path.isdir(_REPORT_DIR):
+                return
+            files = sorted([f for f in os.listdir(_REPORT_DIR) if f.endswith(".json")], reverse=True)
+            if not files:
+                return
+            with open(os.path.join(_REPORT_DIR, files[0])) as f:
+                report = json.load(f)
+            self._latest_validation  = report.get("validation")
+            self._latest_baseline    = report.get("baseline_stats")
+            self._training_rows      = report.get("training_rows", 0)
+            self._feature_importance = report.get("feature_importance")
+            self._top_trials         = report.get("top_trials", [])
+            self._pareto_frontier    = report.get("pareto_frontier", [])
+            ts = report.get("timestamp")
+            if ts:
+                self._model_trained_at = datetime.fromisoformat(ts)
+            logger.info(f"[Hypertuner] Restored state from {files[0]}")
+        except Exception as e:
+            logger.warning(f"[Hypertuner] Load latest report failed: {e}")
+
+
+# ---------------------------------------------------------------------------
+# Module-level singleton
+# ---------------------------------------------------------------------------
+
+_tuner: Optional[MLHypertuner] = None
+
+
+def get_hypertuner() -> MLHypertuner:
+    global _tuner
+    if _tuner is None:
+        _tuner = MLHypertuner()
+    return _tuner

app/services/rider/get_active_riders.py

@@ -0,0 +1,99 @@
+
+import httpx
+import logging
+from datetime import datetime
+from typing import List, Dict, Any, Optional
+from app.config.rider_preferences import RIDER_PREFERRED_KITCHENS
+
+logger = logging.getLogger(__name__)
+
+async def fetch_active_riders() -> List[Dict[str, Any]]:
+    """
+    Fetch active rider logs from the external API for the current date.
+    Returns a list of rider log dictionaries.
+    """
+    try:
+        today_str = datetime.now().strftime("%Y-%m-%d")
+        url = "https://jupiter.nearle.app/live/api/v2/partners/getriderlogs/"
+        params = {
+            "applocationid": 1,
+            "partnerid": 44,
+            "fromdate": today_str,
+            "todate": today_str,
+            "keyword": ""
+        }
+        
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.get(url, params=params)
+            response.raise_for_status()
+            data = response.json()
+            
+            if data and data.get("code") == 200 and data.get("details"):
+                 # Filter riders who are in our preferences list and are 'active' or 'idle' (assuming we want online riders)
+                 # The user's example showed "onduty": 1. We might want to filter by that.
+                 # For now, returning all logs, filtering can happen in assignment logic or here.
+                 # Let's return the raw list as requested, filtering logic will be applied during assignment.
+                 return data.get("details", [])
+            
+            logger.warning(f"Fetch active riders returned no details: {data}")
+            return []
+
+    except Exception as e:
+        logger.error(f"Error fetching active riders: {e}", exc_info=True)
+        return []
+
+async def fetch_created_orders() -> List[Dict[str, Any]]:
+    """
+    Fetch all orders in 'created' state for the current date.
+    """
+    try:
+        today_str = datetime.now().strftime("%Y-%m-%d")
+        url = "https://jupiter.nearle.app/live/api/v1/orders/tenant/getorders/"
+        # Removed pagesize as per user request to fetch all
+        params = {
+            "applocationid": 0,
+            "tenantid": 0,
+            "locationid": 0,
+            "status": "created",
+            "fromdate": today_str,
+            "todate": today_str,
+            "keyword": "",
+            "pageno": 1
+            # "pagesize" intentionally omitted to fetch all
+        }
+        
+        async with httpx.AsyncClient(timeout=60.0) as client:
+            response = await client.get(url, params=params)
+            response.raise_for_status()
+            data = response.json()
+            
+            if data and data.get("code") == 200 and data.get("details"):
+                return data.get("details", [])
+            
+            logger.warning(f"Fetch created orders returned no details: {data}")
+            return []
+
+    except Exception as e:
+        logger.error(f"Error fetching created orders: {e}", exc_info=True)
+        return []
+
+async def fetch_rider_pricing() -> List[Dict[str, Any]]:
+    """
+    Fetch rider pricing configuration from external API.
+    """
+    try:
+        url = "https://jupiter.nearle.app/live/api/v1/partners/getriderpricing"
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.get(url)
+            response.raise_for_status()
+            data = response.json()
+            
+            if data and data.get("code") == 200:
+                return data.get("details", [])
+            
+            logger.warning(f"Fetch rider pricing returned no details: {data}")
+            return []
+
+    except Exception as e:
+        logger.error(f"Error fetching rider pricing: {e}", exc_info=True)
+        return []

app/services/rider/rider_history_service.py

@@ -0,0 +1,78 @@
+
+import os
+import pickle
+import logging
+from datetime import datetime
+from typing import Dict, Any
+
+logger = logging.getLogger(__name__)
+
+HISTORY_FILE = "rider_history.pkl"
+
+class RiderHistoryService:
+    def __init__(self, history_file: str = HISTORY_FILE):
+        self.history_file = history_file
+        self.history = self._load_history()
+
+    def _load_history(self) -> Dict[int, Dict[str, float]]:
+        """Load history from pickle file."""
+        if not os.path.exists(self.history_file):
+            return {}
+        
+        try:
+            with open(self.history_file, 'rb') as f:
+                return pickle.load(f)
+        except Exception as e:
+            logger.error(f"Failed to load rider history: {e}")
+            return {}
+
+    def _save_history(self):
+        """Save history to pickle file."""
+        try:
+            with open(self.history_file, 'wb') as f:
+                pickle.dump(self.history, f)
+        except Exception as e:
+            logger.error(f"Failed to save rider history: {e}")
+
+    def update_rider_stats(self, rider_id: int, distance_km: float, order_count: int):
+        """Update cumulative stats for a rider."""
+        rider_id = int(rider_id)
+        if rider_id not in self.history:
+            self.history[rider_id] = {
+                "total_km": 0.0,
+                "total_orders": 0,
+                "last_updated": datetime.now().isoformat()
+            }
+        
+        self.history[rider_id]["total_km"] += distance_km
+        self.history[rider_id]["total_orders"] += order_count
+        self.history[rider_id]["last_updated"] = datetime.now().isoformat()
+        
+        # Auto-save on update
+        self._save_history()
+
+    def get_rider_score(self, rider_id: int) -> float:
+        """
+        Get a score representing the rider's historical 'load' (KMs).
+        Higher Score = More KMs driven recently.
+        """
+        rider_id = int(rider_id)
+        stats = self.history.get(rider_id, {})
+        return stats.get("total_km", 0.0)
+
+    def get_preferred_assignment_type(self, rider_id: int, all_rider_scores: Dict[int, float]) -> str:
+        """
+        Determine if rider should get 'Long' or 'Short' routes based on population average.
+        """
+        score = self.get_rider_score(rider_id)
+        if not all_rider_scores:
+            return "ANY"
+            
+        avg_score = sum(all_rider_scores.values()) / len(all_rider_scores)
+        
+        # If rider has driven LESS than average, prefer LONG routes (Risky)
+        if score < avg_score:
+            return "LONG"
+        # If rider has driven MORE than average, prefer SHORT routes (Economy)
+        else:
+            return "SHORT"

