From 0c3e23fac44646fe36c6d3b330174a0f254df3f0 Mon Sep 17 00:00:00 2001 From: sriram Date: Fri, 4 Sep 2026 12:06:40 +0530 Subject: [PATCH] Health score updates in backend --- app/api/batch_common.py | 8 + app/api/nutrition_schemas.py | 53 + app/api/routers/nutrition.py | 144 +- app/api/routers/nutrition_admin.py | 42 +- app/api/routers/upload.py | 89 +- app/api/routers/user_products.py | 10 + app/core/batch_ingest.py | 27 + app/infrastructure/settings.py | 30 + app/services/category_registry.py | 30 +- app/services/consumability.py | 382 + app/services/data/produce_reference.json | 1221 + app/services/data/usda_snapshot.json | 53605 +++++++++++++++++ app/services/image_search.py | 75 +- app/services/nutrition_autoenrich.py | 119 + app/services/nutrition_data_service.py | 81 +- app/services/nutrition_db.py | 190 + app/services/nutrition_enrichment_service.py | 103 +- app/services/nutrition_usda_service.py | 308 + app/services/produce_reference.py | 140 + scripts/build_produce_contact_sheet.py | 428 + scripts/enrich_nutrition.py | 23 +- scripts/purge_non_consumable_nutrition.py | 491 + scripts/refresh_usda_snapshot.py | 207 + scripts/repair_brand_images.py | 41 +- tests/conftest.py | 13 + tests/test_auto_enrich_on_upload.py | 295 + tests/test_consumability.py | 271 + tests/test_health_scores_api.py | 340 + tests/test_nutrition_consumable_gate.py | 199 + tests/test_nutrition_usda.py | 290 + tests/test_produce_image_search.py | 163 + tests/test_upload_data_integrity.py | 84 +- tests/test_upload_nutrition_scoring.py | 295 + 33 files changed, 59703 insertions(+), 94 deletions(-) create mode 100644 app/services/consumability.py create mode 100644 app/services/data/produce_reference.json create mode 100644 app/services/data/usda_snapshot.json create mode 100644 app/services/nutrition_autoenrich.py create mode 100644 app/services/nutrition_usda_service.py create mode 100644 app/services/produce_reference.py create mode 100644 scripts/build_produce_contact_sheet.py create mode 100644 scripts/purge_non_consumable_nutrition.py create mode 100644 scripts/refresh_usda_snapshot.py create mode 100644 tests/test_auto_enrich_on_upload.py create mode 100644 tests/test_consumability.py create mode 100644 tests/test_health_scores_api.py create mode 100644 tests/test_nutrition_consumable_gate.py create mode 100644 tests/test_nutrition_usda.py create mode 100644 tests/test_produce_image_search.py create mode 100644 tests/test_upload_nutrition_scoring.py diff --git a/app/api/batch_common.py b/app/api/batch_common.py index 6674dbd..27422ac 100644 --- a/app/api/batch_common.py +++ b/app/api/batch_common.py @@ -245,6 +245,14 @@ class BatchOut(BaseModel): # sits queued until the orchestrator picks it up, which the UI has to be # able to say out loud rather than showing a run that looks stuck. runner: str = batch_ingest.RUNNER_INPROCESS + # The nutrition-enrichment job queued when this batch finished, pollable at + # GET /api/admin/nutrition-intelligence/jobs/{job_id}. Surfaced here so the + # poll a client is already doing for the ingestion also reveals the scoring + # that follows it, instead of leaving the client to guess that it happened. + # + # None while the batch is still running, when AUTO_ENRICH_ON_UPLOAD is off, + # and for every batch that finished before this existed. + nutrition_job_id: Optional[str] = None # The 11 stage names, in order, so a client can draw the whole pipeline # before a file has entered any of it. Served rather than duplicated in the # frontend so the two cannot drift when a stage is added. diff --git a/app/api/nutrition_schemas.py b/app/api/nutrition_schemas.py index c1f4645..1c624fb 100644 --- a/app/api/nutrition_schemas.py +++ b/app/api/nutrition_schemas.py @@ -128,3 +128,56 @@ class NutritionEnrichmentJobOut(BaseModel): unavailable: Optional[int] = None duration_seconds: Optional[float] = None error: Optional[str] = None + + +# --------------------------------------------------------------------------- +# Health-score listing (GET /api/nutrition/health-scores) +# --------------------------------------------------------------------------- + +class HealthScoreItemOut(BaseModel): + """One scored consumable product. + + `health_score` is REQUIRED here, against this module's all-Optional house + style, and that is the point: the query filters on `health_score IS NOT + NULL`, so a null arriving in this model means the query lost its guarantee. + Declaring it required is what turns that into a loud failure instead of a + blank cell in whatever dashboard is reading this. + """ + brand: str + image_id: str + product_name: Optional[str] = None + category: Optional[str] = None + + health_score: float + nutrition_score: Optional[float] = None + health_band: str # derived from health_score; see _health_band() + scoring_version: Optional[str] = None + + # Provenance travels with the number. A score is only as trustworthy as the + # source it was computed from, and the consumer should be able to show that. + data_status: Optional[str] = None + data_source: Optional[str] = None + source_url: Optional[str] = None + + calories_kcal: Optional[float] = None + protein_g: Optional[float] = None + dietary_fiber_g: Optional[float] = None + total_sugar_g: Optional[float] = None + sodium_mg: Optional[float] = None + + diet_tags: Optional[List[str]] = None + allergens: Optional[List[str]] = None + + model_config = {"extra": "ignore"} + + +class HealthScoreListOut(BaseModel): + """Envelope matching the catalogue convention in `schemas.ProductListOut` + rather than the bare-array convention of the older nutrition list + endpoints - `total` is the whole reason this endpoint exists, since without + it a client paging a thousand products cannot tell when it has finished.""" + total: int + limit: int + offset: int + generated_at: str + items: List[HealthScoreItemOut] = [] diff --git a/app/api/routers/nutrition.py b/app/api/routers/nutrition.py index d8b8b9d..65d79b5 100644 --- a/app/api/routers/nutrition.py +++ b/app/api/routers/nutrition.py @@ -1,12 +1,17 @@ from __future__ import annotations -from typing import List, Optional +import csv +import io +from datetime import datetime, timezone +from typing import Any, Dict, Iterator, List, Optional from fastapi import APIRouter, HTTPException, Query +from fastapi.responses import StreamingResponse from app.api.nutrition_schemas import ( - FullNutritionOut, HealthyAlternativeOut, NutritionInsightsOut, - PersonalizedRecommendationOut, ProductListItemOut, SimilarProductOut, + FullNutritionOut, HealthScoreItemOut, HealthScoreListOut, + HealthyAlternativeOut, NutritionInsightsOut, PersonalizedRecommendationOut, + ProductListItemOut, SimilarProductOut, ) from app.intelligence import nutrition_recommendation, nutrition_similarity from app.services import nutrition_alternatives_service, nutrition_analytics_service, nutrition_db @@ -100,6 +105,139 @@ def filter_products( return [ProductListItemOut(**r) for r in results] +# --------------------------------------------------------------------------- +# GET Health Scores - every scored consumable product +# --------------------------------------------------------------------------- +# +# Distinct from `/products` above, which exists to answer "show me the ten +# highest-protein snacks". This one exists to answer "give me the health score +# of every consumable product in the catalogue", and that needs three things +# `/products` does not offer: a total so a client knows when it has finished +# paging, a brand filter, and a one-request export. + +# Derived from health_score alone, never stored - a stored band would be one +# more thing that can fall out of step with the score beside it. +# +# The 60 boundary is not a fresh invention: `nutrition_db.store_healthy_ +# distribution()` already counts `health_score >= 60` as a "healthy product". +# Picking a different number here would leave two parts of the same API +# disagreeing about what healthy means. +_HEALTH_BANDS = ((80.0, "excellent"), (60.0, "good"), (40.0, "fair")) + + +def _health_band(score: float) -> str: + for threshold, label in _HEALTH_BANDS: + if score >= threshold: + return label + return "poor" + + +def _to_item(row: Dict[str, Any]) -> HealthScoreItemOut: + return HealthScoreItemOut(**row, health_band=_health_band(row["health_score"])) + + +_CSV_COLUMNS = ( + "brand", "image_id", "product_name", "category", + "health_score", "health_band", "nutrition_score", "scoring_version", + "data_status", "data_source", "source_url", + "calories_kcal", "protein_g", "dietary_fiber_g", "total_sugar_g", "sodium_mg", + "diet_tags", "allergens", +) + + +def _csv_rows(rows: Iterator[Dict[str, Any]]) -> Iterator[str]: + """Yields the export a row at a time so neither the full result set nor the + full response body is ever held in memory at once.""" + buffer = io.StringIO() + writer = csv.writer(buffer, lineterminator=chr(10)) + + def flush() -> str: + value = buffer.getvalue() + buffer.seek(0) + buffer.truncate(0) + return value + + writer.writerow(_CSV_COLUMNS) + yield flush() + + for row in rows: + row = dict(row, health_band=_health_band(row["health_score"])) + writer.writerow([ + # A Postgres TEXT[] would render as "['Vegan', 'Gluten Free']" via + # str(); pipe-joining keeps the cell readable in a spreadsheet. + "|".join(row[c] or []) if c in ("diet_tags", "allergens") else + ("" if row.get(c) is None else row[c]) + for c in _CSV_COLUMNS + ]) + yield flush() + + +@router.get( + "/health-scores", + response_model=None, + responses={200: { + "model": HealthScoreListOut, + "content": {"application/json": {}, "text/csv": {}}, + "description": "Paged JSON envelope, or the whole list as CSV with format=csv.", + }}, +) +def list_health_scores( + brand: Optional[str] = Query(None, description="Exact brand match, e.g. 'Own Products'"), + category: Optional[str] = Query(None, description="Substring match, case-insensitive"), + min_score: Optional[float] = Query(None, ge=0, le=100), + max_score: Optional[float] = Query(None, ge=0, le=100), + include_unknown: bool = Query( + False, + description="Also return products whose edibility was never confirmed. " + "Non-consumables are never returned either way.", + ), + sort_by: str = Query("health_score", description="health_score|nutrition_score|protein|fiber|sugar|sodium|calcium|iron|vitamin_c|calories|product_name|brand|category"), + order: str = Query("desc", pattern="^(asc|desc)$"), + limit: int = Query(100, ge=1, le=500), + offset: int = Query(0, ge=0), + fmt: str = Query("json", alias="format", pattern="^(json|csv)$"), +): + """Every consumable product that has a health score. + + Only scored products are returned: a consumable whose nutrition could not be + matched to a verified source has no score, and is absent rather than present + with a null - `compute_scores` refuses to invent one, and this endpoint + refuses to imply one. + + Non-consumables (soap, shampoo, mosquito repellent) are excluded by the + stored `edibility` verdict, as are products the classifier could not decide + on. `include_unknown=true` opts the undecided ones back in; nothing opts a + confirmed non-consumable in. + + `format=csv` streams the entire matching set in one response and ignores + `limit`/`offset`, which is the point of it. + """ + filters = { + "brand": brand, "category": category, + "min_score": min_score, "max_score": max_score, + "include_unknown": include_unknown, + } + + if fmt == "csv": + stamp = datetime.now(timezone.utc).strftime("%Y%m%d") + return StreamingResponse( + _csv_rows(nutrition_db.iter_health_scores(sort_by=sort_by, order=order, **filters)), + media_type="text/csv; charset=utf-8", + headers={"Content-Disposition": f'attachment; filename="health_scores_{stamp}.csv"'}, + ) + + rows = nutrition_db.query_health_scores( + sort_by=sort_by, order=order, limit=limit, offset=offset, **filters) + return HealthScoreListOut( + # Counted with the SAME filters that produced `rows`, so the two cannot + # describe different populations. + total=nutrition_db.count_health_scores(**filters), + limit=limit, offset=offset, + generated_at=datetime.now(timezone.utc).isoformat(timespec="seconds"), + items=[_to_item(r) for r in rows], + ) + + # --------------------------------------------------------------------------- # GET Nutrition Analytics (Feature 9) # --------------------------------------------------------------------------- diff --git a/app/api/routers/nutrition_admin.py b/app/api/routers/nutrition_admin.py index 2f51aa6..238a32d 100644 --- a/app/api/routers/nutrition_admin.py +++ b/app/api/routers/nutrition_admin.py @@ -9,6 +9,7 @@ from app.api.background import run_in_background from app.api.deps import require_admin from app.api.nutrition_job_store import nutrition_job_store from app.services import nutrition_enrichment_service +from app.services.nutrition_autoenrich import run_enrich_job logger = logging.getLogger(__name__) router = APIRouter(prefix="/admin/nutrition-intelligence", tags=["admin", "nutrition"]) @@ -19,30 +20,13 @@ class EnrichRequest(BaseModel): generate_narrative: bool = True max_products: int | None = None - -def _run_enrich_job(job_id: str, skip_if_verified: bool, generate_narrative: bool, max_products: int | None) -> None: - nutrition_job_store.update(job_id, status="running") - - def progress_cb(done: int, total: int) -> None: - nutrition_job_store.update(job_id, processed=done, total=total) - - try: - result = nutrition_enrichment_service.enrich_all_products( - skip_if_verified=skip_if_verified, generate_narrative=generate_narrative, - progress_cb=progress_cb, max_products=max_products, - ) - nutrition_job_store.update( - job_id, status="done", detail="Enrichment complete", - result={ - "total_products": result.total_products, "verified": result.verified, - "partial": result.partial, "unavailable": result.unavailable, - "duration_seconds": result.duration_seconds, "error_count": len(result.errors), - "errors": result.errors[:20], - }, - ) - except Exception as e: # noqa: BLE001 - logger.exception("Nutrition enrichment job %s failed", job_id) - nutrition_job_store.update(job_id, status="failed", detail=str(e)) + # `enrich_all_products` has accepted these three for a while; the API had no + # way to pass them, so the endpoint could not do what `scripts/ + # enrich_nutrition.py` could. In particular there was no way to widen past + # ACTIVE_BRANDS, which is what a catalogue-wide run needs. + brands: list[str] | None = None + include_inactive: bool = False + categories: list[str] | None = None def _run_train_job(job_id: str) -> None: @@ -64,7 +48,15 @@ def enrich_nutrition(payload: EnrichRequest) -> dict: Equivalent to `python scripts/enrich_nutrition.py`.""" job = nutrition_job_store.create("enrich") run_in_background( - lambda: _run_enrich_job(job.job_id, payload.skip_if_verified, payload.generate_narrative, payload.max_products), + lambda: run_enrich_job( + job.job_id, + skip_if_verified=payload.skip_if_verified, + generate_narrative=payload.generate_narrative, + max_products=payload.max_products, + brands=payload.brands, + include_inactive=payload.include_inactive, + categories=payload.categories, + ), name=f"nutrition-enrich-{job.job_id[:8]}", ) return {"job_id": job.job_id, "status": job.status} diff --git a/app/api/routers/upload.py b/app/api/routers/upload.py index d5a9364..edb9a2e 100644 --- a/app/api/routers/upload.py +++ b/app/api/routers/upload.py @@ -49,7 +49,8 @@ from fastapi.responses import PlainTextResponse from app.api.deps import require_permission from app.services.vector_store import _connect -from app.services import store_db, nutrition_db +from app.services import store_db, nutrition_db, nutrition_scoring +from app.services.consumability import Edibility, classify_edibility logger = logging.getLogger(__name__) router = APIRouter(prefix="/upload", tags=["upload"]) @@ -566,6 +567,7 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: imported_count = 0 errors: List[Dict[str, Any]] = [] skipped = 0 + skipped_non_consumable = 0 status_counts = {"verified": 0, "partial": 0, "unavailable": 0} try: @@ -592,6 +594,20 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: image_id = image_id or _slug_id(brand, product_name) category = _opt_str(row, ['category', 'cat']) + # THE GATE THIS ENDPOINT NEVER HAD. Every other write path into + # nutrition_facts refuses non-food; a spreadsheet could walk + # straight past them and put a health score on a shampoo, which + # then appeared in the public listing endpoints and the + # analytics leaderboards. A refused row is reported, not + # silently dropped, so the uploader can see it was not imported. + verdict = classify_edibility(category or "", product_name or "") + if verdict.edibility is Edibility.NON_CONSUMABLE: + skipped += 1 + skipped_non_consumable += 1 + _record(errors, index, + "not a food or drink, so no nutrition was stored ({})".format(verdict.reason)) + continue + values = { "calories_kcal": _opt_float(row, ['calories', 'calories_kcal', 'energy']), "protein_g": _opt_float(row, ['protein', 'protein_g']), @@ -617,14 +633,15 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: cur.execute( """ INSERT INTO nutrition_facts - (brand, image_id, product_name, category, data_status, data_source, + (brand, image_id, product_name, category, edibility, data_status, data_source, calories_kcal, protein_g, carbohydrates_g, total_sugar_g, dietary_fiber_g, total_fat_g, sodium_mg, calcium_mg, iron_mg, vitamin_c_mg, updated_at) - VALUES (%s, %s, %s, %s, %s, 'excel_upload', + VALUES (%s, %s, %s, %s, %s, %s, 'excel_upload', %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, CURRENT_TIMESTAMP) ON CONFLICT (brand, image_id) DO UPDATE SET product_name = COALESCE(EXCLUDED.product_name, nutrition_facts.product_name), category = COALESCE(EXCLUDED.category, nutrition_facts.category), + edibility = EXCLUDED.edibility, data_source = 'excel_upload', -- Never downgrade a row a trusted source already verified. data_status = CASE WHEN nutrition_facts.data_status = 'verified' @@ -641,7 +658,7 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: vitamin_c_mg = COALESCE(EXCLUDED.vitamin_c_mg, nutrition_facts.vitamin_c_mg), updated_at = CURRENT_TIMESTAMP """, - (brand, image_id, product_name, category, data_status, + (brand, image_id, product_name, category, verdict.edibility.value, data_status, values["calories_kcal"], values["protein_g"], values["carbohydrates_g"], values["total_sugar_g"], values["dietary_fiber_g"], values["total_fat_g"], values["sodium_mg"], values["calcium_mg"], values["iron_mg"], @@ -652,7 +669,7 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: # Only ever built from values this row actually carried. A row # that carried none produces no insight row at all, rather than # a confident-looking one full of defaults. - health_score = _opt_float(row, ['health_score', 'nutrition_score', 'score']) + sheet_score = _opt_float(row, ['health_score', 'nutrition_score', 'score']) diet_tags_raw = _opt_str(row, ['diet_tags', 'tags', 'diet']) allergens_raw = _opt_str(row, ['allergens', 'allergen']) @@ -664,14 +681,47 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: # told", and it is what stops an empty list reading as "none". allergen_source = "upload" if allergens is not None else "unavailable" - positives = [] - if values["protein_g"] is not None: - positives.append(f"Contains {values['protein_g']}g protein per 100g") - if values["dietary_fiber_g"] is not None: - positives.append(f"Provides {values['dietary_fiber_g']}g dietary fiber") - cautions = [] - if values["total_sugar_g"] is not None: - cautions.append(f"{values['total_sugar_g']}g sugar per 100g") + # THE SCORE IS COMPUTED HERE, NOT READ OFF THE SHEET. + # + # This endpoint used to store whatever was in a `health_score` + # column. That number shares a column - and every endpoint that + # sorts, ranks and filters on it - with scores produced by + # `nutrition_scoring.compute_scores` from USDA and Open Food + # Facts data. Two scales in one column means the ordering means + # nothing: an uploaded 95 outranks a computed 80 without either + # number having been measured the same way. + # + # So the same function scores every row in the system, and the + # same rules generate every insight. The uploaded nutrients are + # the input; the score is a conclusion drawn from them. + facts = dict(values, data_status=data_status, + allergens=allergens or []) + scores = nutrition_scoring.compute_scores(facts) + + if scores: + nutrition_score = scores["nutrition_score"] + health_score = scores["health_score"] + scoring_version = scores["scoring_version"] + else: + # Too few nutrients to score fairly. The operator's own + # number is kept rather than discarded - they may have it + # from a source this sheet does not carry - but + # `scoring_version` says where it came from, so nobody + # later mistakes it for one of ours. + nutrition_score = None + health_score = sheet_score + scoring_version = "excel_upload" + + positives = nutrition_scoring.generate_positive_insights(facts) + cautions = nutrition_scoring.generate_cautions(facts) + # Union, not replacement: the rules derive what the numbers + # support ("High Protein"), while the sheet may carry what they + # cannot ("Vegan" is an ingredient fact, not a nutrient one). + derived_tags = nutrition_scoring.classify_diet_tags(facts) + if derived_tags or diet_tags: + diet_tags = list(dict.fromkeys((diet_tags or []) + derived_tags)) + if allergens: + allergens = nutrition_scoring.normalize_allergens(facts) if health_score is not None or diet_tags or allergens or positives or cautions: cur.execute( @@ -680,13 +730,13 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: (brand, image_id, data_status, nutrition_score, health_score, scoring_version, positive_insights, nutritional_cautions, diet_tags, allergens, allergen_source, generated_at) - VALUES (%s, %s, %s, %s, %s, 'excel_upload', %s, %s, %s, %s, %s, CURRENT_TIMESTAMP) + VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, CURRENT_TIMESTAMP) ON CONFLICT (brand, image_id) DO UPDATE SET data_status = CASE WHEN nutrition_insights.data_status = 'verified' THEN 'verified' ELSE EXCLUDED.data_status END, nutrition_score = COALESCE(EXCLUDED.nutrition_score, nutrition_insights.nutrition_score), health_score = COALESCE(EXCLUDED.health_score, nutrition_insights.health_score), - scoring_version = 'excel_upload', + scoring_version = EXCLUDED.scoring_version, positive_insights = COALESCE(EXCLUDED.positive_insights, nutrition_insights.positive_insights), nutritional_cautions = COALESCE(EXCLUDED.nutritional_cautions, nutrition_insights.nutritional_cautions), diet_tags = COALESCE(EXCLUDED.diet_tags, nutrition_insights.diet_tags), @@ -696,8 +746,8 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: ELSE EXCLUDED.allergen_source END, generated_at = CURRENT_TIMESTAMP """, - (brand, image_id, data_status, health_score, health_score, - positives or None, cautions or None, + (brand, image_id, data_status, nutrition_score, health_score, + scoring_version, positives or None, cautions or None, diet_tags, allergens, allergen_source) ) @@ -715,6 +765,11 @@ async def upload_nutrition_file(file: UploadFile = File(...)) -> Dict[str, Any]: file.filename, len(df), imported_count, errors, skipped, "nutritional intelligence items", data_status_counts=status_counts, + # Reported separately from `rows_skipped`, which otherwise reads as "the + # sheet was malformed". These rows were well-formed and deliberately not + # imported. Named to match `EnrichmentResult.skipped_non_consumable`, so + # both write paths report a refusal the same way. + skipped_non_consumable=skipped_non_consumable, ) diff --git a/app/api/routers/user_products.py b/app/api/routers/user_products.py index c38e3ad..e4d9f97 100644 --- a/app/api/routers/user_products.py +++ b/app/api/routers/user_products.py @@ -918,6 +918,16 @@ def _process_upload(filename: str, content: bytes) -> Dict[str, Any]: logger.info("Upload '%s': saved %d, failed %d, skipped %d blank row(s)", filename, body["added_count"], body["error_count"], skipped_blank_rows) + + # Score the consumables that were just stored. `outcome.brands` is already + # the set of brands actually written, so nothing new is tracked to answer + # this. Returns None (and logs) rather than raising if scoring cannot be + # queued - the products are saved either way, and this response has already + # been shaped to say so. + from app.services.nutrition_autoenrich import submit_enrichment_for_brands + + body["nutrition_job_id"] = submit_enrichment_for_brands( + outcome.brands, source="upload of '{}'".format(filename)) return body diff --git a/app/core/batch_ingest.py b/app/core/batch_ingest.py index 6f88b79..f02c515 100644 --- a/app/core/batch_ingest.py +++ b/app/core/batch_ingest.py @@ -208,6 +208,12 @@ class BatchManifest: # Defaults to INPROCESS so every manifest written before this existed, and # every batch an admin uploads directly, keeps behaving exactly as it did. runner: str = RUNNER_INPROCESS + # The nutrition-enrichment job queued when this batch finished, pollable at + # GET /api/admin/nutrition-intelligence/jobs/{job_id}. None when scoring is + # switched off, when the batch produced no brands, or - importantly - for + # every manifest written before this field existed, which is why `from_dict` + # reads it with a default instead of requiring it. + nutrition_job_id: Optional[str] = None files: List[BatchFile] = field(default_factory=list) # -- derived, recomputed rather than stored, so they cannot drift --------- @@ -282,6 +288,7 @@ class BatchManifest: "detail": self.detail, "submitted_by": self.submitted_by, "runner": self.runner, + "nutrition_job_id": self.nutrition_job_id, "files_total": self.files_total, "files_done": self.files_done, "files_failed": self.files_failed, @@ -316,6 +323,7 @@ class BatchManifest: detail=raw.get("detail"), submitted_by=raw.get("submitted_by"), runner=raw.get("runner") or RUNNER_INPROCESS, + nutrition_job_id=raw.get("nutrition_job_id"), files=files, ) @@ -675,6 +683,25 @@ def run_batch( on_change(manifest) manifest.settle() + + # Score what was just ingested. Submitted here, after the last file has + # settled, rather than per file: one job for the whole batch means one + # `skip_if_verified` sweep per brand instead of one per spreadsheet, and a + # batch of twenty files for the same brand does not queue twenty jobs. + # + # This runs on the batch worker's thread, so it must not do the enrichment + # itself - `submit_enrichment_for_brands` only starts a separate thread and + # returns. It also swallows its own failures: the products ARE ingested by + # this point, and a scoring step that could not start is not a reason to + # tell the operator their catalogue import failed. + try: + from app.services.nutrition_autoenrich import submit_enrichment_for_brands + + manifest.nutrition_job_id = submit_enrichment_for_brands( + manifest.brands(), source="batch {}".format(batch_id)) + except Exception: # noqa: BLE001 + logger.exception("Batch %s: could not queue nutrition enrichment", batch_id) + write_manifest(manifest) on_change(manifest) return manifest diff --git a/app/infrastructure/settings.py b/app/infrastructure/settings.py index d1f5a6b..ae4f064 100644 --- a/app/infrastructure/settings.py +++ b/app/infrastructure/settings.py @@ -349,6 +349,36 @@ USE_PLAYWRIGHT_FALLBACK = _bool("USE_PLAYWRIGHT_FALLBACK", "true") # out 1x1 tracking pixels / broken placeholder images) MIN_IMAGE_BYTES = int(os.getenv("MIN_IMAGE_BYTES", "3000")) +# --------------------------------------------------------------------------- +# USDA FoodData Central - nutrition for loose, unbranded commodities +# --------------------------------------------------------------------------- +# Open Food Facts catalogues packaged products and has no entry for a raw +# apple, which is why every fresh-produce row had no nutrition at all. USDA's +# Foundation Foods and SR Legacy datasets are laboratory analyses of raw +# commodities, published per 100 g of edible portion. +# +# NO KEY IS REQUIRED for normal operation: `nutrition_usda_service` reads a +# snapshot built from USDA's open bulk download, so an enrichment run makes zero +# outbound USDA calls. A key is only needed to look up an id the snapshot lacks, +# or to rebuild the snapshot from the live API. +# +# Plain os.getenv rather than `_require(..., feature_flag=...)` on purpose: a +# fresh checkout with no key must still import, or the whole test suite fails at +# collection time. +USE_USDA_FDC = _bool("USE_USDA_FDC", "true") +USDA_FDC_API_KEY = os.getenv("USDA_FDC_API_KEY", "").strip() + +# Score newly uploaded products automatically, instead of waiting for somebody +# to remember to POST /api/admin/nutrition-intelligence/enrich. Runs as a +# background job AFTER the ingestion batch finishes, never inside it - see the +# comment at the submission site in `app/core/batch_ingest.py`. +# +# The off switch exists because this is the one part of ingestion that makes +# outbound calls per product: an operator loading a very large catalogue on a +# metered connection, or re-running an import they intend to score later in one +# controlled pass, needs a way to say "not now" without a code change. +AUTO_ENRICH_ON_UPLOAD = _bool("AUTO_ENRICH_ON_UPLOAD", "true") + # --------------------------------------------------------------------------- # Store-catalog enrichment pipeline (app/core/store_catalog_pipeline.py) # --------------------------------------------------------------------------- diff --git a/app/services/category_registry.py b/app/services/category_registry.py index 2668b61..96b7825 100644 --- a/app/services/category_registry.py +++ b/app/services/category_registry.py @@ -137,9 +137,25 @@ QUERY_STOP_WORDS = { } -def _find_matches(text: str) -> List[Tuple[str, str, int]]: +def _find_matches(text: str, exact_only: bool = False) -> List[Tuple[str, str, int]]: """Return (category, matched_keyword, keyword_length) for every keyword - found as a whole word/phrase in `text` (case-insensitive).""" + found as a whole word/phrase in `text` (case-insensitive). + + `exact_only` suppresses the fuzzy fallback below. It exists because that + fallback is tuned for USER QUERIES, where a near-miss is a typo worth + recovering, and is actively harmful when the text is a PRODUCT TITLE, where + a near-miss is a different product. The case that forced it: + + detect_category_from_text("Colgate-Palmolive Palmolive Naturals") + -> "Chocolates" + + because "colgate" scores 0.8 against the misspelling keyword "choclate". + A toothpaste brand classified as chocolate is harmless in a search box and + is not harmless in `consumability`, which would then hand a bar of soap a + health score. Callers deciding something about a specific product pass + exact_only=True; the RAG/query path keeps the fuzzy behaviour it was + written for. + """ if not text: return [] lower = text.lower() @@ -152,7 +168,7 @@ def _find_matches(text: str) -> List[Tuple[str, str, int]]: matches.append((category, kw, len(kw))) # Fuzzy matching fallback if exact word search found nothing - if not matches: + if not matches and not exact_only: import difflib words = re.findall(r"\b[a-z]{4,}\b", lower) for entry in CATEGORY_REGISTRY: @@ -169,15 +185,19 @@ def _find_matches(text: str) -> List[Tuple[str, str, int]]: return matches -def detect_category_from_text(text: str) -> Optional[str]: +def detect_category_from_text(text: str, exact_only: bool = False) -> Optional[str]: """Infer a single canonical category from free text (typically a user query), or None if no category-identifying keyword is present. When multiple categories match, the one listed earliest in `CATEGORY_REGISTRY` wins (see module docstring); ties within that are broken by the longest matched keyword. + + Pass `exact_only=True` when `text` is a product title rather than a user + query - see `_find_matches` for the Colgate/chocolate case that makes the + distinction matter. """ - matches = _find_matches(text) + matches = _find_matches(text, exact_only=exact_only) if not matches: return None diff --git a/app/services/consumability.py b/app/services/consumability.py new file mode 100644 index 0000000..ff9136c --- /dev/null +++ b/app/services/consumability.py @@ -0,0 +1,382 @@ +""" +Is this product something a person eats or drinks? + +WHY THIS FILE EXISTS +-------------------- +The nutrition module's contract is that every number it publishes traces to a +verified source (see `nutrition_data_service`'s docstring). That contract says +nothing about products which have no nutrition panel *at all*, and the only +guard that existed was a seventeen-word substring list: + + nutrition_data_service.NON_FOOD_KEYWORDS = ("soap", "detergent", "shampoo", ...) + +matched against `f"{title} {category}".lower()`. It missed every category name it +was not literally spelled with. Measured against the live catalogue, these rows +carry a health score today: + + Colgate-Palmolive Palmolive Naturals General score present + Cavinkare Nyle / Nature's Hair Care score present + P&G Pantene Hair Care score present + Godrej Hit Spray Personal Care - Mosquito 291 kcal (!) + +A mosquito repellent with a calorie count is not a cosmetic defect. It is the +nutrition module asserting a fact about a product it has no business describing, +and a shopper has no way to tell that the number is meaningless. + +Being a substring test, that list also has the opposite failure: "soap" matches +*soapnut* (reetha), a real commodity. Matching here is whole-word. + +WHY THREE STATES AND NOT A BOOLEAN +---------------------------------- +`is_consumable` and `is_non_consumable` are NOT inverses, and that is the single +most important thing in this file. + +Two callers ask opposite questions of the same fact: + + the WRITE gate - "may I attach nutrition to this?" unknown => NO + the DELETE gate - "may I destroy this row?" unknown => NO + +Collapsing them into one boolean makes whichever caller loses the coin-toss act +destructively on a guess: a single `not is_consumable()` in the purge script +would delete every row the classifier merely failed to recognise. So the engine +returns `CONSUMABLE | NON_CONSUMABLE | UNKNOWN`, and both booleans are positive +tests that return False for UNKNOWN. + +WHY A LAYERED DECISION AND NOT ONE KEYWORD LIST +----------------------------------------------- +The catalogue's category strings are not one vocabulary. Four maps have drifted +apart - CATEGORY_REGISTRY (31 names), HSN_GST_TABLE (60), CATEGORY_UNIT_TYPE +(~70) and CATEGORY_TYPE_WORDS - and the live tables hold 66 distinct values +including "General" (40 rows) and the bare strings "1".."5" (17 rows) left by a +bad import. Any single list is stale the moment somebody adds a category. + + 1. An explicit per-category verdict, hand-set, for every string the live + catalogue actually contains. + 2. Failing that, the HSN chapter the category resolves to. The tariff's own + classification, already maintained here for tax purposes. + 3. Failing that, the product title, through the same commodity lexicon and + category detector the ingestion pipeline uses. + 4. Failing that, UNKNOWN - which enriches nothing and deletes nothing. + +WHY THE HSN RANGE IS NOT SIMPLY 01-24 +-------------------------------------- +"Chapters 1 to 24 are the food chapters" is the obvious rule and it is wrong at +exactly the case this project cares about. Chapter 06 is live plants and cut +flowers, and `Flowers` is a live category here with 14 rows that reach the same +Own Products table as the vegetables. A naive range would score a jasmine +garland. Excluded for the same reason: 05 (inedible animal products), 14 +(vegetable plaiting materials), 23 (animal feed) and 24 (tobacco - consumed, but +it publishes no nutrition panel). + +EDGE CASES, AND WHY THEY WENT THE WAY THEY DID +---------------------------------------------- +Flowers False. Sold loose beside the vegetables and routed by the same + produce lexicon, but a garland is not food. +Oral Care False. Toothpaste goes in the mouth and is spat out; it carries + no nutrition panel, and it is Open *Beauty* Facts that matches it. +Health Care - False. A cough syrup is ingested but is regulated as a drug and + Cold & Cough / publishes dosage, not nutrition. Scoring it would be the most + Digestive dangerous error available here. +Baby Care MIXED, so it defers to the title. The audit found 21 live rows +Health Care - that are Nestle Cerelac, Nan Pro and Lactogen sitting beside a + Ayurvedic bottle of baby oil; Dabur Chyawanprash sits beside cough syrup. + Infant formula is among the most nutrition-labelled food sold in + India, so a blanket "not food" here would have been a worse + defect than the one this module fixes. +Health Drinks True. Horlicks, Boost and Complan publish a real nutrition + panel, notwithstanding the word "Health". +Household - False, and listed explicitly so it can never be swept in by + Lamp Oil "Cooking Oils" being True. See `_hsn_verdict` for the second + guard on the same trap. +""" +from __future__ import annotations + +import re +from dataclasses import dataclass +from enum import Enum +from typing import Dict, Optional, Tuple + +from app.services.category_registry import _normalize, detect_category_from_text + + +class Edibility(str, Enum): + CONSUMABLE = "consumable" + NON_CONSUMABLE = "non_consumable" + UNKNOWN = "unknown" + + +@dataclass(frozen=True) +class EdibilityVerdict: + edibility: Edibility + reason: str # human-readable; the purge audit prints this + signal: str # category_map | hsn_chapter | title_lexicon | title_keyword | none + + +# --------------------------------------------------------------------------- +# 1. Explicit verdicts +# --------------------------------------------------------------------------- +# Keyed on the NORMALIZED category ("Pulses, Grains & Spices" -> "pulses grains +# and spices") so a stored string differing only in punctuation or case still +# lands here rather than falling through to the HSN guess. `_normalize` is +# imported rather than reimplemented: a fifth normalizer that disagreed with the +# other four is exactly how this area got into trouble. +_CONSUMABLE_CATEGORIES: Tuple[str, ...] = ( + # Dairy and dairy-adjacent + "Dairy", "Cheese", "Dairy - Desserts", "Ice Cream", + # Drinks + "Beverages", "Tea & Coffee", "Health Drinks", "Food & Beverages", + # Fresh, loose goods sold by weight or by the piece + "Fruits & Vegetables", "Fresh Herbs & Greens", "Fish & Seafood", "Eggs", + # Bakery and biscuit + "Biscuits & Cookies", "Biscuits", "Crackers", "Rusk", "Cakes & Muffins", + "Bakery & Breads", "Breakfast Cereal", + # Confectionery + "Chocolates", "Candy & Confectionery", + # Savoury + "Snacks", "Namkeen", "Ready to Eat", "Noodles & Instant Food", + "Pasta & Noodles", + # Staples, pulses, spices + "Atta & Staples", "Staples", "Flour & Grains", "Salt & Staples", + "Sugar & Jaggery", "Pulses, Grains & Spices", "Spices & Masalas", + "Cooking Oils", "Dry Fruits & Nuts", "Millets", "Rice & Pulses", + # Prepared foods + "Food - Mixes", "Food - Spreads", "Food - Soups & Sauces", + "Pickles & Chutneys", "Health Foods", "Sweets", +) + +_NON_CONSUMABLE_CATEGORIES: Tuple[str, ...] = ( + # Personal care + "Hair Care", "Skin Care", "Skin & Bath Care", "Bath Soap", "Beauty Care", + "Oral Care", "Fragrance & Deodorants", "Men's Grooming", + "Feminine Hygiene", "Personal Care", + # Household + "Detergents & Fabric Care", "Dishwash", "Household Cleaning", + "Household - Agarbatti", "Household - Lamp Oil", "Household - Air Freshener", + "Personal Care - Mosquito Repellent", + # Ingested, but regulated as medicines: they publish dosage, not nutrition + "Health Care - Cold & Cough", "Health Care - Digestive", + "Health Care - Antiseptic", "Health Care - First Aid", + # Not food, despite arriving through the produce lexicon + "Flowers", +) + +# Categories holding BOTH food and non-food, where a single verdict is simply +# wrong. Deferred to the title, exactly like an uninformative category. +# +# Found by running the purge audit before deleting anything - which is what that +# dry run is for. "Baby Care" held 21 live rows, and they are Nestle Cerelac, +# Nan Pro and Lactogen alongside a bottle of baby oil. Infant formula and baby +# cereal are among the most heavily nutrition-labelled products sold in India; +# refusing them a score would have been a worse defect than the one being fixed. +# +# "Health Care - Ayurvedic" is the same shape: Dabur Chyawanprash is eaten by +# the spoonful and carries a nutrition panel, while a cough syrup does not. +_MIXED_CATEGORIES = frozenset( + {_normalize("Baby Care"), _normalize("Health Care - Ayurvedic")} +) + +CATEGORY_VERDICTS: Dict[str, Edibility] = { + **{_normalize(c): Edibility.CONSUMABLE for c in _CONSUMABLE_CATEGORIES}, + **{_normalize(c): Edibility.NON_CONSUMABLE for c in _NON_CONSUMABLE_CATEGORIES}, +} + +# Category strings carrying no information. Treated as MISSING (ask the title), +# not as UNKNOWN (refuse) - 40 live rows sit in "General" and many are real food. +# The bare numerics "1".."5" are import damage on 17 rows; repairing those is a +# catalogue fix, not a consumability rule, so they are recognised and reported +# rather than accommodated. +_UNINFORMATIVE_CATEGORIES = frozenset( + {"", "general", "uncategorized", "uncategorised", "other", "others", + "misc", "miscellaneous", "unknown", "na", "none"} +) + +_JUNK_CATEGORY_RE = re.compile(r"^\d+$") + +# HSN chapters that are food and drink, per the customs tariff. Deliberately NOT +# `range(1, 25)` - see the module docstring for why 05, 06, 14, 23 and 24 are out. +_HSN_FOOD_CHAPTERS = frozenset( + {1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22} +) +# Cosmetics, soap, pharma, insecticide, razors, hygiene articles. +_HSN_NON_FOOD_CHAPTERS = frozenset({5, 6, 14, 23, 24, 28, 29, 30, 33, 34, 38, 82, 96}) + +# Title words that settle an uninformative category on their own. Deliberately +# short: the commodity lexicon and the category detector do the real work, and a +# longer list here would start overriding them. +_NON_FOOD_TITLE_WORDS = frozenset({ + "soap", "detergent", "shampoo", "conditioner", "toothpaste", "toothbrush", + "mouthwash", "deodorant", "perfume", "cosmetic", "lipstick", "kajal", + "talc", "lotion", "moisturizer", "moisturiser", "sunscreen", "facewash", + "handwash", "sanitizer", "sanitiser", "diaper", "sanitary", "napkin", + "razor", "shaving", "cleaner", "disinfectant", "phenyl", "bleach", + "repellent", "mosquito", "agarbatti", "incense", "camphor", "matchbox", + "battery", "bulb", "candle", "polish", "freshener", "dishwash", +}) + +# Title words that positively identify FOOD, consulted before the non-food list. +# These exist for the mixed categories above: a product line name is the only +# signal a title like "Nestle Cerelac 125g" carries, since the commodity lexicon +# knows nothing of it. Naming specific ranges is consistent with how this +# codebase already resolves ambiguity (BRAND_ALIASES, and the "bikis" keyword +# added to Biscuits & Cookies so Britannia Milk Bikis is not filed as Dairy). +_FOOD_TITLE_WORDS = frozenset({ + # infant and toddler nutrition + "cerelac", "lactogen", "nangrow", "nan", "farex", "dexolac", "nusobee", + "formula", "infant", "weaning", "porridge", "cereal", "cereals", + # ayurvedic preparations eaten as food + "chyawanprash", "chyavanprash", "honey", "malt", +}) + +_WORD_RE = re.compile(r"[a-z0-9]+") + + +def _words(text: str) -> frozenset: + """Whole words, lowercased. Whole-word matching is the point: the old + substring gate classified *soapnut* (reetha) as a soap.""" + return frozenset(_WORD_RE.findall((text or "").lower())) + + +def _hsn_verdict(category: str) -> Optional[Edibility]: + """The verdict implied by the HSN chapter this category maps to. + + Reads `HSN_GST_TABLE` directly rather than calling `resolve_hsn_gst`, and + that is deliberate. `resolve_hsn_gst` falls through to `_KEYWORD_FALLBACKS`, + which scans `f"{product_title} {category}"` and contains a greedy + `("oil", "1517")` entry - enough to classify "Household - Lamp Oil" as an + edible oil. Reading the table means only an exact category name can match, + so the fallbacks can never fire here at all. + + Imported lazily: the hsn_gst package pulls in the enrichment machinery, and + the nutrition path should not pay for that import on every call. + """ + from app.services.enrichment.hsn_gst.models import HSN_GST_TABLE + + target = _normalize(category) + for name, entry in HSN_GST_TABLE.items(): + if _normalize(name) != target: + continue + try: + chapter = int(str(entry[0])[:2]) + except (TypeError, ValueError, IndexError): + return None + if chapter in _HSN_FOOD_CHAPTERS: + return Edibility.CONSUMABLE + if chapter in _HSN_NON_FOOD_CHAPTERS: + return Edibility.NON_CONSUMABLE + return None + return None + + +def classify_edibility(category: Optional[str], title: str = "") -> EdibilityVerdict: + """The full verdict, with the reason that produced it. + + The reason string is what the purge audit prints, so a row's fate can be + argued with rather than taken on trust. + """ + normalized = _normalize(category) + informative = ( + normalized + and normalized not in _UNINFORMATIVE_CATEGORIES + and normalized not in _MIXED_CATEGORIES + and not _JUNK_CATEGORY_RE.match(normalized) + ) + + if informative: + verdict = CATEGORY_VERDICTS.get(normalized) + if verdict is not None: + return EdibilityVerdict( + verdict, + f"category {category!r} is listed as {verdict.value}", + "category_map", + ) + + hsn = _hsn_verdict(category or "") + if hsn is not None: + return EdibilityVerdict( + hsn, + f"category {category!r} maps to an HSN chapter that is " + + ("food/beverage" if hsn is Edibility.CONSUMABLE else "not food"), + "hsn_chapter", + ) + + # The category told us nothing usable. Ask the title, through the same two + # resolvers the ingestion pipeline uses, so a row classified here agrees + # with the row the pipeline would have written. + text = (title or "").strip() + if text: + words = _words(text) + + food_hit = words & _FOOD_TITLE_WORDS + if food_hit: + return EdibilityVerdict( + Edibility.CONSUMABLE, + f"title {title!r} names a food product ({sorted(food_hit)[0]!r})", + "title_keyword", + ) + + hit = words & _NON_FOOD_TITLE_WORDS + if hit: + return EdibilityVerdict( + Edibility.NON_CONSUMABLE, + f"title {title!r} names a non-food article ({sorted(hit)[0]!r})", + "title_keyword", + ) + + from app.services.generic_products import canonical_category + + # `detect_category_from_text` is pinned to exact_only. Its fuzzy + # fallback scores "colgate" at 0.8 against the misspelling keyword + # "choclate", so without this a tube of toothpaste reads as Chocolates + # and earns a health score. Verified: that is the live behaviour for + # "Colgate-Palmolive Palmolive Naturals", whose category is "General". + for resolver in ( + canonical_category, + lambda t: detect_category_from_text(t, exact_only=True), + ): + detected = resolver(text) + if not detected: + continue + verdict = CATEGORY_VERDICTS.get(_normalize(detected)) + if verdict is not None: + return EdibilityVerdict( + verdict, + f"title {title!r} reads as {detected!r}, which is {verdict.value}", + "title_lexicon", + ) + + return EdibilityVerdict( + Edibility.UNKNOWN, + f"neither category {category!r} nor title {title!r} identifies this " + "product; refusing to guess", + "none", + ) + + +def is_consumable(category: Optional[str], title: str = "") -> bool: + """True only when this is positively something a person eats or drinks. + + The WRITE gate. False for UNKNOWN, so an unrecognised product gets no + nutrition row and no health score - the same asymmetry + `nutrition_data_service` already chose when it returns + `data_status="unavailable"` rather than zeroes. + """ + return classify_edibility(category, title).edibility is Edibility.CONSUMABLE + + +def is_non_consumable(category: Optional[str], title: str = "") -> bool: + """True only when this is positively NOT food. + + The DELETE gate, and deliberately not `not is_consumable(...)`. False for + UNKNOWN, so the purge script can never destroy a row it merely failed to + recognise. See the module docstring. + """ + return classify_edibility(category, title).edibility is Edibility.NON_CONSUMABLE + + +def is_junk_category(category: Optional[str]) -> bool: + """True for the bare numeric category strings left by a bad import. + + Reported by the purge audit so the 17 affected rows get repaired in the + catalogue rather than worked around here. + """ + return bool(_JUNK_CATEGORY_RE.match(_normalize(category))) diff --git a/app/services/data/produce_reference.json b/app/services/data/produce_reference.json new file mode 100644 index 0000000..173e91c --- /dev/null +++ b/app/services/data/produce_reference.json @@ -0,0 +1,1221 @@ +{ + "Agathi Keerai": { + "image_credit": "Sesbania grandiflora", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/7/74/Starr_050518-1632_Sesbania_grandiflora.jpg", + "wikipedia_title": "Sesbania grandiflora" + }, + "Amaranth Leaves": { + "image_credit": "Amaranthus dubius", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/9/98/Starr_040527-9001_Amaranthus_dubius.jpg", + "usda_description": "Amaranth leaves, raw", + "usda_fdc_id": 168385, + "wikipedia_title": "Amaranthus dubius" + }, + "Amla": { + "image_credit": "Phyllanthus emblica", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/7/7f/Phyllanthus_officinalis.jpg", + "wikipedia_title": "Phyllanthus emblica" + }, + "Apple": { + "image_credit": "Apple", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/a/a6/Pink_lady_and_cross_section.jpg", + "usda_description": "Apples, raw, with skin (Includes foods for USDA's Food Distribution Program)", + "usda_fdc_id": 171688, + "wikipedia_title": "Apple" + }, + "Apple Green": { + "image_credit": "Granny Smith", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/d/d7/Granny_smith_and_cross_section.jpg", + "usda_description": "Apples, raw, with skin (Includes foods for USDA's Food Distribution Program)", + "usda_fdc_id": 171688, + "wikipedia_title": "Granny Smith" + }, + "Apricot": { + "image_credit": "Apricot", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/2/2a/Apricot_and_cross_section.jpg", + "usda_description": "Apricots, raw", + "usda_fdc_id": 171697, + "wikipedia_title": "Apricot" + }, + "Arali": { + "image_credit": "Nerium", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/c/cc/Nerium_oleander_flowers_leaves.jpg", + "wikipedia_title": "Nerium" + }, + "Ash Gourd": { + "image_credit": "Wax gourd", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/d/d2/Benincasa_hispida_compose.jpg", + "wikipedia_title": "Wax gourd" + }, + "Baby Corn": { + "image_credit": "Baby corn", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/a/a1/Baby_corn.jpg", + "wikipedia_title": "Baby corn" + }, + "Banana": { + "image_credit": "Banana", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/d/de/Bananavarieties.jpg", + "usda_description": "Bananas, raw", + "usda_fdc_id": 173944, + "wikipedia_title": "Banana" + }, + "Banana Nendran": { + "image_credit": "Banana", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/d/de/Bananavarieties.jpg", + "usda_description": "Bananas, raw", + "usda_fdc_id": 173944, + "wikipedia_title": "Banana" + }, + "Banana Red": { + "image_credit": "Red banana", + "image_source": "wikipedia_lead", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/9/92/Red_banana_in_Tanzania_0196_Nevit.jpg", + "usda_description": "Bananas, raw", + "usda_fdc_id": 173944, + 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"amount": 0.04, + "id": 1126, + "name": "Tocopherol, gamma", + "unit": "mg" + }, + { + "amount": 0.01, + "id": 1128, + "name": "Tocotrienol, alpha", + "unit": "mg" + }, + { + "amount": 0.0, + "id": 1129, + "name": "Tocotrienol, beta", + "unit": "mg" + }, + { + "amount": 0.639, + "id": 1167, + "name": "Niacin", + "unit": "mg" + }, + { + "amount": 0.61, + "id": 1170, + "name": "Pantothenic acid", + "unit": "mg" + }, + { + "amount": 0.191, + "id": 1175, + "name": "Vitamin B-6", + "unit": "mg" + }, + { + "amount": 0.0, + "id": 1184, + "name": "Vitamin K (Dihydrophylloquinone)", + "unit": "\u00b5g" + }, + { + "amount": 0.82, + "id": 1012, + "name": "Fructose", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1013, + "name": "Lactose", + "unit": "g" + }, + { + "amount": 2.57, + "id": 1003, + "name": "Protein", + "unit": "g" + }, + { + "amount": 0.83, + "id": 1007, + "name": "Ash", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1108, + "name": "Carotene, alpha", + "unit": "\u00b5g" + }, + { + "amount": 0.0, + "id": 1075, + "name": "Galactose", + "unit": "g" + }, + { + "amount": 2.4, + "id": 1079, + "name": "Fiber, total dietary", + "unit": "g" + }, + { + "amount": 0.69, + "id": 1089, + "name": "Iron, Fe", + "unit": "mg" + }, + { + "amount": 21.0, + "id": 1090, + "name": "Magnesium, Mg", + "unit": "mg" + }, + { + "amount": 67.0, + "id": 1091, + "name": "Phosphorus, P", + "unit": "mg" + }, + { + "amount": 36.0, + "id": 1093, + "name": "Sodium, Na", + "unit": "mg" + }, + { + "amount": 0.059, + "id": 1098, + "name": "Copper, Cu", + "unit": "mg" + }, + { + "amount": 0.197, + "id": 1101, + "name": "Manganese, Mn", + "unit": "mg" + }, + { + "amount": 0.0, + "id": 1127, + "name": "Tocopherol, delta", + "unit": "mg" + }, + { + "amount": 0.0, + "id": 1130, + "name": "Tocotrienol, gamma", + "unit": "mg" + }, + { + "amount": 0.0, + "id": 1131, + "name": "Tocotrienol, delta", + "unit": "mg" + }, + { + "amount": 91.3, + "id": 1162, + "name": "Vitamin C, total ascorbic acid", + "unit": "mg" + }, + { + "amount": 0.077, + "id": 1165, + "name": "Thiamin", + "unit": "mg" + }, + { + "amount": 0.114, + "id": 1166, + "name": "Riboflavin", + "unit": "mg" + }, + { + "amount": 0.0, + "id": 1333, + "name": "MUFA 15:1", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1261, + "name": "SFA 8:0", + "unit": "g" + }, + { + "amount": 0.033, + "id": 1210, + "name": "Tryptophan", + "unit": "g" + }, + { + "amount": 0.088, + "id": 1211, + "name": "Threonine", + "unit": "g" + }, + { + "amount": 0.038, + "id": 1215, + "name": "Methionine", + "unit": "g" + }, + { + "amount": 0.117, + "id": 1217, + "name": "Phenylalanine", + "unit": "g" + }, + { + "amount": 0.05, + "id": 1218, + "name": "Tyrosine", + "unit": "g" + }, + { + "amount": 0.104, + "id": 1222, + "name": "Alanine", + "unit": "g" + }, + { + "amount": 0.542, + "id": 1224, + "name": "Glutamic acid", + "unit": "g" + }, + { + "amount": 0.089, + "id": 1225, + "name": "Glycine", + "unit": "g" + }, + { + "amount": 0.11, + "id": 1226, + "name": "Proline", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1300, + "name": "SFA 17:0", + "unit": "g" + }, + { + "amount": 102, + "id": 1185, + "name": "Vitamin K (phylloquinone)", + "unit": "\u00b5g" + }, + { + "amount": 0.0, + "id": 1263, + "name": "SFA 12:0", + "unit": "g" + }, + { + "amount": 65.0, + "id": 1177, + "name": "Folate, total", + "unit": "\u00b5g" + }, + { + "amount": 8.0, + "id": 1106, + "name": "Vitamin A, RAE", + "unit": "\u00b5g" + }, + { + "amount": 0.0, + "id": 1120, + "name": "Cryptoxanthin, beta", + "unit": "\u00b5g" + }, + { + "amount": 0.0, + "id": 1122, + "name": "Lycopene", + "unit": "\u00b5g" + }, + { + "amount": 93.0, + "id": 1107, + "name": "Carotene, beta", + "unit": "\u00b5g" + }, + { + "amount": 745, + "id": 1123, + "name": "Lutein + zeaxanthin", + "unit": "\u00b5g" + }, + { + "amount": 2.4, + "id": 1084, + "name": "Fiber, insoluble", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1082, + "name": "Fiber, soluble", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1116, + "name": "Phytoene", + "unit": "\u00b5g" + }, + { + "amount": 0.0, + "id": 1117, + "name": "Phytofluene", + "unit": "\u00b5g" + }, + { + "amount": 0.41, + "id": 1002, + "name": "Nitrogen", + "unit": "g" + }, + { + "amount": 1.4, + "id": 1063, + "name": "Sugars, Total", + "unit": "g" + }, + { + "amount": 3.8, + "id": 1050, + "name": "Carbohydrate, by summation", + "unit": "g" + }, + { + "amount": 0.0, + "id": 1314, + "name": "MUFA 16:1 c", + "unit": "g" + }, + { + "amount": 0.01, + "id": 1315, + "name": "MUFA 18:1 c", + "unit": "g" + }, + { + "amount": 0.0, + "id": 2009, + "name": "MUFA 14:1 c", + "unit": "g" + }, + { + "amount": 0.001, + "id": 2010, + "name": "MUFA 17:1 c", + "unit": "g" + }, + { + "amount": 0.0, + "id": 2012, + "name": "MUFA 20:1 c", + "unit": "g" + }, + { + "amount": 0.017, + "id": 2016, + "name": "PUFA 18:2 c", + "unit": "g" + }, + { + "amount": 0.0, + "id": 2018, + "name": "PUFA 18:3 c", + "unit": "g" + }, + { + "amount": 0.0, + "id": 2020, + "name": "PUFA 20:3 c", + "unit": "g" + }, + { + "amount": 0.0, + "id": 2022, + "name": "PUFA 20:4c", + "unit": "g" + }, + { + "amount": 0.0, + "id": 2026, + "name": "PUFA 20:2 c", + "unit": "g" + }, + { + "amount": 0.07, + "id": 1085, + "name": "Total fat (NLEA)", + "unit": "g" + } + ], + "portions": [ + { + "description": "cup", + "gram_weight": 76.0 + } + ], + "publication_date": "12/16/2019" + } + }, + "source": "USDA FoodData Central bulk download" +} \ No newline at end of file diff --git a/app/services/image_search.py b/app/services/image_search.py index 5d696dd..189b87c 100644 --- a/app/services/image_search.py +++ b/app/services/image_search.py @@ -48,6 +48,7 @@ from typing import Optional, List import requests from urllib.parse import urlparse import json +import re import logging import subprocess import sys @@ -162,7 +163,8 @@ def find_product_quantity_openfacts(title: str, brand: Optional[str] = None) -> # --------------------------------------------------------------------------- # 2. Wikimedia Commons - free/open media repository, no API key # --------------------------------------------------------------------------- -def find_images_wikimedia(title: str, brand: Optional[str] = None, max_results: int = 20) -> list: +def find_images_wikimedia(title: str, brand: Optional[str] = None, max_results: int = 20, + produce: bool = False) -> list: """Search Wikimedia Commons (the open media library behind Wikipedia) for product/packaging photos. Good secondary source for established brands; complements Open*Facts which leans more food/grocery.""" @@ -180,6 +182,14 @@ def find_images_wikimedia(title: str, brand: Optional[str] = None, max_results: # MediaWiki's standard search syntax including the minus (-) prefix # for terms to exclude. exclusion_terms = "-car -vehicle -automotive -motorsport -racing -motorcycle -bike -people -person -portrait -animal -pet -dog -cat -bird -fish -landscape -nature -tour -travel -building -architecture -sport -game -flower -rose -floral -petal -bouquet -botanical -plant -garden -tree -herb" + if produce: + # Every one of those exclusions describes what a fruit, a vegetable + # or a fish ACTUALLY IS. Searching Commons for a tomato while + # excluding "-plant -garden -nature", or for mackerel while + # excluding "-fish -animal", asks the index to rule out the answer. + # The exclusions exist to keep a BRANDED search off unrelated + # subjects, which is not the problem here. + exclusion_terms = "-logo -advertisement -packaging" search_query = f"{query} {exclusion_terms} filetype:bitmap" resp = requests.get( "https://commons.wikimedia.org/w/api.php", @@ -402,6 +412,23 @@ def _dedupe(urls: list) -> list: # that image search APIs tend to confuse with non-product content. # Keyed by the ambiguous word (lowercase); value is the context term # to append to the search query. +# +# EVERY KEY HERE ASSUMES THE WORD IS A PACKAGED BRAND, NOT A COMMODITY. +# The table was written for Cadbury Perk and Britannia Tiger, where "perk" and +# "tiger" really do need steering away from perks and big cats. For the +# Own Products bucket the word IS the literal item, so the hint inverts the +# search: `"apple" -> "fruit juice"` is why a search for the fruit returned +# juice cartons and milkshakes, and why the stored image for `Apple` was a +# 2-litre juice bottle. +# +# Callers handling loose produce pass `produce=True`, which skips this table +# entirely. Do not "fix" an entry by making it more specific - the whole table +# is wrong in that context, not any one row of it. +# Kept as a named constant rather than an inline literal: this pattern has to +# survive being edited by tooling that mangles backslash escapes, and a silent +# corruption here turns whole-word matching back into no matching at all. +_WORD_BOUNDARY = chr(92) + "b" + _AMBIQUITY_HINTS: dict[str, str] = { "perk": "chocolate wafer", "crunch": "chocolate wafer", @@ -438,7 +465,8 @@ _AMBIQUITY_HINTS: dict[str, str] = { } -def _clean_search_title(title: str, brand: str | None = None) -> str: +def _clean_search_title(title: str, brand: str | None = None, + produce: bool = False) -> str: """Strip redundant brand prefix from title and add context hints for ambiguous product names that confuse image search APIs. @@ -448,6 +476,9 @@ def _clean_search_title(title: str, brand: str | None = None) -> str: _clean_search_title("Britannia Good Day Biscuits", "Britannia") -> "Good Day Biscuits" + + `produce=True` suppresses the hint table entirely - see `_AMBIQUITY_HINTS` + for why it is exactly backwards for a raw commodity. """ clean = (title or "").strip() if brand: @@ -458,10 +489,25 @@ def _clean_search_title(title: str, brand: str | None = None) -> str: if not clean: clean = (title or "").strip() - # Append context hint for any ambiguous word in the cleaned title + if produce: + return clean + + # Append a context hint for an ambiguous word in the cleaned title. + # + # MATCHED AS A WHOLE WORD, not as a substring. The substring version pulled + # hints into every title that merely CONTAINED a keyword: + # + # Pineapple -> "Pineapple fruit juice" (apple) + # Custard Apple -> "Custard Apple fruit juice" (apple) + # Buttermilk -> "Buttermilk chocolate dairy" (milk) + # Baby Corn -> "Baby Corn snack flakes" (corn) + # Eggs White -> "Eggs White toothpaste dental" (white) + # + # Multi-word keys ("good day", "5 star") still need a phrase search, so the + # boundary is applied around the whole key rather than per token. clean_lower = clean.lower() for word, hint in _AMBIQUITY_HINTS.items(): - if word in clean_lower: + if re.search(_WORD_BOUNDARY + re.escape(word) + _WORD_BOUNDARY, clean_lower): clean = f"{clean} {hint}" break @@ -472,14 +518,16 @@ def _clean_search_title(title: str, brand: str | None = None) -> str: # Public API (same signatures as before, so catalog_engine.py and # downstream callers don't need to change) # --------------------------------------------------------------------------- -def find_image_url(title: str, brand: Optional[str] = None, country_hint: Optional[str] = None) -> Optional[str]: +def find_image_url(title: str, brand: Optional[str] = None, country_hint: Optional[str] = None, + produce: bool = False) -> Optional[str]: """Get a single working, validated image URL.""" - urls = find_all_image_urls(title, brand, country_hint) + urls = find_all_image_urls(title, brand, country_hint, produce=produce) return urls[0] if urls else None def find_all_image_urls(title: str, brand: Optional[str] = None, country_hint: Optional[str] = None, - validate: bool = True, max_results: int = 24) -> list: + validate: bool = True, max_results: int = 24, + produce: bool = False) -> list: """Get working image URLs by trying multiple open-source sources in priority order (cheapest/most-reliable first), merging and validating results. Only escalates to the Playwright browser fallback if every @@ -488,11 +536,18 @@ def find_all_image_urls(title: str, brand: Optional[str] = None, country_hint: O # Strip redundant brand prefix from title to avoid repetitive queries # like "Cadbury Perk Cadbury Perk Crunch". - search_title = _clean_search_title(title, brand) + search_title = _clean_search_title(title, brand, produce=produce) - candidates.extend(find_images_openfacts(search_title, brand, max_results)) + # Open*Facts is a PACKAGED-GOODS database - food, beauty and household + # products photographed front-of-pack. Asked about loose produce it answers + # with whatever carton or bottle mentions the word, which is how the live + # catalogue ended up serving openbeautyfacts cosmetics photos for Banana, + # Orange, Papaya, Guava, Lemon, Pear and twenty more. Skipped for produce. + if not produce: + candidates.extend(find_images_openfacts(search_title, brand, max_results)) if len(candidates) < max_results: - candidates.extend(find_images_wikimedia(search_title, brand, max_results)) + candidates.extend(find_images_wikimedia(search_title, brand, max_results, + produce=produce)) if len(candidates) < max_results: candidates.extend(find_all_image_urls_ddg(search_title, brand, max_results)) if len(candidates) < max_results: diff --git a/app/services/nutrition_autoenrich.py b/app/services/nutrition_autoenrich.py new file mode 100644 index 0000000..81a3244 --- /dev/null +++ b/app/services/nutrition_autoenrich.py @@ -0,0 +1,119 @@ +"""Scoring newly uploaded products, without making the uploader wait for it. + +WHY THIS IS NOT A PIPELINE STAGE +-------------------------------- +The obvious place is a twelfth stage in `store_catalog_pipeline`, and it is the +wrong one. That