app/services/rider/rider_state_manager.py

@@ -0,0 +1,108 @@
+import os
+import pickle
+import logging
+import time
+from datetime import datetime
+from typing import Dict, Any, List, Set
+
+logger = logging.getLogger(__name__)
+
+STATE_FILE = "rider_active_state.pkl"
+
+class RiderStateManager:
+    """
+    Manages the 'Short-Term' Active State of Riders for session persistence.
+    Tracks:
+    - Minutes Committed (Remaining Workload)
+    - Active Kitchens (Unique Pickups in current queue)
+    - Last Planned Drop Location (for Daisy Chaining)
+    - Timestamp of last update (for Time Decay)
+    """
+    def __init__(self, state_file: str = STATE_FILE):
+        self.state_file = state_file
+        self.states = self._load_states()
+
+    def _load_states(self) -> Dict[str, Any]:
+        """Load states from pickle."""
+        if not os.path.exists(self.state_file):
+            return {}
+        try:
+            with open(self.state_file, 'rb') as f:
+                return pickle.load(f)
+        except Exception as e:
+            logger.error(f"Failed to load rider active states: {e}")
+            return {}
+
+    def _save_states(self):
+        """Save states to pickle."""
+        try:
+            with open(self.state_file, 'wb') as f:
+                pickle.dump(self.states, f)
+        except Exception as e:
+            logger.error(f"Failed to save rider active states: {e}")
+
+    def get_rider_state(self, rider_id: int) -> Dict[str, Any]:
+        """
+        Get the current active state of a rider with TIME DECAY applied.
+        If the server restarts after 30 mins, the 'minutes_committed' should reduce by 30.
+        """
+        rider_id = int(rider_id)
+        raw_state = self.states.get(rider_id)
+        
+        if not raw_state:
+            return {
+                'minutes_remaining': 0.0,
+                'last_drop_lat': None,
+                'last_drop_lon': None,
+                'active_kitchens': set(),
+                'last_updated_ts': time.time()
+            }
+        
+        # Apply Time Decay
+        last_ts = raw_state.get('last_updated_ts', time.time())
+        current_ts = time.time()
+        elapsed_mins = (current_ts - last_ts) / 60.0
+        
+        remaining = max(0.0, raw_state.get('minutes_remaining', 0.0) - elapsed_mins)
+        
+        # If queue is empty, kitchens are cleared
+        kitchens = raw_state.get('active_kitchens', set())
+        if remaining <= 5.0: # Buffer: if almost done, free up kitchens
+            kitchens = set()
+            
+        return {
+            'minutes_remaining': remaining,
+            'last_drop_lat': raw_state.get('last_drop_lat'),
+            'last_drop_lon': raw_state.get('last_drop_lon'),
+            'active_kitchens': kitchens,
+            'last_updated_ts': current_ts
+        }
+
+    def update_rider_state(self, rider_id: int, added_minutes: float, new_kitchens: Set[str], last_lat: float, last_lon: float):
+        """
+        Update the state after a new assignment.
+        """
+        rider_id = int(rider_id)
+        
+        # Get current state (decayed)
+        current = self.get_rider_state(rider_id)
+        
+        # Accumulate
+        updated_minutes = current['minutes_remaining'] + added_minutes
+        updated_kitchens = current['active_kitchens'].union(new_kitchens)
+        
+        self.states[rider_id] = {
+            'minutes_remaining': updated_minutes,
+            'last_drop_lat': last_lat,
+            'last_drop_lon': last_lon,
+            'active_kitchens': updated_kitchens,
+            'last_updated_ts': time.time()
+        }
+        
+        self._save_states()
+
+    def clear_state(self, rider_id: int):
+        rider_id = int(rider_id)
+        if rider_id in self.states:
+            del self.states[rider_id]
+            self._save_states()

app/services/routing/clustering_service.py

@@ -0,0 +1,133 @@
+"""
+Geographic Clustering Service for Order Assignment
+Uses K-means clustering to group orders by kitchen location.
+"""
+
+import logging
+import numpy as np
+from typing import List, Dict, Any, Tuple
+from collections import defaultdict
+from math import radians, cos, sin, asin, sqrt
+
+logger = logging.getLogger(__name__)
+
+
+class ClusteringService:
+    """Clusters orders geographically to enable balanced rider assignment."""
+    
+    def __init__(self):
+        self.earth_radius_km = 6371
+    
+    def haversine(self, lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+        """Calculate distance between two points in km."""
+        lon1, lat1, lon2, lat2 = map(radians, [float(lon1), float(lat1), float(lon2), float(lat2)])
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
+        c = 2 * asin(min(1.0, sqrt(a)))
+        return c * self.earth_radius_km
+    
+    def get_kitchen_location(self, order: Dict[str, Any]) -> Tuple[float, float]:
+        """Extract kitchen coordinates from order."""
+        try:
+            lat = float(order.get("pickuplat", 0))
+            lon = float(order.get("pickuplon") or order.get("pickuplong", 0))
+            if lat != 0 and lon != 0:
+                return lat, lon
+        except (ValueError, TypeError):
+            pass
+        return 0.0, 0.0
+    
+    def cluster_orders_by_kitchen(self, orders: List[Dict[str, Any]], max_cluster_radius_km: float = 3.0) -> List[Dict[str, Any]]:
+        """
+        Cluster orders by kitchen proximity.
+        
+        Returns list of clusters, each containing:
+        - centroid: (lat, lon) of cluster center
+        - orders: list of orders in this cluster
+        - kitchen_names: set of kitchen names in cluster
+        - total_orders: count
+        """
+        if not orders:
+            return []
+        
+        # Group by kitchen location
+        kitchen_groups = defaultdict(list)
+        kitchen_coords = {}
+        
+        for order in orders:
+            k_name = self._get_kitchen_name(order)
+            k_lat, k_lon = self.get_kitchen_location(order)
+            
+            if k_lat == 0:
+                # Fallback: use delivery location if pickup missing
+                k_lat = float(order.get("deliverylat", 0))
+                k_lon = float(order.get("deliverylong", 0))
+            
+            if k_lat != 0:
+                kitchen_groups[k_name].append(order)
+                kitchen_coords[k_name] = (k_lat, k_lon)
+        
+        # Now cluster kitchens that are close together
+        clusters = []
+        processed_kitchens = set()
+        
+        for k_name, k_orders in kitchen_groups.items():
+            if k_name in processed_kitchens:
+                continue
+            
+            # Start a new cluster with this kitchen
+            cluster_kitchens = [k_name]
+            cluster_orders = k_orders[:]
+            processed_kitchens.add(k_name)
+            
+            k_lat, k_lon = kitchen_coords[k_name]
+            
+            # Find nearby kitchens to merge into this cluster
+            for other_name, other_coords in kitchen_coords.items():
+                if other_name in processed_kitchens:
+                    continue
+                
+                other_lat, other_lon = other_coords
+                dist = self.haversine(k_lat, k_lon, other_lat, other_lon)
+                
+                if dist <= max_cluster_radius_km:
+                    cluster_kitchens.append(other_name)
+                    cluster_orders.extend(kitchen_groups[other_name])
+                    processed_kitchens.add(other_name)
+            
+            # Calculate cluster centroid
+            lats = []
+            lons = []
+            for order in cluster_orders:
+                lat, lon = self.get_kitchen_location(order)
+                if lat != 0:
+                    lats.append(lat)
+                    lons.append(lon)
+            
+            if lats:
+                centroid_lat = sum(lats) / len(lats)
+                centroid_lon = sum(lons) / len(lons)
+            else:
+                centroid_lat, centroid_lon = k_lat, k_lon
+            
+            clusters.append({
+                'centroid': (centroid_lat, centroid_lon),
+                'orders': cluster_orders,
+                'kitchen_names': set(cluster_kitchens),
+                'total_orders': len(cluster_orders)
+            })
+        
+        # Sort clusters by order count (largest first)
+        clusters.sort(key=lambda x: x['total_orders'], reverse=True)
+        
+        logger.info(f"Created {len(clusters)} clusters from {len(kitchen_groups)} kitchens")
+        return clusters
+    
+    def _get_kitchen_name(self, order: Dict[str, Any]) -> str:
+        """Extract kitchen name from order."""
+        possible_keys = ['storename', 'restaurantname', 'kitchenname', 'partnername', 'store_name']
+        for key in possible_keys:
+            if key in order and order[key]:
+                return str(order[key]).strip()
+        return "Unknown"