pipeline runs on `batch_worker`'s single thread draining a +bounded queue: one batch at a time, by design, because a batch is up to twenty +spreadsheets of two thousand rows. Nutrition enrichment is minutes of outbound +network I/O per hundred products, so putting it inside the pipeline would stall +every batch queued behind it, and a network hiccup at Open Food Facts would show +up to an operator as a catalogue import that hung. + +So enrichment is submitted AFTER the batch settles, onto its own thread, with +its own job record - the same shape `POST /api/admin/nutrition-intelligence/ +enrich` already uses, and pollable at the same `/jobs/{job_id}` endpoint. + +WHY IT NEEDS NO "WHICH ROWS ARE NEW" BOOKKEEPING +------------------------------------------------ +`skip_if_verified=True` makes `enrich_all_products` re-fetch only products that +lack verified data. Re-uploading a brand that is already scored therefore costs +nothing but a cheap existence check per row, and the products that do get looked +up are exactly the ones the upload added. Tracking new image_ids separately +would be a second source of truth for the same question. +""" +from __future__ import annotations + +import logging +from typing import Any, Iterable, List, Optional + +from app.api.background import run_in_background +from app.api.nutrition_job_store import nutrition_job_store +from app.infrastructure import settings +from app.services import nutrition_enrichment_service + +logger = logging.getLogger(__name__) + + +def run_enrich_job(job_id: str, *, label: str = "", **enrich_kwargs: Any) -> None: + """Runs `enrich_all_products` under a job record. + + Shared by the admin endpoint and the post-upload hook so both report the + same result keys - a dashboard reading one should not have to special-case + the other. + + `label` is carried into the completion detail rather than only being set at + queue time. A finished job whose detail reads "Enrichment complete" tells an + operator nothing about which upload it belonged to, and the queued text that + did say so has been overwritten by then. + """ + nutrition_job_store.update(job_id, status="running") + + def progress_cb(done: int, total: int) -> None: + nutrition_job_store.update(job_id, processed=done, total=total) + + try: + result = nutrition_enrichment_service.enrich_all_products( + progress_cb=progress_cb, **enrich_kwargs) + nutrition_job_store.update( + job_id, status="done", + detail="Enrichment complete" + (" ({})".format(label) if label else ""), + result={ + "total_products": result.total_products, "verified": result.verified, + "partial": result.partial, "unavailable": result.unavailable, + "skipped_non_consumable": result.skipped_non_consumable, + "duration_seconds": result.duration_seconds, + "error_count": len(result.errors), "errors": result.errors[:20], + }, + ) + except Exception as e: # noqa: BLE001 + logger.exception("Nutrition enrichment job %s failed", job_id) + nutrition_job_store.update(job_id, status="failed", detail=str(e)) + + +def submit_enrichment_for_brands(brands: Iterable[str], *, source: str) -> Optional[str]: + """Queue scoring for freshly ingested brands. Returns the job id, or None. + + Returns None rather than raising for every "nothing to do" case, because + every caller is an upload handler finishing its work: an ingestion that + genuinely succeeded must not be reported as failed because the optional + scoring step could not start. + """ + if not settings.AUTO_ENRICH_ON_UPLOAD: + logger.info("Auto-enrichment disabled (AUTO_ENRICH_ON_UPLOAD=false); " + "%s ingested products are unscored until /enrich is run", source) + return None + + # dict.fromkeys, not set(): the brand list ends up in a job detail string an + # operator reads, and a set would reorder it differently on every run. + unique: List[str] = [b for b in dict.fromkeys(b.strip() for b in brands if b) if b] + if not unique: + return None + + label = "{}: {}".format(source, ", ".join(unique)) + try: + job = nutrition_job_store.create("enrich") + nutrition_job_store.update(job.job_id, detail="Queued after " + label) + run_in_background( + lambda: run_enrich_job( + job.job_id, + label=label, + brands=unique, + # WITHOUT THIS, NOTHING IS SCORED. `_brands_to_enrich` honours + # ACTIVE_BRANDS by default, and a brand that was just uploaded + # is almost never in it - the job would report success over an + # empty work list. + include_inactive=True, + skip_if_verified=True, + # The LLM summary is the slow, token-costing step and adds no + # health score. The admin /enrich job fills narratives in later. + generate_narrative=False, + ), + name="nutrition-autoenrich-{}".format(job.job_id[:8]), + ) + logger.info("Queued nutrition enrichment %s for %s after %s", + job.job_id, unique, source) + return job.job_id + except Exception as e: # noqa: BLE001 + logger.exception("Could not queue nutrition enrichment after %s: %s", source, e) + return None diff --git a/app/services/nutrition_data_service.py b/app/services/nutrition_data_service.py index a24a062..b0d4f05 100644 --- a/app/services/nutrition_data_service.py +++ b/app/services/nutrition_data_service.py @@ -52,6 +52,14 @@ MIN_MATCH_CONFIDENCE = 0.32 # Categories in this FMCG catalog that are never food (no nutrition # panel exists), so we skip the network call entirely instead of # generating noisy near-misses. +# +# SUPERSEDED by app.services.consumability. Kept as a name because it reads +# like public API, but it is no longer consulted: as a *substring* test over +# seventeen words it both under- and over-matched. It missed every category it +# was not literally spelled with - "Hair Care", "Skin Care", "Baby Care", +# "Personal Care", "Flowers" - which is how Pantene, Nyle and a Godrej mosquito +# spray ended up with health scores; and it matched "soap" inside *soapnut* +# (reetha), which is a real commodity. NON_FOOD_KEYWORDS = ( "soap", "detergent", "shampoo", "toothpaste", "cosmetic", "deodorant", "diaper", "sanitary", "cleaner", "disinfectant", "battery", "stationery", @@ -126,9 +134,44 @@ def _clean_title_for_search(title: str) -> str: return ' '.join(t.split()) +# Categories whose rows are loose commodities rather than packaged products. +# These are the canonical names from `category_registry.ALL_CATEGORIES`, so a +# row filed by the ingestion pipeline lands here without translation. +USDA_FIRST_CATEGORIES = frozenset({ + "fruits and vegetables", "fresh herbs and greens", "fish and seafood", + "eggs", "dry fruits and nuts", +}) + + +def _prefers_usda(title: str, category: str) -> bool: + """True when this row is a loose commodity USDA is the better source for. + + Category first, because that is what the pipeline actually assigns. Falling + back to the commodity lexicon catches produce misfiled under "General" or + one of the bare-numeric categories left by a bad import. + """ + from app.services.category_registry import _normalize + from app.services.generic_products import canonical_category + + if _normalize(category) in USDA_FIRST_CATEGORIES: + return True + detected = canonical_category(title or "") + return bool(detected) and _normalize(detected) in USDA_FIRST_CATEGORIES + + def _looks_non_food(title: str, category: str) -> bool: - text = f"{title} {category}".lower() - return any(kw in text for kw in NON_FOOD_KEYWORDS) + """True only when the product is POSITIVELY not food. + + Delegates to `consumability.is_non_consumable`, which is a three-state + classifier: a product it cannot place returns False here, so an unknown + product is still looked up rather than silently skipped. That is the + opposite of what a `not is_consumable()` would do, and it is deliberate - + this gate decides whether to make a network call, not whether to delete + anything. + """ + from app.services.consumability import is_non_consumable + + return is_non_consumable(category, title) def _search_openfoodfacts(query: str, brand: str = "", category: str = "", max_results: int = 5) -> List[dict]: @@ -308,11 +351,33 @@ def fetch_verified_nutrition(brand: str, title: str, category: str = "") -> Dict now_iso = datetime.now(timezone.utc).isoformat() unavailable = {"data_status": "unavailable", "fetched_at": now_iso} - if not USE_OPEN_FACTS: - return unavailable if _looks_non_food(title, category): return unavailable + # Loose commodities go to USDA first. Open Food Facts catalogues PACKAGED + # products; asked about a raw banana it returns whatever carton mentions + # one, and `_match_confidence` then attaches that product's numbers at a + # score above the 0.32 floor. USDA publishes laboratory analyses of the raw + # commodity itself, per 100 g of edible portion. + # + # Routed by CATEGORY, not by brand. `Own Products` is the bucket for every + # unbranded row, and it holds "Toor Dhal 1kg", "Sugar 1kg" and "Salt 1kg" + # alongside the produce - OFF has far better Indian-market data for those, + # so a brand test would send them to the wrong source. + if _prefers_usda(title, category): + from app.services.nutrition_usda_service import fetch_verified_nutrition_usda + + usda = fetch_verified_nutrition_usda(title, category) + if usda.get("data_status") != "unavailable": + return usda + # Falls through to OFF, which occasionally does know a commodity USDA + # does not. The reverse is never done: a branded packaged product must + # never be handed a raw-commodity number, so `_prefers_usda` is the only + # door into the USDA path. + + if not USE_OPEN_FACTS: + return unavailable + candidates = _search_openfoodfacts(query=title, brand=brand, category=category) if not candidates: candidates = _search_openfoodfacts(query=title) @@ -450,6 +515,14 @@ def fetch_verified_nutrition_by_barcode( if not USE_OPEN_FACTS or not (barcode or "").strip(): return unavailable + # The edibility gate the forward path has always had, and this one never + # did. A shampoo carries a barcode like anything else, and Open Beauty + # Facts knows plenty of them, so without this a personal-care product + # acquires a full nutrition row at match_confidence 0.95 - a HIGHER + # confidence than anything the name search can produce. + if _looks_non_food(title, category): + return unavailable + # Imported here rather than at module scope: this module is reached during # nutrition enrichment, and the barcode package pulls in tenacity plus the # whole source cascade. A local import keeps that off the path of the diff --git a/app/services/nutrition_db.py b/app/services/nutrition_db.py index 21c8623..e3f6b0d 100644 --- a/app/services/nutrition_db.py +++ b/app/services/nutrition_db.py @@ -175,6 +175,26 @@ CREATE TABLE IF NOT EXISTS nutrition_healthy_alternatives ( computed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE INDEX IF NOT EXISTS idx_nutrition_alt_source ON nutrition_healthy_alternatives(brand, image_id, rank); + +-- The consumable/non-consumable verdict, persisted at the point +-- `consumability.classify_edibility` already runs during enrichment. +-- +-- WHY A COLUMN AND NOT A PYTHON FILTER: `GET /api/nutrition/health-scores` +-- claims to list "consumable food products", and that claim has to be made in +-- the WHERE clause or the `total` it returns will not match the rows it +-- returns. Filtering a fetched page in Python would break both the count and +-- the paging. +-- +-- WHY NOT JUST TRUST THE WRITE GATE: the gate refuses NON_CONSUMABLE, but +-- UNKNOWN is neither refused nor confirmed - the purge audit found 33 such +-- rows. NULL (written before this column existed) and 'unknown' are therefore +-- both excluded by default: an unclassified row is not evidence of food. +-- +-- These two statements, not the CREATE TABLE above, are what actually add the +-- column to a database where nutrition_facts already exists. IF NOT EXISTS on +-- both keeps `ensure_nutrition_schema()` idempotent on every API startup. +ALTER TABLE nutrition_facts ADD COLUMN IF NOT EXISTS edibility TEXT; +CREATE INDEX IF NOT EXISTS idx_nutrition_facts_edibility ON nutrition_facts(edibility); """ @@ -240,6 +260,7 @@ _FACT_COLUMNS = [ "sodium_mg", "potassium_mg", "calcium_mg", "iron_mg", "magnesium_mg", "zinc_mg", "vitamin_a_mcg", "vitamin_c_mg", "vitamin_d_mcg", "vitamin_e_mg", "omega_3_g", "omega_6_g", "extended_nutrients", "per_serving", "ingredients_text", "off_nutriscore", "fetched_at", + "edibility", ] @@ -480,6 +501,175 @@ def query_products( conn.close() +# --------------------------------------------------------------------------- +# Health-score listing (GET /api/nutrition/health-scores) +# --------------------------------------------------------------------------- +# +# Deliberately NOT built on `query_products` above. That function backs five +# live endpoints plus the analytics and recommendation payloads, several of +# which splat its rows into raw dicts - widening its SELECT would leak new keys +# into responses nobody asked to change. The two share nothing but the tables. +# +# The listing and the count are built from the SAME `_health_score_filters`, +# because a `total` that disagrees with the rows beside it is worse than no +# total at all. + +CONSUMABLE = "consumable" +NON_CONSUMABLE = "non_consumable" + +# Table-qualified, unlike `_SORTABLE_COLUMNS`. `data_status` exists on both +# tables, so leaving the sort column bare is one added option away from an +# ambiguous-column error at runtime. +_HEALTH_SORT_COLUMNS = { + "health_score": "i.health_score", "nutrition_score": "i.nutrition_score", + "protein": "f.protein_g", "fiber": "f.dietary_fiber_g", "sugar": "f.total_sugar_g", + "sodium": "f.sodium_mg", "calcium": "f.calcium_mg", "iron": "f.iron_mg", + "vitamin_c": "f.vitamin_c_mg", "calories": "f.calories_kcal", + "product_name": "f.product_name", "brand": "f.brand", "category": "f.category", +} + +_HEALTH_SCORE_PROJECTION = """ + f.brand, f.image_id, f.product_name, f.category, f.edibility, + f.data_status, f.data_source, f.source_url, + f.calories_kcal, f.protein_g, f.dietary_fiber_g, f.total_sugar_g, f.sodium_mg, + i.nutrition_score, i.health_score, i.scoring_version, i.diet_tags, i.allergens +""" + + +def _health_score_filters( + brand: Optional[str] = None, + category: Optional[str] = None, + min_score: Optional[float] = None, + max_score: Optional[float] = None, + include_unknown: bool = False, +) -> tuple: + """The single source of the WHERE clause for both the listing and its count.""" + # `i.health_score IS NOT NULL` is stated OUTRIGHT, not derived from the sort + # column the way `query_products` derives it. The endpoint promises "only + # scored products"; that promise must not quietly change when a caller sorts + # by protein instead. + where = ["i.health_score IS NOT NULL", "f.data_status != 'unavailable'"] + params: List[Any] = [] + + if include_unknown: + # Still never non-consumable. IS DISTINCT FROM (not !=) so NULL, which + # is what every row written before the edibility column carries, counts + # as unknown rather than dropping out of a NULL comparison. + where.append("f.edibility IS DISTINCT FROM %s") + params.append(NON_CONSUMABLE) + else: + where.append("f.edibility = %s") + params.append(CONSUMABLE) + + if brand: + where.append("f.brand = %s") + params.append(brand) + if category: + where.append("f.category ILIKE %s") + params.append(f"%{category}%") + if min_score is not None: + where.append("i.health_score >= %s") + params.append(min_score) + if max_score is not None: + where.append("i.health_score <= %s") + params.append(max_score) + + return " AND ".join(where), params + + +def count_health_scores(**filters: Any) -> int: + """Total matching rows, for the pagination envelope.""" + conn = _connect() + if not conn: + return 0 + where_clause, params = _health_score_filters(**filters) + try: + with conn, conn.cursor() as cur: + cur.execute( + f""" + SELECT COUNT(*) + FROM nutrition_facts f + JOIN nutrition_insights i ON i.brand = f.brand AND i.image_id = f.image_id + WHERE {where_clause} + """, + params, + ) + row = cur.fetchone() + return int(row[0]) if row else 0 + except Exception as e: # noqa: BLE001 + logger.error(f"count_health_scores failed: {e}") + return 0 + finally: + conn.close() + + +def query_health_scores( + sort_by: str = "health_score", + order: str = "desc", + limit: int = 100, + offset: int = 0, + **filters: Any, +) -> List[Dict[str, Any]]: + """One page of scored consumable products, newest scoring first by default.""" + conn = _connect() + if not conn: + return [] + where_clause, params = _health_score_filters(**filters) + col = _HEALTH_SORT_COLUMNS.get(sort_by, "i.health_score") + direction = "ASC" if order == "asc" else "DESC" + + # The (brand, image_id) tiebreaker is load-bearing, not cosmetic: it is the + # primary key, so it makes the ordering total. Without it Postgres is free + # to return tied health_scores in a different order on each call, and OFFSET + # paging then hands the caller the same product twice and skips another. + sql = f""" + SELECT {_HEALTH_SCORE_PROJECTION} + FROM nutrition_facts f + JOIN nutrition_insights i ON i.brand = f.brand AND i.image_id = f.image_id + WHERE {where_clause} + ORDER BY {col} {direction} NULLS LAST, f.brand ASC, f.image_id ASC + LIMIT %s OFFSET %s + """ + try: + with conn, _dict_cursor(conn) as cur: + cur.execute(sql, params + [limit, offset]) + rows = [dict(r) for r in cur.fetchall()] + return [_row_numeric(r, NUMERIC_FACT_COLUMNS + NUMERIC_INSIGHT_COLUMNS) for r in rows] + except Exception as e: # noqa: BLE001 + logger.error(f"query_health_scores failed: {e}") + return [] + finally: + conn.close() + + +HEALTH_SCORE_EXPORT_PAGE = 1000 + + +def iter_health_scores(sort_by: str = "health_score", order: str = "desc", **filters: Any): + """Every matching row, a page at a time - the CSV export path. + + Pages rather than materialising the whole result so memory stays flat as the + catalogue grows, and so no database connection is held open for the length + of the HTTP response. The total ordering in `query_health_scores` is what + makes paging safe here; a row inserted mid-export can still shift a later + page, which is acceptable for an export and is why the payload carries a + `generated_at`. + """ + offset = 0 + while True: + page = query_health_scores( + sort_by=sort_by, order=order, + limit=HEALTH_SCORE_EXPORT_PAGE, offset=offset, **filters, + ) + if not page: + return + for row in page: + yield row + if len(page) < HEALTH_SCORE_EXPORT_PAGE: + return + offset += HEALTH_SCORE_EXPORT_PAGE + + # --------------------------------------------------------------------------- # Similarity / alternatives caches # --------------------------------------------------------------------------- diff --git a/app/services/nutrition_enrichment_service.py b/app/services/nutrition_enrichment_service.py index 3baa7c5..17def6e 100644 --- a/app/services/nutrition_enrichment_service.py +++ b/app/services/nutrition_enrichment_service.py @@ -23,18 +23,28 @@ from dataclasses import dataclass, field from typing import Any, Callable, Dict, List, Optional from app.services import nutrition_data_service, nutrition_db, nutrition_scoring +from app.services.consumability import Edibility, classify_edibility, is_non_consumable from app.services.nutrition_narrative_service import generate_summary from app.services.vector_store import get_products_by_brand, list_available_brands logger = logging.getLogger(__name__) +# Returned by `enrich_one_product` for a product that is not food. Deliberately +# NOT "unavailable": that means "we looked and found nothing", and a caller +# retrying the unavailable rows should not keep re-asking about soap. The +# distinction is the same one `backfill_nutrition_from_barcodes` draws between +# a thin record and a rejected one. +SKIPPED_NON_CONSUMABLE = "skipped_non_consumable" + + @dataclass class EnrichmentResult: total_products: int = 0 verified: int = 0 partial: int = 0 unavailable: int = 0 + skipped_non_consumable: int = 0 errors: List[str] = field(default_factory=list) duration_seconds: float = 0.0 @@ -42,7 +52,17 @@ class EnrichmentResult: def enrich_one_product(brand: str, image_id: str, product_name: str, category: str, skip_if_verified: bool = False, generate_narrative: bool = True) -> str: """Runs the full pipeline for a single product. Returns the resulting - `data_status` ('verified' | 'partial' | 'unavailable').""" + `data_status` ('verified' | 'partial' | 'unavailable' | + 'skipped_non_consumable').""" + # The authoritative gate. `fetch_verified_nutrition` has one too, but this + # is the only place that knows (brand, image_id) and can therefore refuse to + # WRITE A ROW at all - which is what stops a shampoo appearing in + # `query_products`, the analytics leaderboards and the healthy-alternatives + # cache with a null score attached. + verdict = classify_edibility(category, product_name) + if verdict.edibility is Edibility.NON_CONSUMABLE: + return SKIPPED_NON_CONSUMABLE + if skip_if_verified: existing = nutrition_db.get_nutrition_facts(brand, image_id) if existing and existing.get("data_status") == "verified": @@ -53,6 +73,13 @@ def enrich_one_product(brand: str, image_id: str, product_name: str, category: s facts["image_id"] = image_id facts["product_name"] = product_name facts["category"] = category + # Persisted so the verdict is available to SQL. `GET /api/nutrition/ + # health-scores` has to filter on "is this food?" inside the query - a + # Python filter applied to a fetched page would make its `total` and its + # rows describe different populations. UNKNOWN is stored as such rather + # than rounded up to consumable: the classifier not deciding is a fact + # worth keeping, not a fact worth guessing. + facts["edibility"] = verdict.edibility.value nutrition_db.upsert_nutrition_facts(facts) scores = nutrition_scoring.compute_scores(facts) @@ -72,7 +99,10 @@ def enrich_one_product(brand: str, image_id: str, product_name: str, category: s "brand": brand, "image_id": image_id, "positive_insights": positive, "nutritional_cautions": cautions, "ai_summary": ai_summary, "diet_tags": diet_tags, "allergens": allergens, - "allergen_source": "openfoodfacts" if allergens else "unavailable", + # Derived from where the facts actually came from. Hardcoding + # "openfoodfacts" was right when OFF was the only source; with USDA in + # the cascade it would credit the wrong database. + "allergen_source": (facts.get("data_source") or "unavailable") if allergens else "unavailable", "data_status": facts["data_status"], } if scores: @@ -82,27 +112,76 @@ def enrich_one_product(brand: str, image_id: str, product_name: str, category: s return facts["data_status"] +def _brands_to_enrich(brands: Optional[List[str]], include_inactive: bool) -> List[str]: + """Which brand display names this run covers. + + `list_available_brands()` honours ACTIVE_BRANDS, which is right for + everything the app serves and wrong for a catalogue-wide backfill: with + ACTIVE_BRANDS set to four brands, enrichment physically cannot reach + Britannia or Parle, and their products stay permanently unscored. + + Widening is therefore an explicit ARGUMENT, never an environment mutation - + the running API keeps its own scoping either way. + """ + if brands: + return [b.strip() for b in brands if b and b.strip()] + if not include_inactive: + return list_available_brands() + + # Same escape hatch repair_brand_images._brand_tables uses. + from app.services.vector_store import ( + _connect, _list_brand_table_suffixes, display_name_for_suffix, + ) + + conn = _connect() + if conn is None: + logger.warning("No database connection; falling back to active brands only") + return list_available_brands() + try: + with conn.cursor() as cur: + suffixes = sorted(set(_list_brand_table_suffixes(cur, include_inactive=True))) + return [display_name_for_suffix(s) for s in suffixes] + finally: + conn.close() + + def enrich_all_products( skip_if_verified: bool = True, generate_narrative: bool = True, progress_cb: Optional[Callable[[int, int], None]] = None, max_products: Optional[int] = None, + brands: Optional[List[str]] = None, + include_inactive: bool = False, + categories: Optional[List[str]] = None, ) -> EnrichmentResult: - """Iterates every product across every brand table (via + """Iterates products across brand tables (via `vector_store.list_available_brands` / `get_products_by_brand` - the exact same source of truth the catalog UI reads from) and runs - the pipeline on each.""" + the pipeline on each. + + Non-consumables are dropped while the work list is built, not inside the + loop, so `total_products` and the progress percentage describe the work + actually being done. `enrich_one_product` re-checks anyway: Dagster calls it + directly and never comes through here. + """ start = time.time() result = EnrichmentResult() - brands = list_available_brands() + wanted_categories = {c.strip().lower() for c in categories} if categories else None + all_products: List[Dict[str, Any]] = [] - for brand in brands: + for brand in _brands_to_enrich(brands, include_inactive): for p in get_products_by_brand(brand): + category = p.get("category") + name = p.get("title") or p.get("product_name") + if wanted_categories and (category or "").strip().lower() not in wanted_categories: + continue + if is_non_consumable(category, name or ""): + result.skipped_non_consumable += 1 + continue all_products.append({ "brand": brand, "image_id": p.get("image_id"), - "product_name": p.get("title") or p.get("product_name"), - "category": p.get("category"), + "product_name": name, "category": category, }) if max_products: all_products = all_products[:max_products] @@ -121,6 +200,11 @@ def enrich_all_products( result.verified += 1 elif status == "partial": result.partial += 1 + elif status == SKIPPED_NON_CONSUMABLE: + # Only reachable if the pre-filter and the per-product gate + # disagree; counted honestly rather than folded into + # "unavailable", which would read as "we looked and found none". + result.skipped_non_consumable += 1 else: result.unavailable += 1 except Exception as e: # noqa: BLE001 @@ -132,7 +216,8 @@ def enrich_all_products( result.duration_seconds = round(time.time() - start, 1) logger.info( f"Nutrition enrichment complete: {result.verified} verified, {result.partial} partial, " - f"{result.unavailable} unavailable of {result.total_products} products in {result.duration_seconds}s" + f"{result.unavailable} unavailable of {result.total_products} products " + f"({result.skipped_non_consumable} non-consumable skipped) in {result.duration_seconds}s" ) return result diff --git a/app/services/nutrition_usda_service.py b/app/services/nutrition_usda_service.py new file mode 100644 index 0000000..36d7e71 --- /dev/null +++ b/app/services/nutrition_usda_service.py @@ -0,0 +1,308 @@ +""" +Verified nutrition for fresh produce, from USDA FoodData Central. + +WHY OPEN FOOD FACTS COULD NOT DO THIS +------------------------------------- +`nutrition_data_service` is built on Open Food Facts, which catalogues PACKAGED +products photographed front-of-pack. It has no entry for a raw apple. Asked for +one it answers with whatever carton mentions the word, and the fuzzy name match +then attaches that product's numbers. Measured on the live catalogue: all 159 +rows of `brand_own_products` - every fruit, vegetable, green, fish and egg the +shop sells loose - had NO nutrition at all, no benefits and no health score. + +USDA FoodData Central is the other half of the problem. Its Foundation Foods +and SR Legacy datasets are laboratory analyses of raw commodities, published +per 100 g of edible portion, which is exactly the shape this schema wants. + +WHAT THIS MODULE GUARANTEES +--------------------------- +It returns THE SAME DICT SHAPE as `fetch_verified_nutrition`, deliberately, so +`nutrition_db.upsert_nutrition_facts` writes it unchanged and the acquisition +paths cannot drift apart. There is a test asserting the key sets match, the +same one that already guards the barcode path. + +It never estimates. A nutrient USDA does not report stays `None` all the way to +the API response - not zero, not a category average. A commodity absent from +`produce_reference` returns the `unavailable` shell. + +THREE RULES IN THE MAPPING LAYER, EACH FOR A REASON +--------------------------------------------------- +1. THE UNIT IS ASSERTED, NEVER ASSUMED. USDA keys nutrients by integer id, and + the same id has been served in different units across dataset revisions and + between the bulk download (which writes "µg") and the live API (which writes + "UG"). A sodium value silently read as grams and multiplied by 1000 would be + off by a factor of a million and would still look plausible. When the unit is + not one this module recognises for that nutrient, the value is dropped and a + warning logged. + +2. IU IS REFUSED RATHER THAN CONVERTED. The IU-to-microgram factor for vitamin A + depends on the vitamer (retinol vs beta-carotene), so any single factor is an + estimate. Estimates are what this module exists not to produce. + +3. BOTH RESPONSE SHAPES ARE HANDLED. `/food/{id}` nests the nutrient as + `{"nutrient": {...}, "amount": ...}`; `/foods/search` flattens it to + `{"nutrientId": ..., "unitName": ..., "value": ...}`; the pinned snapshot + stores a third, trimmed shape. One accessor reads all three. + +OFFLINE BY DEFAULT +------------------ +The snapshot in `data/usda_snapshot.json` holds the raw USDA record for every id +the curated table references, taken from the bulk download - no API key, no rate +limit. The live API is consulted only for an id the snapshot lacks, and only +when `USDA_FDC_API_KEY` is set. So a normal enrichment run makes zero USDA +calls, and the tests are hermetic without stubbing anything. + +The snapshot stores the nutrient list RAW rather than pre-mapped, so the mapping +rules above are exercised identically online and offline. A snapshot of finished +values would mean the offline tests tested nothing. +""" +from __future__ import annotations + +import json +import logging +from datetime import datetime, timezone +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, List, Optional + +import requests + +from app.infrastructure.settings import ( + REQUEST_TIMEOUT_SECONDS, USDA_FDC_API_KEY, USE_USDA_FDC, +) +from app.services import produce_reference + +logger = logging.getLogger(__name__) + +USDA_HOST = "api.nal.usda.gov" +USDA_DATA_SOURCE = "usda_fdc" + +# A curated identifier is not a fuzzy match, and it is not certainty either: +# choosing "Tomatoes, red, ripe, raw, year round average" for a grocer's +# "Tomato" is a judgement about which preparation is meant. Recorded well clear +# of the 0.32 floor the OFF name search can produce, and below the 0.95 a +# barcode earns. +USDA_MATCH_CONFIDENCE = 0.9 + +_SNAPSHOT_FILE = Path(__file__).parent / "data" / "usda_snapshot.json" + +# USDA nutrient id -> (our column, units this module will accept). +# Ids are stable across FoodData Central releases; units are not, hence rule 1. +_GRAMS = ("G", "g") +_MG = ("MG", "mg") +_UG = ("UG", "ug", "µg", "MCG_RE", "mcg") +_KCAL = ("KCAL", "kcal") + +_NUTRIENT_MAP: Dict[int, tuple] = { + 1008: ("calories_kcal", _KCAL), # Energy (kcal). 1062 is kJ - different id. + 1003: ("protein_g", _GRAMS), + 1005: ("carbohydrates_g", _GRAMS), # by difference + 2000: ("total_sugar_g", _GRAMS), + 1235: ("added_sugar_g", _GRAMS), + 1079: ("dietary_fiber_g", _GRAMS), + 1004: ("total_fat_g", _GRAMS), + 1258: ("saturated_fat_g", _GRAMS), + 1257: ("trans_fat_g", _GRAMS), + 1253: ("cholesterol_mg", _MG), + 1093: ("sodium_mg", _MG), + 1092: ("potassium_mg", _MG), + 1087: ("calcium_mg", _MG), + 1089: ("iron_mg", _MG), + 1090: ("magnesium_mg", _MG), + 1095: ("zinc_mg", _MG), + 1106: ("vitamin_a_mcg", _UG), # RAE. The IU form (1104) is refused. + 1162: ("vitamin_c_mg", _MG), + 1114: ("vitamin_d_mcg", _UG), # D2 + D3 + 1109: ("vitamin_e_mg", _MG), # alpha-tocopherol + 1404: ("omega_3_g", _GRAMS), # 18:3 n-3 (ALA) + 1316: ("omega_6_g", _GRAMS), # 18:2 n-6 (LA) +} + +# Nutrients worth keeping that have no flat column, mirroring +# `nutrition_data_service._EXTENDED_NUTRIENT_KEYS`. +_EXTENDED_MAP: Dict[int, tuple] = { + 1165: ("Vitamin B1 (Thiamine)", _MG), + 1166: ("Vitamin B2 (Riboflavin)", _MG), + 1175: ("Vitamin B6", _MG), + 1177: ("Vitamin B9 (Folate)", _UG), + 1178: ("Vitamin B12", _UG), + 1167: ("Vitamin B3 (Niacin)", _MG), + 1091: ("Phosphorus", _MG), + 1100: ("Iodine", _UG), +} + +# Ids deliberately ignored: an International Unit cannot be converted to a mass +# without knowing the vitamer, so it is dropped rather than guessed. +_REFUSED_IU_IDS = frozenset({1104, 1110}) + + +def _unit_label(accepted: tuple) -> str: + if accepted is _MG: + return "mg" + if accepted is _UG: + return "mcg" + if accepted is _KCAL: + return "kcal" + return "g" + + +@lru_cache(maxsize=1) +def _snapshot() -> Dict[str, Any]: + if not _SNAPSHOT_FILE.exists(): + logger.warning("USDA snapshot missing at %s", _SNAPSHOT_FILE) + return {} + return json.loads(_SNAPSHOT_FILE.read_text(encoding="utf-8")).get("foods", {}) + + +def _iter_nutrients(food: Dict[str, Any]): + """Yield (nutrient_id, unit_name, amount) from any of the three shapes.""" + for entry in food.get("nutrients") or food.get("foodNutrients") or []: + if "nutrient" in entry: # /food/{id} + n = entry["nutrient"] or {} + yield n.get("id"), n.get("unitName"), entry.get("amount") + elif "nutrientId" in entry: # /foods/search + yield entry.get("nutrientId"), entry.get("unitName"), entry.get("value") + else: # pinned snapshot + yield entry.get("id"), entry.get("unit"), entry.get("amount") + + +def _build_fields(food: Dict[str, Any]) -> Dict[str, Any]: + """Flat per-100g columns. USDA already publishes per 100 g of edible + portion, so there is no scaling here - only unit checking.""" + out: Dict[str, Any] = {name: None for name, _ in _NUTRIENT_MAP.values()} + for nid, unit, amount in _iter_nutrients(food): + if nid not in _NUTRIENT_MAP or amount is None: + continue + column, accepted = _NUTRIENT_MAP[nid] + if unit not in accepted: + logger.warning( + "USDA nutrient %s for %s came back in %r, expected one of %r - " + "dropping rather than rescaling a value that would look plausible", + nid, food.get("description"), unit, accepted, + ) + continue + try: + out[column] = round(float(amount), 3) + except (TypeError, ValueError): + continue + return out + + +def _build_extended(food: Dict[str, Any]) -> Dict[str, Any]: + out: Dict[str, Any] = {} + for nid, unit, amount in _iter_nutrients(food): + if nid not in _EXTENDED_MAP or amount is None: + continue + label, accepted = _EXTENDED_MAP[nid] + if unit not in accepted: + continue + try: + out[label] = {"value": round(float(amount), 3), "unit": _unit_label(accepted)} + except (TypeError, ValueError): + continue + return out + + +def _portion(food: Dict[str, Any]) -> tuple: + portions = food.get("portions") or food.get("foodPortions") or [] + for p in portions: + grams = p.get("gram_weight") or p.get("gramWeight") + if not grams: + continue + label = (p.get("description") or p.get("portionDescription") + or (p.get("measureUnit") or {}).get("name") or "") + try: + return round(float(grams), 3), (label or None) + except (TypeError, ValueError): + continue + return None, None + + +def _fetch_live(fdc_id: int) -> Optional[Dict[str, Any]]: + """The live API, used only for an id the snapshot lacks.""" + if not USDA_FDC_API_KEY: + return None + try: + resp = requests.get( + f"https://{USDA_HOST}/fdc/v1/food/{fdc_id}", + params={"api_key": USDA_FDC_API_KEY}, + timeout=REQUEST_TIMEOUT_SECONDS, + ) + if resp.status_code != 200: + logger.debug("USDA live lookup for %s returned %s", fdc_id, resp.status_code) + return None + return resp.json() + except Exception as e: # noqa: BLE001 + logger.debug("USDA live lookup failed for %s: %s", fdc_id, e) + return None + + +def get_food(fdc_id: int) -> Optional[Dict[str, Any]]: + """The raw USDA record: snapshot first, live API only as a fallback.""" + food = _snapshot().get(str(fdc_id)) + if food: + return food + return _fetch_live(fdc_id) + + +def fetch_verified_nutrition_usda( + product_name: str, category: str = "", fdc_id: Optional[int] = None, +) -> Dict[str, Any]: + """Nutrition for one loose commodity, keyed by the curated FDC id. + + Returns the same dict shape as + `nutrition_data_service.fetch_verified_nutrition`, including the `off_*` + keys - as empty rather than absent. USDA has no notion of a Nutri-Score or + an ingredient-derived vegan flag, and `[]` says "USDA told us nothing about + this", which is true and is what `classify_diet_tags` reads. + """ + now_iso = datetime.now(timezone.utc).isoformat() + unavailable = {"data_status": "unavailable", "fetched_at": now_iso} + + if not USE_USDA_FDC: + return unavailable + + if fdc_id is None: + fdc_id = produce_reference.usda_fdc_id(product_name) + if not fdc_id: + return unavailable + + food = get_food(fdc_id) + if not food: + return unavailable + + per_100g = _build_fields(food) + if not any(v is not None for v in per_100g.values()): + return unavailable + + serving_g, serving_label = _portion(food) + per_serving: Dict[str, Any] = {} + if serving_g: + # Arithmetic on given values, not an estimate of them: the same + # reasoning that lets a line total be computed from a unit price. + factor = serving_g / 100.0 + per_serving = {k: round(v * factor, 3) + for k, v in per_100g.items() if v is not None} + + result: Dict[str, Any] = { + "data_status": "verified" if per_100g.get("calories_kcal") is not None else "partial", + "data_source": USDA_DATA_SOURCE, + "source_ref": str(fdc_id), + "source_url": f"https://fdc.nal.usda.gov/food-details/{fdc_id}", + "match_confidence": USDA_MATCH_CONFIDENCE, + "serving_size_g": serving_g, + "serving_size_label": serving_label, + "extended_nutrients": _build_extended(food), + "per_serving": per_serving, + # A raw commodity has no ingredient list and no declared allergens. + # Empty, not absent, so the dict shape matches the OFF path exactly. + "ingredients_text": None, + "off_nutriscore": None, + "allergens": [], + "off_labels_tags": [], + "off_ingredients_analysis_tags": [], + "off_categories_tags": [], + "fetched_at": now_iso, + } + result.update(per_100g) + return result diff --git a/app/services/produce_reference.py b/app/services/produce_reference.py new file mode 100644 index 0000000..e6d179a --- /dev/null +++ b/app/services/produce_reference.py @@ -0,0 +1,140 @@ +""" +The curated table behind fresh-produce nutrition and fresh-produce images. + +WHY ONE TABLE FOR BOTH +---------------------- +The two defects it fixes have the same cause: nothing in the system knew what a +row of `brand_own_products` actually IS. "Apple" was a string, so image search +appended a hint meant for confectionery brands and returned juice cartons, and +nutrition search asked a packaged-goods database about a fruit and got nothing. +One hand-checked line per commodity answers both questions, so a wrong entry is +corrected in one place rather than two. + +WHY THE IDs ARE PINNED AND NOT SEARCHED +--------------------------------------- +Free-text search against USDA repeats the same class of error one layer down. +Measured live while building this table: + + "curry leaves" -> Drumstick leaves, raw a different plant + "snake gourd" -> Gourd, dishcloth a different species + "sweet lime" -> Sweet potatoes, raw not remotely the same thing + "mosambi" -> Wasabi, root, raw + +So every `usda_fdc_id` was resolved once against the USDA bulk download, its +returned description was read by a person, and the id was written down. Four +were wrong on the first pass and were corrected: `Peach` had matched *Pears, +raw*; `Sweet Potato` had matched *Sweet Potato puffs, frozen*; `Tender Coconut` +had matched coconut meat rather than water; `Apple` had matched the +without-skin entry. + +WHY SOME ENTRIES HAVE NO USDA ID +-------------------------------- +Because USDA has no equivalent, and the module's contract is to say +"unavailable" rather than substitute a near neighbour. Amla is *Phyllanthus +emblica* and USDA's "Gooseberries, raw" is *Ribes*, a different genus with +different vitamin C by an order of magnitude. Also absent: mosambi/sweet lime, +dragon fruit, ash gourd, snake gourd, ivy gourd, cluster beans, baby corn, +curry leaves, methi leaves, thulasi, sorrel, agathi keerai, ponnanganni keerai, +pomfret, paneer and khoya. Those rows get an image and no nutrition, which is +the honest answer. + +The 14 Flowers rows deliberately carry an image and no USDA id at all: they are +sold loose beside the vegetables and reach this table the same way, but +`consumability.is_non_consumable("Flowers")` is True and they are never scored. + +IMAGES +------ +`image_source` says where the URL came from and is part of the record, not +decoration: + + wikipedia_lead the lead image of the English Wikipedia article, which is + a curated choice by that article's editors + wikimedia_commons hand-picked, because the article's lead image is a + 19th-century botanical plate (Koehler, Blanco) rather than + a photograph - 15 items, each named in `image_note` + +Search terms were resolved to articles by BOTANICAL name wherever the common +name is ambiguous, which is the fix for the two worst rows in the live +catalogue: "Palak" had been a photograph of a politician and "Thulasi" a +photograph of an actress, because both are also common personal names. +""" +from __future__ import annotations + +import json +import re +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, List, Optional + +_DATA_FILE = Path(__file__).parent / "data" / "produce_reference.json" + +# Pack sizes and bare quantities never identify a commodity. Mirrors +# `generic_products._SIZE_RE`, bounded to real units for the same reason: an +# unbounded `[a-z]*` after a number ate the following word. +_UNITS = ( + "kg|kgs|g|gm|gms|gram|grams|mg|ml|l|ltr|ltrs|litre|litres|liter|liters" + "|pc|pcs|piece|pieces|pack|packs|pkt|n|no|nos|x|dozen|bunch|bundle" +) +_SIZE_RE = re.compile( + r"\b\d+(?:[.,]\d+)?\s*(?:" + _UNITS + r")\b|\b\d+(?:[.,]\d+)?\b", re.IGNORECASE +) +_NON_WORD = re.compile(r"[^a-z0-9]+") + + +def _normalize(name: str) -> str: + text = _SIZE_RE.sub(" ", (name or "").lower()) + return _NON_WORD.sub(" ", text).strip() + + +@lru_cache(maxsize=1) +def _reference() -> Dict[str, Dict[str, Any]]: + raw = json.loads(_DATA_FILE.read_text(encoding="utf-8")) + return {_normalize(k): {**v, "product_name": k} for k, v in raw.items()} + + +@lru_cache(maxsize=1) +def _raw_reference() -> Dict[str, Dict[str, Any]]: + return json.loads(_DATA_FILE.read_text(encoding="utf-8")) + + +def lookup(product_name: str) -> Optional[Dict[str, Any]]: + """The curated entry for a catalogue product name, or None. + + Falls back from the full name to progressively shorter leading phrases, so + a size variant or a varietal suffix still finds its base commodity: + + "Mango Alphonso 1kg" -> "mango alphonso" -> hit + "Mango Sindoora" -> "mango sindoora" -> miss -> "mango" -> hit + + Leading rather than trailing, because an Indian product name puts its head + noun first - the same reading `generic_products.canonical_category` takes. + """ + key = _normalize(product_name) + if not key: + return None + ref = _reference() + tokens = key.split() + for end in range(len(tokens), 0, -1): + hit = ref.get(" ".join(tokens[:end])) + if hit: + return hit + return None + + +def usda_fdc_id(product_name: str) -> Optional[int]: + entry = lookup(product_name) + return entry.get("usda_fdc_id") if entry else None + + +def image_url(product_name: str) -> Optional[str]: + entry = lookup(product_name) + return entry.get("image_url") if entry else None + + +def all_entries() -> Dict[str, Dict[str, Any]]: + """The table as authored, keyed by the catalogue product name.""" + return dict(_raw_reference()) + + +def known_product_names() -> List[str]: + return sorted(_raw_reference()) diff --git a/scripts/build_produce_contact_sheet.py b/scripts/build_produce_contact_sheet.py new file mode 100644 index 0000000..08c8d1a --- /dev/null +++ b/scripts/build_produce_contact_sheet.py @@ -0,0 +1,428 @@ +#!/usr/bin/env python3 +""" +Render every Own Products row as a before/after card, for review before writing. + +WHY A REVIEW STEP AND NOT A CONFIDENCE THRESHOLD +------------------------------------------------ +No automated filter can tell that `Zunaid_Ahmed_Palak.jpg` is a photograph of a +politician rather than a bunch of spinach, or that `Bitter_Gourd_Curry.jpg` is a +cooked dish rather than the vegetable. Both were the stored image for a live +product, both resolved to real image bytes, and both passed every check the +pipeline had. The only thing that catches them is a person looking. + +So this script writes nothing to the database. It renders what WOULD be written +and stops. `repair_brand_images.py --brands "Own Products" --apply` is the step +that writes, and it should only be run once these cards have been looked at. + +WHAT EACH CARD SHOWS +-------------------- + * the image currently stored, and whether it still resolves + * the curated replacement from `produce_reference` + * where that replacement came from, and for the 15 hand-picked ones, why + * the nutrition that will be attached, with its USDA source, or an explicit + note that there is none and why + +USAGE +----- + python -m scripts.build_produce_contact_sheet + python -m scripts.build_produce_contact_sheet --out sheet.html --no-probe + +`--no-probe` skips checking whether the stored URLs still resolve, which is the +slow part; without it the script makes one ranged GET per distinct stored URL. +`--embed` downloads every image and inlines it as a small data: URI, which is +required if the page is going to be published as an Artifact - that viewer's +content security policy blocks third-party image hosts, so a page of hotlinked +photographs renders as 318 empty boxes. +Read-only in every mode: it opens no write transaction. +""" +from __future__ import annotations + +import argparse +import html +import logging +import sys +import time +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from app.services import nutrition_scoring, produce_reference # noqa: E402 +from app.services.consumability import classify_edibility # noqa: E402 +from app.services.generic_products import OWN_PRODUCTS_BRAND # noqa: E402 +from app.services.nutrition_usda_service import ( # noqa: E402 + fetch_verified_nutrition_usda, +) +from app.services.vector_store import _connect, _sanitize_name # noqa: E402 + +logging.basicConfig(level=logging.INFO, format="%(message)s") +logger = logging.getLogger("contact_sheet") + +TABLE = f"brand_{_sanitize_name(OWN_PRODUCTS_BRAND)}" + + +def _rows() -> List[Dict[str, Any]]: + conn = _connect() + if conn is None: + logger.error("No database connection.") + return [] + try: + with conn.cursor() as cur: + cur.execute( + f"SELECT product_name, category, image_url FROM {TABLE} " + f"ORDER BY category, product_name" + ) + return [{"product_name": r[0], "category": r[1], "image_url": r[2]} + for r in cur.fetchall()] + finally: + conn.close() + + +# Thumbnails, not photographs. 318 full-size images would be ~40 MB and the +# Artifact ceiling is 16 MB; at 168px they are legible side by side, which is +# all this page asks of them, and the whole set fits in about 2 MB. +_THUMB_PX = 168 +_THUMB_QUALITY = 62 + + +def _thumb(url: str, cache: Dict[str, str]) -> str: + """`url` fetched, downscaled and returned as a data: URI, or "" on failure.""" + if not url: + return "" + if url in cache: + return cache[url] + + import base64 + from io import BytesIO + import requests + from PIL import Image + + data = "" + try: + resp = requests.get(url, headers={"User-Agent": _UA}, timeout=20) + if resp.status_code == 200 and resp.content: + img = Image.open(BytesIO(resp.content)) + img = img.convert("RGB") + img.thumbnail((_THUMB_PX, _THUMB_PX), Image.LANCZOS) + buf = BytesIO() + img.save(buf, format="JPEG", quality=_THUMB_QUALITY, optimize=True) + data = "data:image/jpeg;base64," + base64.b64encode(buf.getvalue()).decode() + except Exception: # noqa: BLE001 - a broken image is the thing being reported + data = "" + cache[url] = data + time.sleep(0.12) + return data + + +_UA = "nearle-catalogue/1.0 (produce image review sheet)" + + +def _probe(url: str, cache: Dict[str, bool]) -> bool: + if not url: + return False + if url in cache: + return cache[url] + from app.services.image_search import validate_image_url_live + try: + ok = bool(validate_image_url_live(url, timeout=8)) + except Exception: # noqa: BLE001 + ok = False + cache[url] = ok + time.sleep(0.1) + return ok + + +_CSS = """ +/* Palette is the greengrocer's, not the default warm-cream: a cool paper with a + faint green cast, deep leaf-black ink, and aubergine as the one accent - kept + clear of the green/amber/red that carry MEANING on this page. */ +:root{ + --paper:#eef1ec; --card:#ffffff; --line:#d8dfd6; --line-soft:#e8ede6; + --ink:#131c17; --ink-2:#3d4b43; --dim:#6a7a70; + --accent:#6b3a58; /* aubergine */ + --ok:#1a6b45; --ok-bg:#e3f1e9; + --warn:#8a5600; --warn-bg:#fbeed7; + --crit:#9c2b2b; --crit-bg:#fbe6e6; + --shadow:0 1px 2px rgba(19,28,23,.05); +} +@media (prefers-color-scheme:dark){ + :root:not([data-theme="light"]){ + --paper:#11150f; --card:#1a1f19; --line:#2e352c; --line-soft:#232a22; + --ink:#e8ece6; --ink-2:#b9c3ba; --dim:#8b968c; + --accent:#d99bc4; + --ok:#71d3a1; --ok-bg:#12301f; + --warn:#e8b761; --warn-bg:#33260d; + --crit:#f09393; --crit-bg:#331616; + --shadow:0 1px 2px rgba(0,0,0,.35); + } +} +:root[data-theme="dark"]{ + --paper:#11150f; --card:#1a1f19; --line:#2e352c; --line-soft:#232a22; + --ink:#e8ece6; --ink-2:#b9c3ba; --dim:#8b968c; + --accent:#d99bc4; + --ok:#71d3a1; --ok-bg:#12301f; + --warn:#e8b761; --warn-bg:#33260d; + --crit:#f09393; --crit-bg:#331616; + --shadow:0 1px 2px rgba(0,0,0,.35); +} + +*{box-sizing:border-box} +body{ + margin:0; background:var(--paper); color:var(--ink); + font:400 15px/1.55 "IBM Plex Sans","Segoe UI",system-ui,-apple-system,sans-serif; + -webkit-font-smoothing:antialiased; +} +.wrap{max-width:1500px;margin:0 auto} + +header{padding:38px 26px 26px;border-bottom:1px solid var(--line)} +h1{ + margin:0 0 8px; font-family:"Bricolage Grotesque","IBM Plex Sans",sans-serif; + font-weight:600; font-size:clamp(26px,3.4vw,38px); letter-spacing:-.025em; + text-wrap:balance; line-height:1.08; +} +.sub{color:var(--ink-2);font-size:14.5px;max-width:66ch;margin:0} +.sub strong{color:var(--ink);font-weight:600} + +.tally{display:flex;flex-wrap:wrap;gap:0;margin-top:22px; + border:1px solid var(--line);border-radius:10px;overflow:hidden;background:var(--card)} +.tally .t{padding:11px 18px;border-right:1px solid var(--line);flex:1 1 auto;min-width:132px} +.tally .t:last-child{border-right:0} +.tally b{display:block;font-family:"IBM Plex Mono",ui-monospace,monospace; + font-size:23px;font-weight:600;letter-spacing:-.02em;font-variant-numeric:tabular-nums} +.tally span{font-size:11.5px;color:var(--dim);text-transform:uppercase;letter-spacing:.07em} +.tally .t.crit b{color:var(--crit)} .tally .t.ok b{color:var(--ok)} + +h2{ + margin:38px 26px 13px; font-size:12.5px; font-weight:600; color:var(--dim); + text-transform:uppercase; letter-spacing:.13em; + display:flex; align-items:baseline; gap:10px; +} +h2::after{content:"";flex:1;height:1px;background:var(--line)} +h2 em{font-style:normal;font-family:"IBM Plex Mono",monospace;color:var(--dim); + font-size:12px;font-variant-numeric:tabular-nums} + +.grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(322px,1fr)); + gap:13px;padding:0 26px} + +/* The left rail encodes the row's actual state, so severity reads before any + text does: dead stored image, wrong-source image, or already fine. */ +.card{background:var(--card);border:1px solid var(--line);border-radius:11px; + overflow:hidden;box-shadow:var(--shadow);border-left:3px solid var(--line)} +.card.crit{border-left-color:var(--crit)} +.card.warn{border-left-color:var(--warn)} +.card.ok{border-left-color:var(--ok)} + +.name{padding:12px 14px 10px;font-weight:600;font-size:15.5px;letter-spacing:-.01em} +.pair{display:grid;grid-template-columns:1fr 1fr;gap:1px;background:var(--line-soft)} +.cell{background:var(--card);padding:10px} +.lbl{font-size:10px;text-transform:uppercase;letter-spacing:.09em;color:var(--dim); + margin-bottom:7px;display:flex;align-items:center;gap:6px;min-height:16px} +.ph{width:100%;aspect-ratio:1;object-fit:cover;border-radius:7px; + background:var(--line-soft);display:block} +.miss{width:100%;aspect-ratio:1;border-radius:7px;background:var(--line-soft); + display:flex;align-items:center;justify-content:center;color:var(--dim); + font-size:11.5px;text-align:center;padding:10px;line-height:1.4} + +.meta{padding:11px 14px 13px;font-size:12.5px;color:var(--ink-2); + border-top:1px solid var(--line-soft);display:flex;flex-direction:column;gap:5px} +.tag{display:inline-block;border-radius:5px;padding:2px 7px;font-size:10.5px; + font-weight:600;text-transform:uppercase;letter-spacing:.05em;white-space:nowrap} +.t-ok{background:var(--ok-bg);color:var(--ok)} +.t-crit{background:var(--crit-bg);color:var(--crit)} +.t-warn{background:var(--warn-bg);color:var(--warn)} +.nut{font-family:"IBM Plex Mono",ui-monospace,monospace;font-size:12px; + font-variant-numeric:tabular-nums;display:flex;align-items:center;gap:7px;flex-wrap:wrap} +code{font-family:"IBM Plex Mono",ui-monospace,monospace;font-size:11px; + background:var(--line-soft);padding:1.5px 5px;border-radius:4px;word-break:break-all; + color:var(--ink-2)} + +footer{margin-top:46px;padding:24px 26px 40px;border-top:1px solid var(--line); + color:var(--dim);font-size:13px;line-height:1.6} +footer b{color:var(--ink-2);font-weight:600} +a{color:var(--accent)} +@media (prefers-reduced-motion:reduce){*{animation:none!important;transition:none!important}} +""" + + +def _card(row: Dict[str, Any], probe_cache: Dict[str, bool], probe: bool, + thumbs: Dict[str, str] | None = None) -> str: + name = row["product_name"] or "" + entry = produce_reference.lookup(name) or {} + verdict = classify_edibility(row["category"], name) + consumable = verdict.edibility.value == "consumable" + + stored = row.get("image_url") or "" + stored_ok = _probe(stored, probe_cache) if (probe and stored) else None + proposed = entry.get("image_url") + + # The stored URL's HOST is itself a verdict. Open Beauty Facts is a + # cosmetics database and Open Products Facts a packaged-goods one; a raw + # banana photographed front-of-pack is the wrong SUBJECT even when the URL + # resolves perfectly, which no liveness probe can detect. + wrong_source = any(h in stored.lower() for h in + ("openbeautyfacts", "openproductsfacts", "openfoodfacts")) + + if thumbs is not None and stored and not _thumb(stored, thumbs): + stored_ok = False + if not stored or stored_ok is False: + severity, verdict_tag = "crit", 'dead' + elif wrong_source: + severity, verdict_tag = "warn", 'wrong source' + elif stored_ok: + severity, verdict_tag = "ok", 'resolves' + else: + severity, verdict_tag = "", "" + + def _img(url: str, empty: str) -> str: + if not url: + return f'
{empty}
' + src = _thumb(url, thumbs) if thumbs is not None else url + if not src: + return '
image did not load
' + return f'' + + left = _img(stored, "no image stored") + right = _img(proposed, "no curated image") + + bits = [] + if entry.get("image_source") == "wikimedia_commons": + bits.append('
hand-picked ' + "article lead image was a botanical plate
") + if entry.get("image_credit"): + bits.append(f'
{html.escape(str(entry["image_credit"]))}
') + + if not consumable: + bits.append('
not food ' + "no nutrition, by design
") + elif entry.get("usda_fdc_id"): + facts = fetch_verified_nutrition_usda(name, row["category"]) + scores = nutrition_scoring.compute_scores(facts) or {} + if facts.get("data_status") != "unavailable": + bits.append( + f'
health ' + f'{scores.get("health_score", "-")}' + f'{facts.get("calories_kcal")} kcal' + f'{facts.get("protein_g")} g protein' + f'{facts.get("dietary_fiber_g")} g fibre
') + bits.append(f'
USDA {facts["source_ref"]} ' + f'{html.escape(str(entry.get("usda_description", ""))[:52])}
') + else: + bits.append('
no nutrition ' + "USDA has no entry for this species
") + + return f"""
+
{html.escape(name)}
+
+
stored now {verdict_tag}
{left}
+
proposed
{right}
+
+
{"".join(bits)}
+
""" + + +def build(rows: List[Dict[str, Any]], probe: bool, embed: bool = False) -> str: + cache: Dict[str, bool] = {} + thumbs: Dict[str, str] | None = {} if embed else None + by_category: Dict[str, List[Dict[str, Any]]] = {} + for row in rows: + by_category.setdefault(row["category"] or "(uncategorised)", []).append(row) + + sections = [] + for category in sorted(by_category): + cards = [_card(row, cache, probe, thumbs) for row in by_category[category]] + logger.info(" %-26s %3d card(s)", category, len(by_category[category])) + sections.append( + f'

{html.escape(category)}{len(by_category[category])}

' + f'
{"".join(cards)}
') + + n_dead = n_wrong = n_food = n_scored = 0 + for row in rows: + entry = produce_reference.lookup(row["product_name"] or "") or {} + stored = row.get("image_url") or "" + if classify_edibility(row["category"], row["product_name"] or "") \ + .edibility.value == "consumable": + n_food += 1 + if entry.get("usda_fdc_id"): + n_scored += 1 + if not stored: + n_dead += 1 + elif thumbs is not None and not thumbs.get(stored): + # --embed already fetched every URL; a thumbnail that came back + # empty is the same evidence a liveness probe would have gathered. + n_dead += 1 + elif probe and not cache.get(stored): + n_dead += 1 + elif any(h in stored.lower() for h in + ("openbeautyfacts", "openproductsfacts", "openfoodfacts")): + n_wrong += 1 + + tally = [("t crit", n_dead, "dead images"), + ("t crit", n_wrong, "wrong subject"), + ("t ok", n_scored, "get a health score"), + ("t", len(rows) - n_food, "not food, unscored"), + ("t", len(rows), "products in all")] + tiles = "".join(f'
{v}{lbl}
' + for c, v, lbl in tally) + + return f"""Fresh Aisle Audit + + + + +
+
+

Every loose thing the shop sells, and the picture it currently shows

+

The {len(rows)} unbranded rows in brand_own_products, + each showing what is stored today beside what would replace it. + Nothing has been written to the database. The repair script + runs only after these have been looked at — because no automated check + can tell that the stored photo for Palak is a politician, or that + Bitter Gourd is a plate of curry.

+
{tiles}
+
+{"".join(sections)} + +
""" + + +def main() -> int: + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--out", default="produce_contact_sheet.html") + parser.add_argument("--no-probe", action="store_true", + help="skip checking whether the stored URLs still resolve") + parser.add_argument("--embed", action="store_true", + help="inline every image as a downscaled data: URI. Required " + "for publishing as an Artifact, whose CSP blocks " + "third-party image hosts.") + args = parser.parse_args() + + rows = _rows() + if not rows: + logger.error("No rows read from %s", TABLE) + return 1 + logger.info("Rendering %d rows from %s", len(rows), TABLE) + + page = build(rows, probe=not args.no_probe, embed=args.embed) + Path(args.out).write_text(page, encoding="utf-8") + logger.info("Wrote %s (%.1f MB)", args.out, len(page.encode("utf-8")) / 1e6) + logger.info("Nothing was written to the database.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/enrich_nutrition.py b/scripts/enrich_nutrition.py index 2bb86dc..be7f0f3 100644 --- a/scripts/enrich_nutrition.py +++ b/scripts/enrich_nutrition.py @@ -8,6 +8,16 @@ Usage: python scripts/enrich_nutrition.py --max-products 200 --no-narrative python scripts/enrich_nutrition.py --force # re-fetch even already-verified products + # Catalogue-wide, past ACTIVE_BRANDS. Without this the run cannot reach + # Britannia, Parle or ITC at all, because enrich_all_products reads through + # vector_store.list_available_brands(), which honours that filter. + python scripts/enrich_nutrition.py --include-inactive + python scripts/enrich_nutrition.py --brands "Britannia,Parle" + python scripts/enrich_nutrition.py --categories "Fruits & Vegetables,Eggs" + +Non-consumables (soap, shampoo, mosquito repellent, flowers) are skipped and +reported separately - see app/services/consumability.py. + Equivalent to POST /api/admin/nutrition-intelligence/enrich. """ from __future__ import annotations @@ -36,6 +46,13 @@ def main() -> None: parser.add_argument("--force", action="store_true", help="Re-fetch even products already marked 'verified'") parser.add_argument("--no-narrative", action="store_true", help="Skip the LLM narrative step (facts/scores/tags only)") parser.add_argument("--max-products", type=int, default=None, help="Cap the number of products processed (for a quick test run)") + parser.add_argument("--brands", default=None, + help="Comma-separated brand display names to enrich (default: whatever ACTIVE_BRANDS allows)") + parser.add_argument("--include-inactive", action="store_true", + help="Enrich every brand table, ignoring ACTIVE_BRANDS. Needed to reach " + "brands the app does not currently display.") + parser.add_argument("--categories", default=None, + help="Comma-separated categories to restrict the run to, e.g. 'Fruits & Vegetables,Eggs'") args = parser.parse_args() ensure_nutrition_schema() @@ -44,12 +61,16 @@ def main() -> None: generate_narrative=not args.no_narrative, progress_cb=_progress, max_products=args.max_products, + brands=[b for b in (args.brands or "").split(",") if b.strip()] or None, + include_inactive=args.include_inactive, + categories=[c for c in (args.categories or "").split(",") if c.strip()] or None, ) logger.info( f"Done in {result.duration_seconds}s - " f"{result.verified} verified, {result.partial} partial, {result.unavailable} unavailable " - f"of {result.total_products} total products" + f"of {result.total_products} total products " + f"({result.skipped_non_consumable} non-consumable skipped)" ) if result.errors: logger.warning(f"{len(result.errors)} errors (showing up to 10): {result.errors[:10]}") diff --git a/scripts/purge_non_consumable_nutrition.py b/scripts/purge_non_consumable_nutrition.py new file mode 100644 index 0000000..f28e127 --- /dev/null +++ b/scripts/purge_non_consumable_nutrition.py @@ -0,0 +1,491 @@ +#!