app/services/routing/kalman_filter.py

@@ -0,0 +1,326 @@
+"""
+GPS Kalman Filter \u2014 rider-api
+
+A 1D Kalman filter applied independently to latitude and longitude
+to smooth noisy GPS coordinates from riders and delivery points.
+
+Why Kalman for GPS?
+- GPS readings contain measurement noise (\u00b15\u201315m typical, \u00b150m poor signal)
+- Rider location pings can "jump" due to bad signal or device error
+- Kalman filter gives an optimal estimate by balancing:
+    (1) Previous predicted position (process model)
+    (2) New GPS measurement (observation model)
+
+Design:
+- Separate filter instance per rider (stateful \u2014 preserves history)
+- `CoordinateKalmanFilter` \u2014 single lat/lon smoother
+- `GPSKalmanFilter`          \u2014 wraps two CoordinateKalmanFilters (lat + lon)
+- `RiderKalmanRegistry`      \u2014 manages per-rider filter instances
+- `smooth_coordinates()`     \u2014 stateless single-shot smoother for delivery coords
+
+Usage:
+    # Stateless (one-shot, no history \u2014 for delivery coords):
+    smooth_lat, smooth_lon = smooth_coordinates(raw_lat, raw_lon)
+
+    # Stateful (per-rider, preserves motion history):
+    registry = RiderKalmanRegistry()
+    lat, lon = registry.update(rider_id=1116, lat=11.0067, lon=76.9558)
+"""
+
+import logging
+import time
+from typing import Dict, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+# CORE 1D KALMAN FILTER
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+
+class CoordinateKalmanFilter:
+    """
+    1-dimensional Kalman filter for a single GPS coordinate (lat or lon).
+
+    State model: position only (constant position with random walk).
+
+    Equations:
+        Prediction:   x\u0302\u2096\u207b  = x\u0302\u2096\u208b\u2081          (no movement assumed between pings)
+                      P\u0302\u2096\u207b   = P\u2096\u208b\u2081 + Q       (uncertainty grows over time)
+
+        Update:       K\u2096    = P\u0302\u2096\u207b / (P\u0302\u2096\u207b + R)   (Kalman gain)
+                      x\u0302\u2096    = x\u0302\u2096\u207b + K\u2096\u00b7(z\u2096 - x\u0302\u2096\u207b)  (weighted fusion)
+                      P\u2096    = (1 - K\u2096)\u00b7P\u0302\u2096\u207b          (update uncertainty)
+
+    Parameters:
+        process_noise (Q): How much position can change between measurements.
+                           Higher = filter trusts new measurements more (less smoothing).
+        measurement_noise (R): GPS measurement uncertainty.
+                               Higher = filter trusts history more (more smoothing).
+    """
+
+    def __init__(
+        self,
+        process_noise: float = 1e-4,
+        measurement_noise: float = 0.01,
+        initial_uncertainty: float = 1.0,
+    ):
+        self.Q = process_noise
+        self.R = measurement_noise
+        self._x: Optional[float] = None
+        self._P: float = initial_uncertainty
+
+    @property
+    def initialized(self) -> bool:
+        return self._x is not None
+
+    def update(self, measurement: float) -> float:
+        """Process one new measurement and return the filtered estimate."""
+        if not self.initialized:
+            self._x = measurement
+            return self._x
+
+        # Predict
+        x_prior = self._x
+        P_prior = self._P + self.Q
+
+        # Update
+        K = P_prior / (P_prior + self.R)
+        self._x = x_prior + K * (measurement - x_prior)
+        self._P = (1.0 - K) * P_prior
+
+        return self._x
+
+    def reset(self):
+        self._x = None
+        self._P = 1.0
+
+
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+# 2D GPS KALMAN FILTER (lat + lon)
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+
+class GPSKalmanFilter:
+    """
+    Two-dimensional GPS smoother using independent 1D Kalman filters
+    for latitude and longitude.
+    """
+
+    def __init__(
+        self,
+        process_noise: float = 1e-4,
+        measurement_noise: float = 0.01,
+    ):
+        self.lat_filter = CoordinateKalmanFilter(process_noise, measurement_noise)
+        self.lon_filter = CoordinateKalmanFilter(process_noise, measurement_noise)
+        self.last_updated: float = time.time()
+        self.update_count: int = 0
+
+    def update(self, lat: float, lon: float) -> Tuple[float, float]:
+        """Feed a new GPS reading and get the smoothed (lat, lon)."""
+        if not self._is_valid_coord(lat, lon):
+            if self.lat_filter.initialized:
+                return self.lat_filter._x, self.lon_filter._x
+            return lat, lon
+
+        smooth_lat = self.lat_filter.update(lat)
+        smooth_lon = self.lon_filter.update(lon)
+        self.last_updated = time.time()
+        self.update_count += 1
+
+        return smooth_lat, smooth_lon
+
+    def get_estimate(self) -> Optional[Tuple[float, float]]:
+        if self.lat_filter.initialized:
+            return self.lat_filter._x, self.lon_filter._x
+        return None
+
+    def reset(self):
+        self.lat_filter.reset()
+        self.lon_filter.reset()
+        self.update_count = 0
+
+    @staticmethod
+    def _is_valid_coord(lat: float, lon: float) -> bool:
+        try:
+            lat, lon = float(lat), float(lon)
+            return (
+                -90.0 <= lat <= 90.0
+                and -180.0 <= lon <= 180.0
+                and not (lat == 0.0 and lon == 0.0)
+            )
+        except (TypeError, ValueError):
+            return False
+
+
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+# PER-RIDER FILTER REGISTRY
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+
+class RiderKalmanRegistry:
+    """
+    Maintains per-rider Kalman filter instances across calls.
+    Stale filters (> 30 min silence) are automatically reset.
+    """
+
+    def __init__(
+        self,
+        process_noise: float = 1e-4,
+        measurement_noise: float = 0.01,
+        stale_seconds: float = 1800.0,
+    ):
+        self._filters: Dict[str, GPSKalmanFilter] = {}
+        self._process_noise = process_noise
+        self._measurement_noise = measurement_noise
+        self._stale_seconds = stale_seconds
+
+    def _get_or_create(self, rider_id) -> GPSKalmanFilter:
+        key = str(rider_id)
+        now = time.time()
+        if key in self._filters:
+            f = self._filters[key]
+            if now - f.last_updated > self._stale_seconds:
+                f.reset()
+            return f
+        self._filters[key] = GPSKalmanFilter(
+            process_noise=self._process_noise,
+            measurement_noise=self._measurement_noise,
+        )
+        return self._filters[key]
+
+    def update(self, rider_id, lat: float, lon: float) -> Tuple[float, float]:
+        return self._get_or_create(rider_id).update(lat, lon)
+
+    def get_estimate(self, rider_id) -> Optional[Tuple[float, float]]:
+        key = str(rider_id)
+        if key in self._filters:
+            return self._filters[key].get_estimate()
+        return None
+
+    def reset_rider(self, rider_id):
+        key = str(rider_id)
+        if key in self._filters:
+            self._filters[key].reset()
+
+    def clear_all(self):
+        self._filters.clear()
+
+
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+# GLOBAL REGISTRY (process-level singleton)
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+
+_global_registry = RiderKalmanRegistry()
+
+
+def get_registry() -> RiderKalmanRegistry:
+    """Get the process-level rider Kalman filter registry."""
+    return _global_registry
+
+
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+# STATELESS COORDINATE SMOOTHER
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+
+def smooth_coordinates(
+    lat: float,
+    lon: float,
+    *,
+    prior_lat: Optional[float] = None,
+    prior_lon: Optional[float] = None,
+    process_noise: float = 1e-4,
+    measurement_noise: float = 0.01,
+) -> Tuple[float, float]:
+    """
+    Stateless single-shot GPS smoother.
+    If a prior is provided, blends the new reading towards it.
+    """
+    f = GPSKalmanFilter(process_noise=process_noise, measurement_noise=measurement_noise)
+    if prior_lat is not None and prior_lon is not None:
+        try:
+            _flat = float(prior_lat)
+            _flon = float(prior_lon)
+            if GPSKalmanFilter._is_valid_coord(_flat, _flon):
+                f.update(_flat, _flon)
+        except (TypeError, ValueError):
+            pass
+    return f.update(lat, lon)
+
+
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+# BATCH SMOOTHERS
+# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
+
+def smooth_rider_locations(riders: list) -> list:
+    """
+    Apply Kalman smoothing to a list of rider dicts in-place using
+    the global per-rider registry (history preserved across calls).
+
+    Reads/writes: latitude, longitude (and currentlat/currentlong if present).
+    Adds: _kalman_smoothed = True on each processed rider.
+    """
+    registry = get_registry()
+    for rider in riders:
+        try:
+            rider_id = (
+                rider.get("userid") or rider.get("riderid") or
+                rider.get("id") or "unknown"
+            )
+            raw_lat = float(rider.get("latitude") or rider.get("currentlat") or 0)
+            raw_lon = float(rider.get("longitude") or rider.get("currentlong") or 0)
+            if raw_lat == 0.0 and raw_lon == 0.0:
+                continue
+            smooth_lat, smooth_lon = registry.update(rider_id, raw_lat, raw_lon)
+            # Cast back to string for Go compatibility
+            s_lat, s_lon = str(round(smooth_lat, 8)), str(round(smooth_lon, 8))
+            rider["latitude"] = s_lat
+            rider["longitude"] = s_lon
+            if "currentlat" in rider:
+                rider["currentlat"] = s_lat
+            if "currentlong" in rider:
+                rider["currentlong"] = s_lon
+            rider["_kalman_smoothed"] = True
+        except Exception as e:
+            logger.debug(f"Kalman rider smoothing skipped: {e}")
+    return riders
+
+
+def smooth_order_coordinates(orders: list) -> list:
+    """
+    Apply stateless Kalman smoothing to delivery coordinates in a list
+    of order dicts. Uses pickup coords as a seed (prior) when available.
+
+    Modifies orders in-place. Returns the same list.
+    """
+    for order in orders:
+        try:
+            dlat = float(order.get("deliverylat") or order.get("droplat") or 0)
+            dlon = float(order.get("deliverylong") or order.get("droplon") or 0)
+            if not GPSKalmanFilter._is_valid_coord(dlat, dlon):
+                continue
+
+            plat_raw = order.get("pickuplat")
+            plon_raw = order.get("pickuplon") or order.get("pickuplong")
+            try:
+                plat = float(plat_raw) if plat_raw else None
+                plon = float(plon_raw) if plon_raw else None
+            except (TypeError, ValueError):
+                plat, plon = None, None
+
+            smooth_dlat, smooth_dlon = smooth_coordinates(
+                dlat, dlon,
+                prior_lat=plat,
+                prior_lon=plon,
+            )
+            # Cast back to string for Go compatibility (fixes unmarshal error)
+            s_lat, s_lon = str(round(smooth_dlat, 8)), str(round(smooth_dlon, 8))
+            order["deliverylat"] = s_lat
+            order["deliverylong"] = s_lon
+            if "droplat" in order:
+                order["droplat"] = s_lat
+            if "droplon" in order:
+                order["droplon"] = s_lon
+            order["_kalman_smoothed"] = True
+        except Exception as e:
+            logger.debug(f"Kalman order smoothing skipped: {e}")
+    return orders

app/services/routing/realistic_eta_calculator.py

@@ -0,0 +1,158 @@
+"""
+Realistic ETA Calculator for Delivery Operations
+
+Accounts for:
+- City traffic conditions
+- Stop time at pickup/delivery
+- Navigation time
+- Parking/finding address time
+- Different speeds for different order types
+"""
+
+import logging
+from typing import Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class RealisticETACalculator:
+    """
+    Calculates realistic ETAs accounting for real-world delivery conditions.
+    """
+    
+    def __init__(self):
+        from app.config.dynamic_config import get_config
+        cfg = get_config()
+
+        # BASE SPEED (km/h) - Driven by the DB configuration
+        base_speed = cfg.get("avg_speed_kmh", 18.0)
+        
+        # REALISTIC SPEEDS based on time of day
+        self.CITY_SPEED_HEAVY_TRAFFIC = base_speed * 0.7  # Usually ~12 km/h
+        self.CITY_SPEED_MODERATE      = base_speed        # Usually ~18 km/h
+        self.CITY_SPEED_LIGHT         = base_speed * 1.2  # Usually ~21.6 km/h
+        
+        # TIME BUFFERS (minutes)
+        self.PICKUP_TIME = cfg.get("eta_pickup_time_min", 3.0)
+        self.DELIVERY_TIME = cfg.get("eta_delivery_time_min", 4.0)
+        self.NAVIGATION_BUFFER = cfg.get("eta_navigation_buffer_min", 1.5)
+        
+        # DISTANCE-BASED SPEED SELECTION
+        # Short distances (<2km) are slower due to more stops/starts
+        # Long distances (>8km) might have highway portions
+        self.SHORT_TRIP_FACTOR = cfg.get("eta_short_trip_factor", 0.8)
+        self.LONG_TRIP_FACTOR  = cfg.get("eta_long_trip_factor", 1.1)
+        
+    def calculate_eta(self, 
+                     distance_km: float, 
+                     is_first_order: bool = False,
+                     order_type: str = "Economy",
+                     time_of_day: str = "peak") -> int:
+        """
+        Calculate realistic ETA in minutes.
+        
+        Args:
+            distance_km: Distance to travel in kilometers
+            is_first_order: If True, includes pickup time
+            order_type: "Economy", "Premium", or "Risky"
+            time_of_day: "peak", "normal", or "light" traffic
+            
+        Returns:
+            ETA in minutes (rounded up for safety)
+        """
+        
+        if distance_km <= 0:
+            return 0
+        
+        # 1. SELECT SPEED BASED ON CONDITIONS
+        if time_of_day == "peak":
+            base_speed = self.CITY_SPEED_HEAVY_TRAFFIC
+        elif time_of_day == "light":
+            base_speed = self.CITY_SPEED_LIGHT
+        else:
+            base_speed = self.CITY_SPEED_MODERATE
+        
+        # 2. ADJUST SPEED BASED ON DISTANCE
+        # Short trips are slower (more intersections, traffic lights)
+        if distance_km < 2.0:
+            effective_speed = base_speed * self.SHORT_TRIP_FACTOR
+        elif distance_km > 8.0:
+            effective_speed = base_speed * self.LONG_TRIP_FACTOR
+        else:
+            effective_speed = base_speed
+        
+        # 3. CALCULATE TRAVEL TIME
+        travel_time = (distance_km / effective_speed) * 60  # Convert to minutes
+        
+        # 4. ADD BUFFERS
+        total_time = travel_time
+        
+        # Pickup time (only for first order in sequence)
+        if is_first_order:
+            total_time += self.PICKUP_TIME
+        
+        # Delivery time (always)
+        total_time += self.DELIVERY_TIME
+        
+        # Navigation buffer (proportional to distance)
+        if distance_km > 3.0:
+            total_time += self.NAVIGATION_BUFFER
+        
+        # 5. SAFETY MARGIN (Round up to next minute)
+        # Riders prefer to arrive early than late
+        eta_minutes = int(total_time) + 1
+        
+        return eta_minutes
+    
+    def calculate_batch_eta(self, orders: list) -> list:
+        """
+        Calculate ETAs for a batch of orders in sequence.
+        
+        Args:
+            orders: List of order dicts with 'previouskms' and 'step' fields
+            
+        Returns:
+            Same list with updated 'eta' fields
+        """
+        for order in orders:
+            distance_km = float(order.get('previouskms', 0))
+            step = order.get('step', 1)
+            order_type = order.get('ordertype', 'Economy')
+            
+            # First order includes pickup time
+            is_first = (step == 1)
+            
+            # Assume peak traffic for safety (can be made dynamic)
+            eta = self.calculate_eta(
+                distance_km=distance_km,
+                is_first_order=is_first,
+                order_type=order_type,
+                time_of_day="normal"  # Default to moderate traffic
+            )
+            
+            order['eta'] = str(eta)
+            order['eta_realistic'] = True  # Flag to indicate realistic calculation
+        
+        return orders
+
+
+def get_time_of_day_category() -> str:
+    """
+    Determine current traffic conditions based on time.
+    
+    Returns:
+        "peak", "normal", or "light"
+    """
+    from datetime import datetime
+    
+    
+    current_hour = datetime.now().hour
+    
+    # Peak hours: 8-10 AM, 12-2 PM, 5-8 PM
+    if (8 <= current_hour < 10) or (12 <= current_hour < 14) or (17 <= current_hour < 20):
+        return "peak"
+    # Light traffic: Late night/early morning
+    elif current_hour < 7 or current_hour >= 22:
+        return "light"
+    else:
+        return "normal"