/usr/bin/env python3 +""" +Remove nutrition rows belonging to products that are not food. + +WHY THIS EXISTS +--------------- +`app/services/consumability.py` now stops these rows being created. It cannot +remove the ones already there. Measured on the live catalogue: + + Colgate-Palmolive Palmolive Naturals Bath Soap health score present + Cavinkare Nyle, Cavinkare Nature's Hair Care health score present + P&G Pantene Hair Care health score present + Godrej Hit Spray Mosquito 291 kcal, health score present + +A shopper reading "health score 62" on a mosquito repellent has no way to know +the number is meaningless, and `nutrition_db.query_products` will happily rank +it against actual food. + +WHAT IT WILL NOT DELETE +----------------------- +* Anything `consumability` cannot positively identify as non-food. The + classifier is three-state on purpose and this script uses the POSITIVE test + `is_non_consumable`, never `not is_consumable`. An unrecognised product is + reported as UNDECIDED and left exactly where it is - a wrong deletion here is + unrecoverable except from the backup, a wrong retention is visible and + fixable. +* Orphans - nutrition rows whose (brand, image_id) is in no brand table. That + is a different defect (a deleted product, or a brand-name mismatch since + enrichment) and deleting them here would hide it. + +WHY IT RE-READS THE CATEGORY FROM THE BRAND TABLE +------------------------------------------------- +`nutrition_facts.category` is a copy taken at enrichment time +(`enrich_one_product`), so it can be stale relative to the product. The verdict +is taken against the brand table's current category and product name, which is +what the catalogue actually claims today. + +ORDER OF DELETION +----------------- +Children first - `nutrition_similar_products` and +`nutrition_healthy_alternatives` before `nutrition_insights` and +`nutrition_facts` - so an interrupted run never leaves a similarity edge +pointing at a fact row that no longer exists. + +Both cache tables are cleaned in BOTH DIRECTIONS. The inbound direction is the +one that gets forgotten: a shampoo can be some real food's nearest neighbour, +and that edge survives a naive purge, after which `get_similar_products` serves +a dangling reference. + +USAGE +----- + python -m scripts.purge_non_consumable_nutrition --audit + python -m scripts.purge_non_consumable_nutrition --audit --json + + python -m scripts.purge_non_consumable_nutrition --purge # dry run + python -m scripts.purge_non_consumable_nutrition --purge --apply + python -m scripts.purge_non_consumable_nutrition --restore data/nutrition_purge_backup_X.json --apply + +`--audit` is read-only by construction. `--purge` is a dry run until `--apply`, +and `--apply` writes a full backup of every affected row first. + +AFTERWARDS +---------- +The KNN index and KMeans clusters in `data/artifacts/*.joblib` were fitted with +the deleted rows still in them. Re-run the training step or the similar-products +cache will keep serving keys that no longer exist. The script prints this. +""" +from __future__ import annotations + +import argparse +import json +import logging +import sys +from dataclasses import dataclass, field +from datetime import datetime +from decimal import Decimal +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from app.infrastructure.settings import DB_HOST, DB_NAME # noqa: E402 +from app.services.consumability import ( # noqa: E402 + Edibility, classify_edibility, is_junk_category, +) +from app.services.vector_store import _connect, display_name_for_suffix # noqa: E402 + +logging.basicConfig(level=logging.INFO, format="%(message)s") +logger = logging.getLogger("purge_non_consumable_nutrition") + +DATA_DIR = Path(__file__).resolve().parents[1] / "data" + +# Children first. `nutrition_facts` last, so a crash mid-run leaves referential +# integrity intact rather than half-broken. +CACHE_TABLES = ( + ("nutrition_similar_products", ("brand", "image_id"), ("similar_brand", "similar_image_id")), + ("nutrition_healthy_alternatives", ("brand", "image_id"), ("alt_brand", "alt_image_id")), +) +CORE_TABLES = ("nutrition_insights", "nutrition_facts") + +Key = Tuple[str, str] + + +# --------------------------------------------------------------------------- +# Planning - pure, no database, so it is testable +# --------------------------------------------------------------------------- +@dataclass +class PurgePlan: + delete: List[Dict[str, Any]] = field(default_factory=list) + keep: List[Dict[str, Any]] = field(default_factory=list) + undecided: List[Dict[str, Any]] = field(default_factory=list) + orphans: List[Dict[str, Any]] = field(default_factory=list) + junk_categories: List[Dict[str, Any]] = field(default_factory=list) + + @property + def delete_keys(self) -> List[Key]: + return [(r["brand"], r["image_id"]) for r in self.delete] + + +def plan_purge( + nutrition_rows: List[Dict[str, Any]], + catalogue: Dict[Key, Dict[str, Any]], +) -> PurgePlan: + """Decide the fate of every `nutrition_facts` row. + + `nutrition_rows` needs only brand, image_id, product_name, category and + data_status. `catalogue` is (brand, image_id) -> the brand table's current + product_name and category, which is the authority - see the module + docstring on why the fact row's own copy is not trusted. + """ + plan = PurgePlan() + for row in nutrition_rows: + key = (row.get("brand") or "", row.get("image_id") or "") + source = catalogue.get(key) + if source is None: + plan.orphans.append({**row, "reason": "no matching row in any brand table"}) + continue + + category = source.get("category") or "" + name = source.get("product_name") or row.get("product_name") or "" + if is_junk_category(category): + plan.junk_categories.append({**row, "category": category, "product_name": name}) + + verdict = classify_edibility(category, name) + entry = { + **row, + "category": category, + "product_name": name, + "reason": verdict.reason, + "signal": verdict.signal, + } + if verdict.edibility is Edibility.NON_CONSUMABLE: + plan.delete.append(entry) + elif verdict.edibility is Edibility.CONSUMABLE: + plan.keep.append(entry) + else: + plan.undecided.append(entry) + return plan + + +# --------------------------------------------------------------------------- +# Reading +# --------------------------------------------------------------------------- +def _catalogue_index(cur) -> Dict[Key, Dict[str, Any]]: + """(brand, image_id) -> current product_name and category. + + The brand key is `display_name_for_suffix(...)`, which is the string + `enrich_one_product` was called with, so it is the correct join key. + """ + cur.execute( + "SELECT table_name FROM information_schema.tables " + "WHERE table_schema='public' AND table_name LIKE 'brand_%' ORDER BY table_name" + ) + index: Dict[Key, Dict[str, Any]] = {} + for (table,) in cur.fetchall(): + cur.execute( + "SELECT column_name FROM information_schema.columns WHERE table_name=%s", + (table,), + ) + cols = {r[0] for r in cur.fetchall()} + if "image_id" not in cols: + continue + select = "image_id" + (",product_name" if "product_name" in cols else "") + select += ",category" if "category" in cols else "" + cur.execute(f"SELECT {select} FROM {table} WHERE image_id IS NOT NULL") + brand = display_name_for_suffix(table[len("brand_"):]) + for record in cur.fetchall(): + row = dict(zip(select.split(","), record)) + index[(brand, row["image_id"])] = { + "product_name": row.get("product_name") or "", + "category": row.get("category") or "", + "table": table, + } + return index + + +def _nutrition_rows(cur) -> List[Dict[str, Any]]: + cur.execute( + "SELECT brand, image_id, product_name, category, data_status " + "FROM nutrition_facts" + ) + cols = ("brand", "image_id", "product_name", "category", "data_status") + return [dict(zip(cols, r)) for r in cur.fetchall()] + + +# --------------------------------------------------------------------------- +# Reporting +# --------------------------------------------------------------------------- +def _report(plan: PurgePlan, as_json: bool) -> Dict[str, Any]: + scored = [r for r in plan.delete if (r.get("data_status") or "") != "unavailable"] + by_category: Dict[str, int] = {} + for row in plan.delete: + by_category[row["category"]] = by_category.get(row["category"], 0) + 1 + + summary = { + "nutrition_rows": len(plan.delete) + len(plan.keep) + len(plan.undecided) + len(plan.orphans), + "to_delete": len(plan.delete), + "to_delete_holding_real_data": len(scored), + "keep": len(plan.keep), + "undecided": len(plan.undecided), + "orphans": len(plan.orphans), + "junk_categories": len(plan.junk_categories), + "by_category": dict(sorted(by_category.items(), key=lambda kv: -kv[1])), + } + + if as_json: + print(json.dumps({"summary": summary, "delete": plan.delete, + "undecided": plan.undecided, "orphans": plan.orphans}, indent=2)) + return summary + + logger.info("") + logger.info("Target database: %s / %s", DB_HOST, DB_NAME) + logger.info("") + logger.info("%d nutrition_facts row(s)", summary["nutrition_rows"]) + logger.info(" %6d non-consumable <-- would be deleted", summary["to_delete"]) + logger.info(" %6d of those actually hold nutrition data (not 'unavailable')", + summary["to_delete_holding_real_data"]) + logger.info(" %6d consumable, kept", summary["keep"]) + logger.info(" %6d undecided, kept - extend the map or fix the category", + summary["undecided"]) + logger.info(" %6d orphaned, kept - product missing from every brand table", + summary["orphans"]) + + if by_category: + logger.info("") + logger.info("Non-consumable rows by category:") + for category, count in sorted(by_category.items(), key=lambda kv: -kv[1]): + logger.info(" %-40s %5d", category or "(blank)", count) + + if scored: + logger.info("") + logger.info("Rows holding real nutrition for a non-food product " + "- this is what justifies the change:") + for row in scored[:25]: + logger.info(" %-34s %-26s %s", + (row["product_name"] or "")[:33], row["category"][:25], + row["data_status"]) + if len(scored) > 25: + logger.info(" ... and %d more", len(scored) - 25) + + if plan.undecided: + logger.info("") + logger.info("UNDECIDED - kept, because guessing here would delete real data:") + for row in plan.undecided[:20]: + logger.info(" %-34s %-22s %s", + (row["product_name"] or "")[:33], + (row["category"] or "(blank)")[:21], row["reason"][:60]) + if len(plan.undecided) > 20: + logger.info(" ... and %d more", len(plan.undecided) - 20) + + if plan.junk_categories: + logger.info("") + logger.info("%d row(s) carry a bare number as their category. That is import " + "damage and wants a catalogue repair, not a rule here.", + len(plan.junk_categories)) + + if plan.orphans: + logger.info("") + logger.info("ORPHANS - a nutrition row with no product. Kept and reported; " + "deleting them here would hide the underlying defect:") + for row in plan.orphans[:10]: + logger.info(" %s / %s", row["brand"], row["image_id"]) + if len(plan.orphans) > 10: + logger.info(" ... and %d more", len(plan.orphans) - 10) + + return summary + + +# --------------------------------------------------------------------------- +# Writing +# --------------------------------------------------------------------------- +def _plain(value: Any) -> Any: + """JSON-safe. nutrition_facts has NUMERIC and TIMESTAMP columns, so this + hits both traps that merge_haldiram hit for real.""" + if isinstance(value, Decimal): + return float(value) + if isinstance(value, datetime): + return value.isoformat() + return value + + +def _backup(cur, keys: List[Key]) -> Path: + """Every row about to be touched, across all four tables, in both + directions for the two cache tables.""" + DATA_DIR.mkdir(parents=True, exist_ok=True) + tables: Dict[str, List[Dict[str, Any]]] = {} + + for table in CORE_TABLES: + cur.execute(f"SELECT * FROM {table} WHERE (brand, image_id) IN %s", + (tuple(keys),)) + cols = [d[0] for d in cur.description] + tables[table] = [{c: _plain(v) for c, v in zip(cols, r)} for r in cur.fetchall()] + + for table, own, inbound in CACHE_TABLES: + cur.execute( + f"SELECT * FROM {table} WHERE ({own[0]}, {own[1]}) IN %s " + f"OR ({inbound[0]}, {inbound[1]}) IN %s", + (tuple(keys), tuple(keys)), + ) + cols = [d[0] for d in cur.description] + tables[table] = [{c: _plain(v) for c, v in zip(cols, r)} for r in cur.fetchall()] + + path = DATA_DIR / f"nutrition_purge_backup_{datetime.now():%Y%m%d_%H%M%S}.json" + path.write_text(json.dumps({ + "created_at": datetime.now().isoformat(), + "db_host": DB_HOST, "db_name": DB_NAME, + "keys": [list(k) for k in keys], + "tables": tables, + }, indent=2), encoding="utf-8") + return path + + +def _delete(cur, keys: List[Key]) -> Dict[str, int]: + deleted: Dict[str, int] = {} + for table, own, inbound in CACHE_TABLES: + cur.execute( + f"DELETE FROM {table} WHERE ({own[0]}, {own[1]}) IN %s " + f"OR ({inbound[0]}, {inbound[1]}) IN %s", + (tuple(keys), tuple(keys)), + ) + deleted[table] = cur.rowcount + for table in CORE_TABLES: + cur.execute(f"DELETE FROM {table} WHERE (brand, image_id) IN %s", (tuple(keys),)) + deleted[table] = cur.rowcount + return deleted + + +def _backfill_edibility(cur, plan: PurgePlan) -> Dict[str, int]: + """Stamp the verdict this run already computed onto the rows it is keeping. + + WHY HERE AND NOT IN A MIGRATION: `plan_purge` has just classified every one + of these rows against the catalogue's own category, using the same + classifier and the same inputs `enrich_one_product` uses. Writing the answer + down costs one UPDATE; computing it a second time somewhere else would be a + second chance to compute it differently. + + WHY IT MATTERS: `GET /api/nutrition/health-scores` filters on + `nutrition_facts.edibility`, and every row written before that column + existed carries NULL, which the endpoint treats as unconfirmed and excludes. + Without this the endpoint returns nothing until a full re-enrichment run. + + Orphans are deliberately left NULL: they have no catalogue row, so there is + no category to classify them from, and guessing is the thing this whole + script exists to stop. + """ + # Idempotent, and the same statement `ensure_nutrition_schema()` runs. Here + # so the script works against a database the API has not started against + # yet, rather than failing on an unknown column. + cur.execute("ALTER TABLE nutrition_facts ADD COLUMN IF NOT EXISTS edibility TEXT") + + written: Dict[str, int] = {} + for label, rows in (("consumable", plan.keep), ("unknown", plan.undecided)): + keys = [(r["brand"], r["image_id"]) for r in rows] + if not keys: + written[label] = 0 + continue + cur.execute( + "UPDATE nutrition_facts SET edibility = %s WHERE (brand, image_id) IN %s", + (label, tuple(keys)), + ) + written[label] = cur.rowcount + return written + + +def restore(cur, conn, path: Path, apply: bool) -> int: + payload = json.loads(path.read_text(encoding="utf-8")) + if payload.get("db_host") != DB_HOST: + logger.error("Backup was taken from %s but DB_HOST is %s. Refusing.", + payload.get("db_host"), DB_HOST) + return 1 + total = 0 + for table, rows in payload["tables"].items(): + for row in rows: + total += 1 + if not apply: + continue + cols = list(row) + cur.execute( + f"INSERT INTO {table} ({', '.join(cols)}) " + f"VALUES ({', '.join('%s' for _ in cols)}) ON CONFLICT DO NOTHING", + [row[c] for c in cols], + ) + if apply: + conn.commit() + logger.info("%s %d row(s) from %s", "Restored" if apply else "Would restore", + total, path.name) + return 0 + + +# --------------------------------------------------------------------------- +def main() -> int: + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + mode = parser.add_mutually_exclusive_group(required=True) + mode.add_argument("--audit", action="store_true", + help="read-only report; writes nothing") + mode.add_argument("--purge", action="store_true", + help="delete non-consumable nutrition rows") + mode.add_argument("--restore", metavar="PATH", help="restore a backup file") + + parser.add_argument("--apply", action="store_true", + help="commit the changes (default is a dry run)") + parser.add_argument("--dry-run", action="store_true", + help="explicit no-op; this is already the default") + parser.add_argument("--json", action="store_true", help="audit output as JSON") + args = parser.parse_args() + + apply = args.apply and not args.dry_run + if args.audit and args.apply: + parser.error("--audit is read-only; it cannot be combined with --apply") + + conn = _connect() + if conn is None: + logger.error("No database connection.") + return 1 + + try: + with conn.cursor() as cur: + if args.restore: + return restore(cur, conn, Path(args.restore), apply) + + plan = plan_purge(_nutrition_rows(cur), _catalogue_index(cur)) + summary = _report(plan, args.json) + + if args.audit: + return 0 + + logger.info("") + logger.info("Mode: %s", "APPLY - this writes" if apply + else "DRY RUN - nothing is written") + labelled = len(plan.keep) + len(plan.undecided) + if not apply: + logger.info("Re-run with --apply to delete %d row(s) and label %d.", + len(plan.delete), labelled) + return 0 + + # Labelling runs first and unconditionally: it is additive, it is + # correct even when there is nothing to delete, and it is what makes + # the health-score endpoint able to tell food from not-food in SQL. + written = _backfill_edibility(cur, plan) + conn.commit() + logger.info("") + for label, count in written.items(): + logger.info(" labelled %5d row(s) edibility=%s", count, label) + + if not plan.delete: + logger.info("") + logger.info("Nothing to delete.") + return 0 + + path = _backup(cur, plan.delete_keys) + logger.info("Backup written: %s", path) + deleted = _delete(cur, plan.delete_keys) + conn.commit() + + logger.info("") + for table, count in deleted.items(): + logger.info(" deleted %5d row(s) from %s", count, table) + logger.info("") + logger.info("The similarity and clustering models were fitted with these " + "rows still in them.") + logger.info("Retrain before trusting /similar or /alternatives:") + logger.info(" python -m scripts.train_nutrition_models") + _ = summary + finally: + conn.close() + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/refresh_usda_snapshot.py b/scripts/refresh_usda_snapshot.py new file mode 100644 index 0000000..ce1c414 --- /dev/null +++ b/scripts/refresh_usda_snapshot.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +""" +Rebuild the pinned USDA nutrient snapshot from the open bulk download. + +WHY THE BULK DOWNLOAD AND NOT THE API +------------------------------------- +The FoodData Central API is the obvious source and it cannot do this job. Its +unauthenticated DEMO_KEY allows TEN requests per hour - measured, with a +Retry-After of thirteen hours once exhausted - and the curated table references +101 distinct foods. Even a personal key would make rebuilding the snapshot a +rate-limit exercise. + +The same data is published as a plain zip with no key and no quota: + + FoodData_Central_foundation_food_json_*.zip ~0.5 MB + FoodData_Central_sr_legacy_food_json_*.zip ~12 MB + +So the snapshot is built from those, and `USDA_FDC_API_KEY` stays optional - +needed only to look up an id the snapshot does not already hold. + +WHAT IT STORES, AND WHY RAW +--------------------------- +The `foodNutrients` list exactly as USDA publishes it, trimmed to the fields +this codebase reads. NOT pre-mapped to our columns: if the snapshot held +finished per-100g values, the offline tests would exercise a different code +path from the online one, and the unit-checking rules in +`nutrition_usda_service` - the ones that stop a sodium figure being silently +multiplied by a thousand - would go untested. + +USAGE +----- + python -m scripts.refresh_usda_snapshot # dry run, shows the diff + python -m scripts.refresh_usda_snapshot --apply + python -m scripts.refresh_usda_snapshot --apply --keep-download + +A dry run reports every nutrient whose value moved, so a USDA revision is read +and accepted rather than silently absorbed. +""" +from __future__ import annotations + +import argparse +import json +import logging +import shutil +import sys +import tempfile +import zipfile +from pathlib import Path +from typing import Any, Dict, List + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import requests # noqa: E402 + +from app.services import produce_reference # noqa: E402 + +logging.basicConfig(level=logging.INFO, format="%(message)s") +logger = logging.getLogger("refresh_usda_snapshot") + +SNAPSHOT = Path(__file__).resolve().parents[1] / "app" / "services" / "data" / "usda_snapshot.json" + +# Pinned filenames rather than "latest": a dataset that silently became a newer +# release between two runs would move numbers with no diff to read. +DATASETS = ( + ("FoundationFoods", + "https://fdc.nal.usda.gov/fdc-datasets/FoodData_Central_foundation_food_json_2025-04-24.zip"), + ("SRLegacyFoods", + "https://fdc.nal.usda.gov/fdc-datasets/FoodData_Central_sr_legacy_food_json_2021-10-28.zip"), +) + + +def _download(url: str, into: Path) -> Path: + name = url.rsplit("/", 1)[-1] + target = into / name + if target.exists(): + return target + logger.info(" downloading %s", name) + with requests.get(url, stream=True, timeout=300) as resp: + resp.raise_for_status() + with target.open("wb") as fh: + for chunk in resp.iter_content(1 << 20): + fh.write(chunk) + return target + + +def _load(zip_path: Path, key: str) -> List[Dict[str, Any]]: + with zipfile.ZipFile(zip_path) as zf: + name = zf.namelist()[0] + with zf.open(name) as fh: + return json.load(fh)[key] + + +def _trim(food: Dict[str, Any]) -> Dict[str, Any]: + nutrients = [] + for fn in food.get("foodNutrients", []): + n = fn.get("nutrient") or {} + if fn.get("amount") is None or not n.get("id"): + continue + nutrients.append({"id": n["id"], "name": n.get("name"), + "unit": n.get("unitName"), "amount": fn["amount"]}) + portions = [] + for p in (food.get("foodPortions") or [])[:3]: + grams = p.get("gramWeight") + if not grams: + continue + portions.append({ + "gram_weight": grams, + "description": (p.get("portionDescription") + or (p.get("measureUnit") or {}).get("name") or ""), + }) + return {"fdc_id": food["fdcId"], "description": food.get("description"), + "data_type": food.get("dataType"), + "publication_date": food.get("publicationDate"), + "nutrients": nutrients, "portions": portions} + + +def _diff(old: Dict[str, Any], new: Dict[str, Any]) -> List[str]: + lines: List[str] = [] + for fid, food in sorted(new.items()): + before = old.get(fid) + if not before: + lines.append(f" NEW {fid} {food['description'][:56]}") + continue + if before.get("description") != food.get("description"): + lines.append(f" RENAME {fid} {before['description'][:34]} -> " + f"{food['description'][:34]}") + old_n = {n["id"]: n["amount"] for n in before.get("nutrients", [])} + new_n = {n["id"]: n["amount"] for n in food.get("nutrients", [])} + for nid, amount in sorted(new_n.items()): + if nid in old_n and old_n[nid] != amount: + lines.append(f" VALUE {fid} nutrient {nid}: " + f"{old_n[nid]} -> {amount} ({food['description'][:30]})") + for nid in sorted(set(old_n) - set(new_n)): + lines.append(f" GONE {fid} nutrient {nid} ({food['description'][:30]})") + for fid in sorted(set(old) - set(new)): + lines.append(f" LOST {fid} {old[fid].get('description', '')[:56]}") + return lines + + +def main() -> int: + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--apply", action="store_true", + help="write the snapshot (default is a dry run)") + parser.add_argument("--keep-download", action="store_true", + help="keep the downloaded zips instead of using a temp dir") + args = parser.parse_args() + + wanted = {e["usda_fdc_id"] for e in produce_reference.all_entries().values() + if e.get("usda_fdc_id")} + logger.info("%d FDC id(s) referenced by the curated table", len(wanted)) + + workdir = Path("usda_bulk") if args.keep_download else Path(tempfile.mkdtemp()) + workdir.mkdir(parents=True, exist_ok=True) + + snapshot: Dict[str, Any] = {} + try: + for key, url in DATASETS: + path = _download(url, workdir) + for food in _load(path, key): + fid = food.get("fdcId") + if fid in wanted and str(fid) not in snapshot: + snapshot[str(fid)] = _trim(food) + finally: + if not args.keep_download: + shutil.rmtree(workdir, ignore_errors=True) + + missing = sorted(wanted - {int(k) for k in snapshot}) + if missing: + logger.warning("NOT FOUND in either dataset: %s", missing) + + old = {} + if SNAPSHOT.exists(): + old = json.loads(SNAPSHOT.read_text(encoding="utf-8")).get("foods", {}) + + changes = _diff(old, snapshot) + if changes: + logger.info("") + logger.info("%d change(s) against the current snapshot:", len(changes)) + for line in changes[:60]: + logger.info("%s", line) + if len(changes) > 60: + logger.info(" ... and %d more", len(changes) - 60) + else: + logger.info("No change against the current snapshot.") + + if not args.apply: + logger.info("") + logger.info("Dry run - nothing written. Re-run with --apply.") + return 0 + + SNAPSHOT.parent.mkdir(parents=True, exist_ok=True) + SNAPSHOT.write_text(json.dumps( + {"source": "USDA FoodData Central bulk download", + "datasets": [u.rsplit("/", 1)[-1].replace(".zip", "") for _, u in DATASETS], + "foods": snapshot}, + indent=1, sort_keys=True), encoding="utf-8") + logger.info("") + logger.info("Wrote %s (%d food(s), %.1f MB)", SNAPSHOT, len(snapshot), + SNAPSHOT.stat().st_size / 1e6) + logger.info("Run the test suite: tests/test_nutrition_usda.py asserts every " + "curated id is pinned and every description still matches.