app/services/routing/route_optimizer.py

@@ -0,0 +1,425 @@
+"""Production-grade route optimization using Google OR-Tools.
+
+ALGORITHM: TSP / VRP with Google OR-Tools
+- Industry-standard solver (same as used by major logistics companies)
+- Constraint-based optimization
+- Handles time windows (future proofing)
+- Guaranteed optimal or near-optimal solution
+
+FEATURES:
+- Automatic outlier detection and coordinate correction
+- Hybrid distance calculation (Google Maps + Haversine fallback)
+- Robust error handling for invalid inputs
+"""
+
+import math
+import os
+import logging
+import asyncio
+from typing import Dict, Any, List as _List, Optional, Tuple, Union
+from datetime import datetime, timedelta
+import httpx
+from app.services.routing.kalman_filter import smooth_order_coordinates
+import numpy as np
+from app.core.arrow_utils import calculate_haversine_matrix_vectorized
+from app.config.dynamic_config import get_config
+
+try:
+    from ortools.constraint_solver import routing_enums_pb2
+    from ortools.constraint_solver import pywrapcp
+    ORTOOLS_AVAILABLE = True
+except ImportError:
+    ORTOOLS_AVAILABLE = False
+    logging.warning("Google OR-Tools not found. Falling back to simple greedy solver.")
+
+logger = logging.getLogger(__name__)
+
+
+class RouteOptimizer:
+    """Route optimization using Google OR-Tools (Async)."""
+    
+    def __init__(self):
+        self.earth_radius = 6371  # Earth radius in km
+        _cfg = get_config()
+
+        # Initialize Realistic ETA Calculator
+        from app.services.routing.realistic_eta_calculator import RealisticETACalculator, get_time_of_day_category
+        self.eta_calculator = RealisticETACalculator()
+        self.get_traffic_condition = get_time_of_day_category
+
+        # Speed settings (ML-tuned via DynamicConfig)
+        self.avg_speed_kmh = float(_cfg.get("avg_speed_kmh"))
+
+        # Road factor (haversine -> road distance multiplier, ML-tuned)
+        self.road_factor = float(_cfg.get("road_factor"))
+
+        # Google Maps API settings
+        self.google_maps_api_key = os.getenv("GOOGLE_MAPS_API_KEY", "")
+        self.use_google_maps = bool(self.google_maps_api_key)
+
+        # Solver time limit (ML-tuned)
+        self.search_time_limit_seconds = int(_cfg.get("search_time_limit_seconds"))
+
+        # Initialize ID3 Behavior Analyzer
+        from app.services.ml.behavior_analyzer import get_analyzer
+        self.behavior_analyzer = get_analyzer()
+
+    def haversine_distance(self, lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+        """Calculate great circle distance between two points on Earth (in km)."""
+        try:
+            lat1, lon1, lat2, lon2 = map(math.radians, [float(lat1), float(lon1), float(lat2), float(lon2)])
+            dlat = lat2 - lat1
+            dlon = lon2 - lon1
+            a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2
+            c = 2 * math.asin(math.sqrt(a))
+            return self.earth_radius * c
+        except Exception:
+            return 0.0
+    
+    async def _get_google_maps_distances_batch(self, origin_lat: float, origin_lon: float,
+                                         destinations: _List[tuple]) -> Dict[tuple, float]:
+        """Get road distances for multiple destinations from Google Maps API. (Async, Parallel)"""
+        if not self.use_google_maps or not destinations:
+            return {}
+        
+        results = {}
+        batch_size = 25
+        chunks = [destinations[i:i + batch_size] for i in range(0, len(destinations), batch_size)]
+        
+        async def process_batch(batch):
+            batch_result = {}
+            try:
+                dest_str = "|".join([f"{lat},{lon}" for lat, lon in batch])
+                url = "https://maps.googleapis.com/maps/api/distancematrix/json"
+                params = {
+                    "origins": f"{origin_lat},{origin_lon}",
+                    "destinations": dest_str,
+                    "key": self.google_maps_api_key,
+                    "units": "metric"
+                }
+                async with httpx.AsyncClient(timeout=10.0) as client:
+                    response = await client.get(url, params=params)
+                    response.raise_for_status()
+                    data = response.json()
+                    
+                    if data.get("status") == "OK":
+                        rows = data.get("rows", [])
+                        if rows:
+                            elements = rows[0].get("elements", [])
+                            for idx, element in enumerate(elements):
+                                if idx < len(batch):
+                                    dest_coord = batch[idx]
+                                    if element.get("status") == "OK":
+                                        dist = element.get("distance", {}).get("value")
+                                        dur = element.get("duration", {}).get("value")
+                                        if dist is not None:
+                                            batch_result[dest_coord] = {
+                                                'distance': dist / 1000.0,
+                                                'duration': dur / 60.0 if dur else None
+                                            }
+            except Exception as e:
+                logger.warning(f"Google Maps batch call failed: {e}")
+            return batch_result
+
+        batch_results_list = await asyncio.gather(*[process_batch(chunk) for chunk in chunks])
+        for res in batch_results_list:
+            results.update(res)
+        return results
+
+    def _solve_tsp_ortools(self, locations: _List[Tuple[float, float]], dist_matrix: _List[_List[float]]) -> _List[int]:
+        """Solve TSP using Google OR-Tools."""
+        if not ORTOOLS_AVAILABLE:
+            # Fallback to simple Greedy NN if OR-Tools not installed
+            return self._solve_greedy(locations, dist_matrix)
+
+        if not locations or len(locations) <= 1:
+            return [0]
+            
+        manager = pywrapcp.RoutingIndexManager(len(locations), 1, 0) # num_nodes, num_vehicles, depot
+        routing = pywrapcp.RoutingModel(manager)
+
+        def distance_callback(from_index, to_index):
+            from_node = manager.IndexToNode(from_index)
+            to_node = manager.IndexToNode(to_index)
+            
+            # Open TSP: Returning to the depot (index 0) has zero cost.
+            # This ensures the solver optimizes for the path from start to last drop-off
+            # rather than a closed circuit that might be reversed if the rider is on the "far" side.
+            if to_node == 0:
+                return 0
+                
+            # OR-Tools works with integers, so we scale by 1000 (meters)
+            val = dist_matrix[from_node][to_node]
+            return int(val * 1000)
+
+        transit_callback_index = routing.RegisterTransitCallback(distance_callback)
+        routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
+
+        search_parameters = pywrapcp.DefaultRoutingSearchParameters()
+        search_parameters.first_solution_strategy = (
+            routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
+        )
+        search_parameters.local_search_metaheuristic = (
+            routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
+        )
+        search_parameters.time_limit.seconds = self.search_time_limit_seconds
+
+        solution = routing.SolveWithParameters(search_parameters)
+
+        if solution:
+            index = routing.Start(0)
+            route = []
+            while not routing.IsEnd(index):
+                route.append(manager.IndexToNode(index))
+                index = solution.Value(routing.NextVar(index))
+            return route
+        else:
+            return self._solve_greedy(locations, dist_matrix)
+
+    def _solve_greedy(self, locations, dist_matrix):
+        """Simple Greedy Nearest Neighbor fallback."""
+        unvisited = set(range(1, len(locations)))
+        curr = 0
+        route = [0]
+        while unvisited:
+            nearest = min(unvisited, key=lambda x: dist_matrix[curr][x])
+            route.append(nearest)
+            unvisited.remove(nearest)
+            curr = nearest
+        return route
+
+    def _cleanup_coords(self, lat: Any, lon: Any, ref_lat: float, ref_lon: float) -> Tuple[float, float]:
+        """
+        Heuristic to fix bad coordinates.
+        1. Fixes lat==lon typo.
+        2. Fixes missing negative signs if needed (not needed for India).
+        3. Projects outlier > 500km to reference (centroid).
+        """
+        try:
+            lat = float(lat)
+            lon = float(lon)
+        except:
+            return 0.0, 0.0
+
+        if lat == 0 or lon == 0:
+            return lat, lon
+
+        # 1. Check strict equality (typo)
+        if abs(lat - lon) < 0.0001:
+            if ref_lon != 0:
+                # If reference is available, assume lat is correct and fix lon
+                # (Common error: copy lat to lon field)
+                return lat, ref_lon
+        
+        # 2. Check general outlier (e.g. 500km away)
+        if ref_lat != 0 and ref_lon != 0:
+            dist = self.haversine_distance(lat, lon, ref_lat, ref_lon)
+            if dist > 500:
+                # Returning reference prevents map explosion
+                return ref_lat, ref_lon
+                
+        return lat, lon
+
+    async def optimize_provider_payload(self, orders: _List[Dict[str, Any]], start_coords: Optional[tuple] = None) -> _List[Dict[str, Any]]:
+        """Optimize delivery route and add step metrics (OR-Tools)."""
+        if not orders:
+            return []
+        
+        # Deep copy
+        orders = [dict(order) for order in orders]
+
+        # 0. KALMAN FILTER - Smooth noisy delivery GPS coordinates
+        orders = smooth_order_coordinates(orders)
+
+        # Helpers
+        def _to_float(v: Any) -> float:
+            try: return float(v)
+            except: return 0.0
+
+        def _normalize_dt(val: Any) -> str:
+            if val in (None, "", 0): return ""
+            s = str(val).strip()
+            for fmt in ("%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%d %H:%M:%S"):
+                try: return datetime.strptime(s, fmt).strftime("%Y-%m-%d %H:%M:%S")
+                except: pass
+            return s
+
+        # 1. PREPARE COORDINATES & CENTROID
+        valid_lats = []
+        valid_lons = []
+        
+        for o in orders:
+            lat = _to_float(o.get("deliverylat"))
+            lon = _to_float(o.get("deliverylong"))
+            if lat != 0 and lon != 0:
+                valid_lats.append(lat)
+                valid_lons.append(lon)
+        
+        centroid_lat = sum(valid_lats)/len(valid_lats) if valid_lats else 0.0
+        centroid_lon = sum(valid_lons)/len(valid_lons) if valid_lons else 0.0
+        
+        # 2. DETERMINE START LOCATION (With Fix)
+        start_lat, start_lon = 0.0, 0.0
+        
+        # Try explicit start_coords first
+        if start_coords and len(start_coords) == 2:
+             try:
+                 start_lat, start_lon = float(start_coords[0]), float(start_coords[1])
+             except: pass
+             
+        # Fallback to pickup location in orders
+        if start_lat == 0:
+            for o in orders:
+                plat = _to_float(o.get("pickuplat"))
+                plon = _to_float(o.get("pickuplon") or o.get("pickuplong"))
+                if plat != 0:
+                    start_lat, start_lon = plat, plon
+                    break
+        
+        # Fallback to centroid
+        if start_lat == 0:
+            start_lat, start_lon = centroid_lat, centroid_lon
+            
+        # FIX BAD START COORDINATES
+        start_lat, start_lon = self._cleanup_coords(start_lat, start_lon, centroid_lat, centroid_lon)
+
+        # 3. BUILD LOCATIONS LIST FOR SOLVER
+        # Index 0 is Start (Depot), 1..N are orders
+        locations = [(start_lat, start_lon)]
+        points_map = [] # Maps solver index 1..N back to original order index
+        
+        for idx, order in enumerate(orders):
+            lat = _to_float(order.get("deliverylat"))
+            lon = _to_float(order.get("deliverylong"))
+            
+            # Project coordinates and ensure they are strings for Go compatibility
+            lat, lon = self._cleanup_coords(lat, lon, centroid_lat, centroid_lon)
+            order_str_lat, order_str_lon = str(lat), str(lon)
+            
+            order["deliverylat"] = order_str_lat
+            order["deliverylong"] = order_str_lon
+            if "droplat" in order: order["droplat"] = order_str_lat
+            if "droplon" in order: order["droplon"] = order_str_lon
+            
+            locations.append((lat, lon))
+            points_map.append(idx)
+            
+        # 4. COMPUTE DISTANCE MATRIX (Vectorized with Arrow/NumPy)
+        # road_factor is now ML-tuned (was hardcoded 1.3)
+        lats = np.array([loc[0] for loc in locations])
+        lons = np.array([loc[1] for loc in locations])
+        dist_matrix = calculate_haversine_matrix_vectorized(lats, lons) * self.road_factor
+        
+        # 5. RISK-AWARE COST MATRIX (ID3 INTELLIGENCE)
+        # Apply Risk Penalties to the matrix before solving
+        cost_matrix = dist_matrix.copy()
+        traffic = self.get_traffic_condition()
+        
+        num_locs = len(locations)
+        risk_penalty_count = 0
+        
+        for i in range(num_locs):
+            for j in range(num_locs):
+                if i == j: continue
+                # Predict success risk for this specific leg
+                dist_km = dist_matrix[i][j]
+                prediction = self.behavior_analyzer.predict(
+                    distance_km=dist_km,
+                    timestamp_or_band=traffic,
+                )
+                
+                if prediction.get("label") == "RISK":  # High Risk predicted by ID3
+                    # Add 25% penalty to distance to discourage this leg
+                    cost_matrix[i][j] *= 1.25
+                    risk_penalty_count += 1
+        
+        if risk_penalty_count > 0:
+            logger.info(f"ID3 Intelligence: Applied {risk_penalty_count} Risk Penalties to optimize for delivery safety.")
+
+        # 6. SOLVE TSP
+        route_indices = self._solve_tsp_ortools(locations, cost_matrix)
+        
+        # Remove 0 (depot)
+        optimized_order_indices = [i for i in route_indices if i != 0]
+        
+        # 6. BUILD RESULT
+        result = []
+        cumulative_dist = 0.0
+        
+        # Track previous location (starts at 0)
+        prev_idx = 0
+        
+        for step_num, solver_idx in enumerate(optimized_order_indices, start=1):
+            order_idx = points_map[solver_idx - 1]
+            order = dict(orders[order_idx])
+            
+            # Clean fields
+            for k in ("step", "previouskms", "cumulativekms", "eta", "actualkms", "ordertype"): 
+                order.pop(k, None)
+            
+            # Normalize dates
+            for field in ["orderdate", "deliverytime", "created"]:
+                if field in order: order[field] = _normalize_dt(order.get(field))
+            
+            # Distance for this leg
+            step_dist = dist_matrix[prev_idx][solver_idx]
+            cumulative_dist += step_dist
+            
+            # Metadata (Step metrics are integers in the Go struct)
+            order["step"] = int(step_num)
+            order["previouskms"] = int(0 if step_num == 1 else int(round(step_dist)))
+            order["cumulativekms"] = int(round(cumulative_dist))
+            
+            # 7. METRICS (Calculate actual distance, prioritize provider input)
+            plat, plon = start_lat, start_lon
+            if plat == 0: plat, plon = _to_float(order.get("pickuplat")), _to_float(order.get("pickuplon") or order.get("pickuplong"))
+            dlat, dlon = locations[solver_idx]
+            
+            # Baseline: Haversine * 1.3 (estimated road factor)
+            true_dist = self.haversine_distance(plat, plon, dlat, dlon) * 1.3
+            
+            provided_kms = order.get("kms")
+            if provided_kms not in (None, "", 0, "0"):
+                try:
+                    # If provider gave us a distance, respect it as the 'actual' distance
+                    true_dist = float(provided_kms)
+                except:
+                    pass
+            
+            order["actualkms"] = str(round(true_dist, 2))
+            order["kms"] = str(provided_kms) if provided_kms else str(int(round(true_dist)))
+            
+            # Financial metrics - keeping as numbers for calculations
+            if "rider_charge" in order: order["rider_charge"] = round(float(order["rider_charge"]), 2)
+            if "profit" in order: order["profit"] = round(float(order["profit"]), 2)
+            
+            # Type & ETA
+            order["ordertype"] = "Economy" if true_dist <= 5 else "Premium" if true_dist <= 12 else "Risky"
+            
+            traffic = self.get_traffic_condition()
+            eta = self.eta_calculator.calculate_eta(
+                distance_km=step_dist,
+                is_first_order=(step_num == 1),
+                order_type=order["ordertype"],
+                time_of_day=traffic
+            )
+            order["eta"] = str(eta)
+            
+            result.append(order)
+            prev_idx = solver_idx
+            
+        return result
+
+def optimize_route(orders: _List[Dict[str, Any]]) -> _List[Dict[str, Any]]:
+    """Synchronous wrapper."""
+    optimizer = RouteOptimizer()
+    try:
+        loop = asyncio.get_event_loop()
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+    if loop.is_running():
+        # Fallback if loop is running (shouldn't happen in standard usage)
+        return [] 
+    return loop.run_until_complete(optimizer.optimize_provider_payload(orders))

app/services/routing/zone_service.py

@@ -0,0 +1,196 @@
+
+import logging
+from typing import List, Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+class ZoneService:
+    """
+    Service to classify orders and riders into geographic zones.
+    Defaulting to Coimbatore logic as per user context.
+    """
+    
+    # Approximate Center of Coimbatore (Gandhipuram/Bus Stand area)
+    CENTER_LAT = 11.0168
+    CENTER_LON = 76.9558
+    
+    def __init__(self):
+        pass
+
+    def determine_zone(self, lat: float, lon: float, pincode: Optional[str] = None) -> str:
+        """
+        Determine the zone (North, South, East, West, etc.) based on coordinates.
+        """
+        if lat == 0 or lon == 0:
+            return "Unknown"
+
+        lat_diff = lat - self.CENTER_LAT
+        lon_diff = lon - self.CENTER_LON
+        
+        # Simple Quadrant Logic
+        # North: +Lat
+        # South: -Lat
+        # East: +Lon
+        # West: -Lon
+        
+        # Define a small central buffer (0.01 degrees ~ 1.1km)
+        buffer = 0.010
+        
+        is_north = lat_diff > buffer
+        is_south = lat_diff < -buffer
+        is_east = lon_diff > buffer
+        is_west = lon_diff < -buffer
+        
+        zone_parts = []
+        
+        if is_north: zone_parts.append("North")
+        elif is_south: zone_parts.append("South")
+        
+        if is_east: zone_parts.append("East")
+        elif is_west: zone_parts.append("West")
+        
+        if not zone_parts:
+            return "Central"
+            
+        return " ".join(zone_parts)
+
+    def group_by_zones(self, flat_orders: List[Dict[str, Any]], unassigned_orders: List[Dict[str, Any]] = None, fuel_charge: float = 2.5, base_pay: float = 30.0) -> Dict[str, Any]:
+        """
+        Group a flat list of optimized orders into Zones -> Riders -> Orders.
+        Calculates profit per order and per zone.
+        """
+        zones_map = {} # "North East": { "riders": { rider_id: [orders] } }
+        unassigned_orders = unassigned_orders or []
+        
+        # Merge both for initial processing if you want everything zoned
+        all_to_process = []
+        for o in flat_orders:
+            all_to_process.append((o, True))
+        for o in unassigned_orders:
+            all_to_process.append((o, False))
+
+        for order, is_assigned in all_to_process:
+            # 1. Extract Coords
+            try:
+                # Prefer Delivery location for zoning (where the customer is)
+                lat = float(order.get("deliverylat") or order.get("droplat") or 0)
+                lon = float(order.get("deliverylong") or order.get("droplon") or 0)
+                pincode = str(order.get("deliveryzip") or "")
+            except:
+                lat, lon, pincode = 0, 0, ""
+            
+            # 2. Get Zone
+            zone_name = self.determine_zone(lat, lon, pincode)
+            order["zone_name"] = zone_name
+            
+            # 3. Initialize Zone Bucket
+            if zone_name not in zones_map:
+                zones_map[zone_name] = {
+                    "riders_map": {}, 
+                    "total_orders": 0,
+                    "assigned_orders": 0,
+                    "unassigned_orders": [],
+                    "total_kms": 0.0,
+                    "total_profit": 0.0
+                }
+            
+            # 4. Add to Rider bucket within Zone
+            rider_id = order.get("userid") or order.get("_id")
+            
+            # Track kms and profit for this zone
+            try:
+                # 'actualkms' is preferred for delivery distance
+                dist = float(order.get("actualkms", order.get("previouskms", 0)))
+                zones_map[zone_name]["total_kms"] += dist
+                
+                # Individual charge for this order: Fixed Base + Variable Distance
+                order_amount = float(order.get("orderamount") or order.get("deliveryamount") or 0)
+                rider_payment = base_pay + (dist * fuel_charge)
+                profit = order_amount - rider_payment
+                
+                order["rider_charge"] = round(rider_payment, 2)
+                order["profit"] = round(profit, 2)
+                
+                # Profit-based classification (Order Type)
+                if profit <= 0:
+                    order["ordertype"] = "Loss"
+                elif profit <= 5:
+                    order["ordertype"] = "Risky"
+                elif profit <= 10:
+                    order["ordertype"] = "Economy"
+                else:
+                    order["ordertype"] = "Premium"
+                
+                zones_map[zone_name]["total_profit"] += profit
+            except:
+                pass
+
+            # If strictly unassigned order (no rider), put in unassigned
+            if not is_assigned:
+                zones_map[zone_name]["unassigned_orders"].append(order)
+            else:
+                str_rid = str(rider_id)
+                if str_rid not in zones_map[zone_name]["riders_map"]:
+                    zones_map[zone_name]["riders_map"][str_rid] = {
+                        "rider_details": {
+                            "id": str_rid,
+                            "name": order.get("username", "Unknown")
+                        },
+                        "orders": []
+                    }
+                zones_map[zone_name]["riders_map"][str_rid]["orders"].append(order)
+                zones_map[zone_name]["assigned_orders"] += 1
+            
+            zones_map[zone_name]["total_orders"] += 1
+
+        # 5. Restructure for API Response
+        output_zones = []
+        zone_metrics = []
+        
+        sorted_zone_names = sorted(zones_map.keys())
+        
+        for z_name in sorted_zone_names:
+            z_data = zones_map[z_name]
+            
+            # Flatten riders map
+            riders_list = []
+            for r_id, r_data in z_data["riders_map"].items():
+                riders_list.append({
+                    "rider_id": r_data["rider_details"]["id"],
+                    "rider_name": r_data["rider_details"]["name"],
+                    "orders_count": len(r_data["orders"]),
+                    "orders": r_data["orders"]
+                })
+            
+            # Create the flat metric summary
+            metrics = {
+                "zone_name": z_name,
+                "total_orders": z_data["total_orders"],
+                "assigned_orders": z_data["assigned_orders"],
+                "unassigned_orders_count": len(z_data["unassigned_orders"]),
+                "active_riders_count": len(riders_list),
+                "total_delivery_kms": round(z_data["total_kms"], 2),
+                "total_profit": round(z_data["total_profit"], 2)
+            }
+            
+            zone_metrics.append(metrics)
+            
+            # Create the detailed zone object with flattened metrics
+            zone_obj = {
+                "zone_name": z_name,
+                "total_orders": metrics["total_orders"],
+                "active_riders_count": metrics["active_riders_count"],
+                "assigned_orders": metrics["assigned_orders"],
+                "unassigned_orders_count": metrics["unassigned_orders_count"],
+                "total_delivery_kms": metrics["total_delivery_kms"],
+                "total_profit": metrics["total_profit"],
+                "riders": riders_list,
+                "unassigned_orders": z_data["unassigned_orders"]
+            }
+            
+            output_zones.append(zone_obj)
+            
+        return {
+            "detailed_zones": output_zones,
+            "zone_analysis": zone_metrics
+        }

docker-compose.yml

@@ -0,0 +1,36 @@
+version: "3.9"
+
+networks:
+  frontend:
+    external: true
+
+services:
+  routes_api:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: routes-api:latest
+    container_name: routes_api
+    restart: unless-stopped
+    environment:
+      - UVICORN_WORKERS=2
+      - REDIS_URL=redis://:${REDIS_PASSWORD}@routes_redis:6379/0
+      # Optional: Set cache TTL in seconds (default: 300 = 5 min, 86400 = 24h)
+      # Uncomment and set in .env file: REDIS_CACHE_TTL_SECONDS=86400
+      # - REDIS_CACHE_TTL_SECONDS=${REDIS_CACHE_TTL_SECONDS}
+      # Google Maps API key for accurate road distance calculation (actualkms)
+      # Set in .env file: GOOGLE_MAPS_API_KEY=your_api_key_here
+      - GOOGLE_MAPS_API_KEY=${GOOGLE_MAPS_API_KEY}
+    labels:
+      - traefik.enable=true
+      - traefik.http.routers.routes_api.rule=Host(`routes.workolik.com`)
+      - traefik.http.routers.routes_api.entrypoints=websecure
+      - traefik.http.routers.routes_api.tls.certresolver=letsencrypt
+      - traefik.http.services.routes_api.loadbalancer.server.port=8002
+      - traefik.docker.network=frontend
+    volumes:
+      - ./ml_data:/app/ml_data
+      - ./rider_history.pkl:/app/rider_history.pkl
+      - ./rider_active_state.pkl:/app/rider_active_state.pkl
+    networks:
+      - frontend

docker-entrypoint.sh

@@ -0,0 +1,11 @@
+#!/bin/sh
+set -e
+
+# Get number of workers from environment or default to 1
+WORKERS=${UVICORN_WORKERS:-1}
+
+echo "Starting Route Optimization API with ${WORKERS} worker(s)..."
+
+# Start uvicorn
+exec uvicorn app.main:app --host 0.0.0.0 --port 8002 --workers ${WORKERS}
+

requirements.txt

@@ -0,0 +1,18 @@
+fastapi
+uvicorn
+python-dotenv
+requests
+numpy
+pandas
+scikit-learn
+scipy
+openpyxl
+xlsxwriter
+httpx
+ortools
+pyarrow
+# ML Hypertuning
+xgboost>=2.0.0
+optuna>=3.5.0
+sqlalchemy>=2.0.0
+apscheduler>=3.10.0

run_simulation.py

@@ -0,0 +1,173 @@
+import json
+import logging
+import asyncio
+from app.services.core.assignment_service import AssignmentService
+from app.services.routing.route_optimizer import RouteOptimizer
+from app.core.arrow_utils import save_optimized_route_parquet
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load Environment Variables
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+    print("✅ Loaded .env file")
+except ImportError:
+    print("⚠️ python-dotenv not installed, skipping .env load")
+
+async def run_simulation():
+    print("🚀 Starting Logic Simulation (High Efficiency Mode + K-wMeans)...")
+    
+    # 1. Load Orders (using route.json as source)
+    try:
+        with open('route.json', 'r') as f:
+            route_data = json.load(f)
+    except FileNotFoundError:
+        print("❌ route.json not found.")
+        return
+    
+    raw_orders = route_data.get('details', [])
+    # Strip assignment data to simulate fresh orders
+    clean_orders = []
+    for o in raw_orders:
+        o_copy = o.copy()
+        for key in ['userid', 'step', 'cumulativekms', 'eta']:
+            o_copy.pop(key, None)
+        clean_orders.append(o_copy)
+        
+    print(f"📦 Loaded {len(clean_orders)} orders.")
+
+    # 2. Mock Riders
+    # Using the 5 rider fleet as agreed
+    rider_ids = [753, 883, 1114, 1271, 1116, 1096, 897, 950, 1272, 1133] # Full Active Riders List
+    # Rider Starting Locations (Based on "Mostly Available Location")
+    # Coordinates approximated for Coimbatore areas
+    rider_locations = {
+        1116: (11.0067, 76.9558), # VIVEK ANANDAN: RS PURAM
+        1096: (11.0450, 76.9000), # NARAYANASAMY: VADAVALI
+        897:  (11.0430, 76.9380), # VARUN EDWARD: KAVUNDAMPALAYAM
+        950:  (11.0330, 76.9800), # JAYASABESH: GANAPATHY
+        1114: (11.0450, 77.0000), # TAMILAZHAGAN: GANDHIMA NAGAR
+        883:  (11.0200, 77.0000), # RAJAN: PEELAMEDU
+        1272: (10.9950, 77.0000), # MUTHURAJA: RAMANATHAPURAM
+        753:  (11.0000, 77.0300), # MANIKANDAN: SINGANALLUR
+        1133: (11.0067, 76.9558), # THATCHINAMOORTHI: RS PURAM (Covering Kavundampalayam to Kovaipudur)
+        1271: (11.0067, 76.9558)  # Legacy ID for Thatchinamoorthi
+    }
+
+    riders = []
+    for i, rid in enumerate(rider_ids):
+        lat, lon = rider_locations.get(rid, (11.0168, 76.9558)) # Default to Central if unknown
+        riders.append({
+            "userid": rid,
+            "status": "idle",
+            "onduty": 1,
+            "latitude": str(lat),
+            "longitude": str(lon)
+        })
+
+    # 3. Run Assignment
+    assignment_service = AssignmentService()
+    try:
+        assignments, unassigned_orders = assignment_service.assign_orders(clean_orders, riders)
+    except Exception as e:
+        print(f"❌ Error during assignment: {e}")
+        import traceback
+        traceback.print_exc()
+        return
+
+    # 4. Generate Output (Mirroring API Logic)
+    optimizer = RouteOptimizer()
+    
+    output_details = []
+    distribution = {}
+    assigned_count = 0
+    
+    # Prepare async tasks
+    tasks = []
+    task_rids = []
+    
+    for rid, orders in assignments.items():
+        if not orders: continue
+        distribution[rid] = len(orders)
+        assigned_count += len(orders)
+        
+        # Optimize Route & Add Metrics (Cumulative KMS, Step, etc.)
+        mock_rider = next((r for r in riders if r["userid"] == rid), None)
+        start_coords = None
+        if mock_rider:
+            start_coords = (float(mock_rider['latitude']), float(mock_rider['longitude']))
+
+        tasks.append(optimizer.optimize_provider_payload(orders, start_coords=start_coords))
+        task_rids.append(rid)
+
+    # Run tasks
+    if tasks:
+        results = await asyncio.gather(*tasks)
+        
+        for rid, optimized_route in zip(task_rids, results):
+            mock_rider = next((r for r in riders if r["userid"] == rid), {})
+            r_name = mock_rider.get("username", "")
+            r_contact = mock_rider.get("contactno", "")
+            
+            total_kms = 0
+            if optimized_route:
+                try:
+                    total_kms = max([float(o.get("cumulativekms", 0)) for o in optimized_route])
+                except:
+                    total_kms = sum([float(o.get("actualkms", o.get("kms", 0))) for o in optimized_route])
+
+            for o in optimized_route:
+                o['userid'] = rid
+                o['username'] = r_name
+                o['rider'] = r_name
+                o['ridercontactno'] = r_contact
+                o['riderkms'] = str(round(total_kms, 2))
+                output_details.append(o)
+
+    # 5. Zone Processing
+    fuel_charge = 2.5
+    base_pay = 30.0
+    from app.services.routing.zone_service import ZoneService
+    zone_service = ZoneService()
+    zone_data = zone_service.group_by_zones(output_details, unassigned_orders, fuel_charge=fuel_charge, base_pay=base_pay)
+    
+    # 6. Save output.json
+    output_data = {
+        "message": "Success",
+        "status": True,
+        "details": output_details,
+        "zone_summary": zone_data["zone_analysis"],
+        "zones": zone_data["detailed_zones"],
+        "meta": {
+            "total_orders": len(clean_orders),
+            "total_riders": len(rider_ids),
+            "assigned_orders": assigned_count,
+            "unassigned_orders": len(unassigned_orders),
+            "total_profit": round(sum(z["total_profit"] for z in zone_data["zone_analysis"]), 2),
+            "unassigned_details": [
+                {"id": o.get("orderid") or o.get("_id"), "reason": o.get("unassigned_reason")}
+                for o in unassigned_orders
+            ],
+            "distribution_summary": distribution
+        }
+    }
+    
+    with open('output.json', 'w') as f:
+        json.dump(output_data, f, indent=4)
+        
+    # Apache Arrow / Parquet Export
+    try:
+        save_optimized_route_parquet(output_details, 'output.parquet')
+        print("📊 Also saved results to output.parquet (Apache Arrow format)")
+    except Exception as e:
+        print(f"⚠️ Could not save Parquet: {e}")
+        
+    print("✅ Simulation Complete. Saved to output.json")
+    print("📊 Distribution Summary:")
+    print(json.dumps(distribution, indent=4))
+
+if __name__ == "__main__":
+    asyncio.run(run_simulation())

start.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+"""Mobile-optimized startup script for the Delivery Route Optimization API."""
+
+import uvicorn
+
+def main():
+    """Start the mobile-optimized API server."""
+    print("📱 Starting Mobile Delivery Route Optimization API...")
+    print("⚡ Optimized for real-time mobile apps")
+    print("🎯 Default algorithm: GREEDY (ultra-fast)")
+    print("📚 Documentation: http://localhost:8002/docs")
+    print("=" * 60)
+    
+    uvicorn.run(
+        "app.main:app", 
+        host="0.0.0.0", 
+        port=8002, 
+        reload=True,
+        access_log=True,
+        log_level="info"
+    )
+
+if __name__ == "__main__":
+    main()