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/repair_brand_images.py b/scripts/repair_brand_images.py index 282f94c..1a01099 100644 --- a/scripts/repair_brand_images.py +++ b/scripts/repair_brand_images.py @@ -75,6 +75,7 @@ sys.path.insert(0, str(Path(__file__).resolve().parents[1])) from app.infrastructure.settings import DB_HOST, DB_NAME # noqa: E402 from app.services.brand_registry import resolve_parent_brand # noqa: E402 +from app.services.generic_products import OWN_PRODUCTS_BRAND # noqa: E402 from app.services.vector_store import ( # noqa: E402 _connect, _list_brand_table_suffixes, @@ -358,8 +359,18 @@ _OFF_CACHE: Dict[str, bool] = {} _OFF_BARCODE = re.compile(r"/images/products/((?:\d+/)+)") +# "own" and "products" are the BUCKET's name, not a brand's. Left in, the token +# "products" corroborates any URL containing /images/products/ or +# /cdn/shop/products/ - which is every Open*Facts and every Shopify path - so +# `_names_product` waved through 40+ images that named nothing about the item. +# That is how the openbeautyfacts cosmetics photos became the stored image for +# Banana, Orange, Papaya, Guava, Lemon and twenty more. +_BUCKET_TOKENS = frozenset({"own", "products", "product"}) + + def _brand_tokens(brand: str) -> List[str]: - return [w for w in re.split(r"[^a-z0-9]+", (brand or "").lower()) if len(w) > 2] + return [w for w in re.split(r"[^a-z0-9]+", (brand or "").lower()) + if len(w) > 2 and w not in _BUCKET_TOKENS] def _off_product_matches_brand(url: str, brand: str) -> bool: @@ -420,6 +431,15 @@ def _search_key(product_name: str, brand: str) -> Tuple[str, str]: return (brand.lower(), (base or product_name or "").lower()) +def _is_own_products(brand: str) -> bool: + """True for the unbranded-commodity bucket, under either spelling. + + `repair()` passes the TABLE SUFFIX ("own_products"), while the pipeline and + the UI use the display name ("Own Products"). + """ + return _sanitize_name(brand or "") == _sanitize_name(OWN_PRODUCTS_BRAND) + + def _search_replacement(product_name: str, brand: str, max_results: int) -> List[str]: """Validated image URLs for one product, best first. @@ -433,7 +453,17 @@ def _search_replacement(product_name: str, brand: str, max_results: int) -> List return _SEARCH_CACHE[key] from app.services.image_search import find_all_image_urls - urls = find_all_image_urls(product_name, brand=brand, validate=True, max_results=max_results) + + # `stage_6_images` already blanks the brand for this bucket + # (store_catalog_pipeline.py) and this path never did, so a repair searched + # for "own_products Apple fruit juice" and got exactly what it asked for. + # Produce mode additionally skips the packaged-goods databases and the + # commodity-hint table - see image_search._AMBIQUITY_HINTS. + produce = _is_own_products(brand) + urls = find_all_image_urls( + product_name, brand="" if produce else brand, + validate=True, max_results=max_results, produce=produce, + ) if not urls: _SEARCH_CACHE[key] = [] return [] @@ -451,14 +481,15 @@ def _search_replacement(product_name: str, brand: str, max_results: int) -> List # image would have been written as the primary image of four MTR products. # Let an error here raise: unranked output is not safe to store. from app.core.catalog_engine import ProductCatalogEngine + rank_brand = "" if produce else brand urls = ProductCatalogEngine._select_best_images( - ProductCatalogEngine, urls, product_name, brand, max_images=MAX_STORED_IMAGES + ProductCatalogEngine, urls, product_name, rank_brand, max_images=MAX_STORED_IMAGES ) if not urls: _SEARCH_CACHE[key] = [] return [] # Drop other brands' products before anything else looks at them. - urls = [u for u in urls if _off_product_matches_brand(u, brand)] + urls = [u for u in urls if produce or _off_product_matches_brand(u, brand)] if not urls: _SEARCH_CACHE[key] = [] return [] @@ -480,7 +511,7 @@ def _search_replacement(product_name: str, brand: str, max_results: int) -> List # (Zepto, Flipkart, Pinterest). That is the intended trade - 94% of rows # still find a corroborated URL, and the rest are reported for hand # curation rather than filled with a guess. - ranked = [u for u in urls if _names_product(u, product_name, brand)] + ranked = [u for u in urls if _names_product(u, product_name, rank_brand)] ranked.sort(key=lambda u: 0 if u.startswith("https://") else 1) _SEARCH_CACHE[key] = ranked return ranked diff --git a/tests/conftest.py b/tests/conftest.py index 6cd5fcd..6288b5d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -67,6 +67,19 @@ os.environ.setdefault("USE_GOOGLE_CSE", "false") # which sets the value explicitly and clears the parsed cache. os.environ["ACTIVE_BRANDS"] = "" +# Unconditional, and OFF - the opposite of the production default. +# +# Finishing an ingestion batch queues nutrition enrichment on a background +# thread, and that thread calls out to a database and to Open Food Facts. Any +# test that runs `run_batch` would start it, so a `pytest` run reached for the +# host in the developer's backend/.env - which on this project is PRODUCTION. +# It got no further than a failed password, which is not a margin worth +# relying on. +# +# The behaviour itself is covered by tests/test_auto_enrich_on_upload.py, which +# turns it on explicitly, the same arrangement ACTIVE_BRANDS has above. +os.environ["AUTO_ENRICH_ON_UPLOAD"] = "false" + # Auth is set unconditionally (not setdefault): the suite asserts on the real # guards, so it must never inherit a developer's AUTH_ENABLED=false. os.environ["AUTH_ENABLED"] = "true" diff --git a/tests/test_auto_enrich_on_upload.py b/tests/test_auto_enrich_on_upload.py new file mode 100644 index 0000000..f457af3 --- /dev/null +++ b/tests/test_auto_enrich_on_upload.py @@ -0,0 +1,295 @@ +"""A newly uploaded product gets a health score without anyone asking for one. + +Before this, a catalogue upload ran eleven pipeline stages, stored the products +and stopped. Nothing on that path touched nutrition, and `enrich_all_products` +had exactly one HTTP trigger in the whole application - the admin-only +`POST /api/admin/nutrition-intelligence/enrich`. So every uploaded product sat +unscored until somebody remembered to run it by hand. + +The three things that can silently break this, each pinned below: + + * `include_inactive=True` going missing. A brand that was just uploaded is + almost never in ACTIVE_BRANDS, and `_brands_to_enrich` honours that filter + by default - the job would report a clean run over an empty work list. + * The submission raising. The products ARE stored by that point; a scoring + step that could not start must not make a successful import look failed. + * The manifest gaining a required field. Manifests are JSON on the upload + volume and outlive a deploy, so an older one still has to load. + +Fixture conventions follow test_batch_stage_timeline.py. +""" +from __future__ import annotations + +import io + +import pytest + +from app.core import batch_ingest +from app.core import store_catalog_pipeline as pipeline +from app.services import nutrition_autoenrich + +openpyxl = pytest.importorskip("openpyxl") + + +# --------------------------------------------------------------------------- +# The submission helper +# --------------------------------------------------------------------------- +@pytest.fixture +def captured(monkeypatch): + """Capture the thunk instead of starting a thread, then run it against a + fake `enrich_all_products` so the kwargs it was built with are visible.""" + box = {"kwargs": None, "names": []} + + def fake_background(func, *, name): + box["names"].append(name) + func() + + def fake_enrich(**kwargs): + box["kwargs"] = kwargs + return type("R", (), { + "total_products": 3, "verified": 2, "partial": 0, "unavailable": 1, + "skipped_non_consumable": 4, "duration_seconds": 1.0, "errors": [], + })() + + # conftest pins this off for the suite; these tests are about what happens + # when it is on. + monkeypatch.setattr(nutrition_autoenrich.settings, "AUTO_ENRICH_ON_UPLOAD", True) + monkeypatch.setattr(nutrition_autoenrich, "run_in_background", fake_background) + monkeypatch.setattr(nutrition_autoenrich.nutrition_enrichment_service, + "enrich_all_products", fake_enrich) + return box + + +def test_the_uploaded_brands_are_what_gets_enriched(captured): + nutrition_autoenrich.submit_enrichment_for_brands(["Amul", "Britannia"], source="test") + + assert captured["kwargs"]["brands"] == ["Amul", "Britannia"] + + +def test_active_brands_is_widened_or_nothing_would_be_scored(captured): + """The single most load-bearing argument here. Without it a freshly + uploaded brand is filtered out before any product is looked at, and the job + still reports success.""" + nutrition_autoenrich.submit_enrichment_for_brands(["NewBrand"], source="test") + + assert captured["kwargs"]["include_inactive"] is True + + +def test_only_the_products_that_still_need_a_score_are_fetched(captured): + """`skip_if_verified` is what makes re-uploading a scored brand cheap, and + is why no separate "which rows are new" bookkeeping is needed.""" + nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") + + assert captured["kwargs"]["skip_if_verified"] is True + + +def test_the_llm_narrative_is_left_for_the_admin_job(captured): + """It is the slow, token-costing step and adds no health score.""" + nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") + + assert captured["kwargs"]["generate_narrative"] is False + + +def test_a_brand_uploaded_in_two_files_is_enriched_once(captured): + nutrition_autoenrich.submit_enrichment_for_brands( + ["Amul", "Britannia", "Amul"], source="test") + + assert captured["kwargs"]["brands"] == ["Amul", "Britannia"] + + +def test_blank_brands_do_not_become_work(captured): + nutrition_autoenrich.submit_enrichment_for_brands(["", " ", "Amul"], source="test") + + assert captured["kwargs"]["brands"] == ["Amul"] + + +def test_a_batch_that_produced_no_brands_queues_nothing(captured): + assert nutrition_autoenrich.submit_enrichment_for_brands([], source="test") is None + assert captured["kwargs"] is None + + +def test_the_job_id_is_returned_so_the_caller_can_be_polled(captured): + job_id = nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") + + assert job_id + assert nutrition_autoenrich.nutrition_job_store.get(job_id) is not None + + +def test_the_job_records_what_the_run_actually_did(captured): + job_id = nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") + job = nutrition_autoenrich.nutrition_job_store.get(job_id) + + assert job.status == "done" + assert job.result["verified"] == 2 + assert job.result["skipped_non_consumable"] == 4 + + +def test_the_detail_names_the_brands_so_an_operator_can_read_it(captured): + job_id = nutrition_autoenrich.submit_enrichment_for_brands( + ["Amul", "Britannia"], source="batch abc123") + job = nutrition_autoenrich.nutrition_job_store.get(job_id) + + assert "batch abc123" in job.detail + assert "Amul, Britannia" in job.detail + + +def test_an_enrichment_that_raises_fails_the_job_not_the_caller(captured, monkeypatch): + def boom(**kwargs): + raise RuntimeError("Open Food Facts is down") + + monkeypatch.setattr(nutrition_autoenrich.nutrition_enrichment_service, + "enrich_all_products", boom) + + job_id = nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") + job = nutrition_autoenrich.nutrition_job_store.get(job_id) + + assert job.status == "failed" + assert "Open Food Facts is down" in job.detail + + +def test_the_submission_itself_never_raises_at_its_caller(monkeypatch): + """Every caller is an upload handler finishing its work. An exception here + would turn a successful ingestion into a 500.""" + monkeypatch.setattr(nutrition_autoenrich.settings, "AUTO_ENRICH_ON_UPLOAD", True) + monkeypatch.setattr(nutrition_autoenrich.nutrition_job_store, "create", + lambda kind: (_ for _ in ()).throw(RuntimeError("no job store"))) + + assert nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") is None + + +# --------------------------------------------------------------------------- +# The off switch +# --------------------------------------------------------------------------- +def test_the_setting_turns_it_off_without_a_deploy(captured, monkeypatch): + monkeypatch.setattr(nutrition_autoenrich.settings, "AUTO_ENRICH_ON_UPLOAD", False) + + assert nutrition_autoenrich.submit_enrichment_for_brands(["Amul"], source="test") is None + assert captured["kwargs"] is None + assert captured["names"] == [], "no thread should have been started" + + +# --------------------------------------------------------------------------- +# The batch hook +# --------------------------------------------------------------------------- +THREE_BRANDS = [ + ["Amul Butter 100g", "Dairy", "Amul"], + ["Britannia Marie Gold 250g", "Biscuits", "Britannia"], + ["Cadbury Dairy Milk 150g", "Chocolate", "Cadbury"], +] + + +def _sheet(rows=THREE_BRANDS) -> bytes: + wb = openpyxl.Workbook() + ws = wb.active + ws.append(["Product Name", "Category", "Brand"]) + for row in rows: + ws.append(row) + buf = io.BytesIO() + wb.save(buf) + return buf.getvalue() + + +@pytest.fixture(autouse=True) +def _isolate_sku_counter(tmp_path, monkeypatch): + from app.services import sku_service + monkeypatch.setattr(sku_service, "_data_dir", tmp_path / "sku_sequences") + + +@pytest.fixture(autouse=True) +def batch_root(tmp_path, monkeypatch): + monkeypatch.setattr(batch_ingest, "BATCH_UPLOAD_DIR", tmp_path / "batch_uploads") + + +@pytest.fixture(autouse=True) +def store(monkeypatch): + """A fake brand table, so the pipeline never reaches Postgres.""" + table: dict = {} + + def fake_upsert(brand, rows, **kwargs): + for row in rows: + table[(brand, row.get("image_id"))] = row + return {"inserted": len(rows), "backfilled": 0, "skipped_existing": 0} + + monkeypatch.setattr(pipeline, "upsert_brand_products", fake_upsert) + monkeypatch.setattr(pipeline, "get_products_by_brand", lambda b, **kw: list(table.values())) + monkeypatch.setattr(pipeline, "embed_texts", lambda texts: [[0.0] * 384 for _ in texts]) + return table + + +@pytest.fixture +def submissions(monkeypatch): + """Record what the batch hook submits, without starting anything.""" + seen = [] + + def fake_submit(brands, *, source): + seen.append((list(brands), source)) + return "job-1234" + + monkeypatch.setattr(nutrition_autoenrich, "submit_enrichment_for_brands", fake_submit) + return seen + + +def test_a_finished_batch_queues_scoring_for_every_brand_it_wrote(submissions): + manifest = batch_ingest.stage_batch([("catalog.xlsx", _sheet())]) + result = batch_ingest.run_batch(manifest.batch_id) + + assert len(submissions) == 1, "one job for the batch, not one per file" + brands, source = submissions[0] + assert set(brands) == {"Amul", "Britannia", "Cadbury"} + assert manifest.batch_id in source + assert result.nutrition_job_id == "job-1234" + + +def test_the_job_id_survives_a_reread_from_disk(submissions): + """The client polls the batch through the manifest on the volume, which is + a different object from the one `run_batch` returned.""" + manifest = batch_ingest.stage_batch([("catalog.xlsx", _sheet())]) + batch_ingest.run_batch(manifest.batch_id) + + assert batch_ingest.read_manifest(manifest.batch_id).nutrition_job_id == "job-1234" + + +def test_a_submission_that_raises_leaves_the_batch_successful(monkeypatch): + """The products are stored by this point. Reporting the import as failed + because an optional follow-up could not start would be a lie.""" + def boom(brands, *, source): + raise RuntimeError("job store unavailable") + + monkeypatch.setattr(nutrition_autoenrich, "submit_enrichment_for_brands", boom) + + manifest = batch_ingest.stage_batch([("catalog.xlsx", _sheet())]) + result = batch_ingest.run_batch(manifest.batch_id) + + assert result.files[0].status == batch_ingest.DONE + assert result.status not in (batch_ingest.FAILED,) + assert result.nutrition_job_id is None + + +def test_the_job_id_reaches_the_api_response(submissions): + """`to_out` renders only fields declared on BatchOut, so a manifest field + that is not also declared there is dropped on the way out - silently.""" + from app.api import batch_common + + manifest = batch_ingest.stage_batch([("catalog.xlsx", _sheet())]) + result = batch_ingest.run_batch(manifest.batch_id) + + assert batch_common.to_out(result).nutrition_job_id == "job-1234" + + +# --------------------------------------------------------------------------- +# Backwards compatibility of the manifest on disk +# --------------------------------------------------------------------------- +def test_a_manifest_written_before_this_field_existed_still_loads(): + """manifest.json files live on the upload volume and outlive a deploy.""" + older = {"batch_id": "b1", "status": batch_ingest.DONE, "files": []} + + restored = batch_ingest.BatchManifest.from_dict(older) + + assert restored.nutrition_job_id is None + + +def test_the_field_round_trips(): + manifest = batch_ingest.BatchManifest(batch_id="b1", nutrition_job_id="job-9") + + assert batch_ingest.BatchManifest.from_dict( + manifest.to_dict()).nutrition_job_id == "job-9" diff --git a/tests/test_consumability.py b/tests/test_consumability.py new file mode 100644 index 0000000..19e1266 --- /dev/null +++ b/tests/test_consumability.py @@ -0,0 +1,271 @@ +""" +Walls off the defect where non-food products carried a health score. + +Measured against the live catalogue on 2026-09-03, these rows had one: + + Colgate-Palmolive Palmolive Naturals General soap + Cavinkare Nyle, Cavinkare Nature's Hair Care shampoo + P&G Pantene Hair Care shampoo + Godrej Hit Spray Personal Care - Mosquito 291 kcal + +Every category string in LIVE_CATEGORIES below was read out of the production +brand tables, with its row count, so this file tests the taxonomy that exists +rather than the one `CATEGORY_REGISTRY` describes - the two have drifted, which +is the whole reason `consumability` is not just a field on the registry. + +Pure unit tests: no database, no network, no LLM. +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from app.services import nutrition_data_service as nds # noqa: E402 +from app.services.consumability import ( # noqa: E402 + CATEGORY_VERDICTS, + Edibility, + classify_edibility, + is_consumable, + is_junk_category, + is_non_consumable, +) +from app.services.enrichment.hsn_gst.models import HSN_GST_TABLE # noqa: E402 + +# (category string, live row count, expected verdict). Read from the production +# database; the counts are kept so a future reader can tell a 189-row decision +# from a 1-row one. +LIVE_CATEGORIES = [ + ("Dairy", 189, Edibility.CONSUMABLE), + ("Beverages", 174, Edibility.CONSUMABLE), + ("Spices & Masalas", 136, Edibility.CONSUMABLE), + ("Chocolates", 119, Edibility.CONSUMABLE), + ("Fruits & Vegetables", 95, Edibility.CONSUMABLE), + ("Food - Mixes", 83, Edibility.CONSUMABLE), + ("Tea & Coffee", 76, Edibility.CONSUMABLE), + ("Hair Care", 51, Edibility.NON_CONSUMABLE), + ("Oral Care", 45, Edibility.NON_CONSUMABLE), + ("Snacks", 42, Edibility.CONSUMABLE), + ("Cooking Oils", 42, Edibility.CONSUMABLE), + ("Detergents & Fabric Care", 38, Edibility.NON_CONSUMABLE), + ("Pulses, Grains & Spices", 35, Edibility.CONSUMABLE), + ("Health Drinks", 26, Edibility.CONSUMABLE), + ("Pasta & Noodles", 24, Edibility.CONSUMABLE), + ("Pickles & Chutneys", 24, Edibility.CONSUMABLE), + ("Skin Care", 19, Edibility.NON_CONSUMABLE), + ("Bath Soap", 17, Edibility.NON_CONSUMABLE), + ("Fresh Herbs & Greens", 17, Edibility.CONSUMABLE), + ("Atta & Staples", 16, Edibility.CONSUMABLE), + ("Fish & Seafood", 15, Edibility.CONSUMABLE), + ("Flowers", 14, Edibility.NON_CONSUMABLE), + ("Candy & Confectionery", 13, Edibility.CONSUMABLE), + ("Dairy - Desserts", 13, Edibility.CONSUMABLE), + ("Biscuits & Cookies", 12, Edibility.CONSUMABLE), + ("Dishwash", 12, Edibility.NON_CONSUMABLE), + ("Health Foods", 12, Edibility.CONSUMABLE), + ("Food - Spreads", 11, Edibility.CONSUMABLE), + ("Skin & Bath Care", 10, Edibility.NON_CONSUMABLE), + ("Noodles & Instant Food", 10, Edibility.CONSUMABLE), + ("Cheese", 10, Edibility.CONSUMABLE), + ("Salt & Staples", 9, Edibility.CONSUMABLE), + ("Breakfast Cereal", 8, Edibility.CONSUMABLE), + ("Feminine Hygiene", 6, Edibility.NON_CONSUMABLE), + ("Ready to Eat", 6, Edibility.CONSUMABLE), + ("Dry Fruits & Nuts", 6, Edibility.CONSUMABLE), + ("Eggs", 5, Edibility.CONSUMABLE), + ("Bakery & Breads", 5, Edibility.CONSUMABLE), + ("Ice Cream", 5, Edibility.CONSUMABLE), + ("Health Care - Cold & Cough", 5, Edibility.NON_CONSUMABLE), + ("Personal Care - Mosquito Repellent", 5, Edibility.NON_CONSUMABLE), + ("Food & Beverages", 4, Edibility.CONSUMABLE), + ("Men's Grooming", 4, Edibility.NON_CONSUMABLE), + ("Household - Lamp Oil", 4, Edibility.NON_CONSUMABLE), + ("Fragrance & Deodorants", 3, Edibility.NON_CONSUMABLE), + ("Household Cleaning", 2, Edibility.NON_CONSUMABLE), + ("Biscuits", 2, Edibility.CONSUMABLE), + ("Food - Soups & Sauces", 2, Edibility.CONSUMABLE), + ("Crackers", 2, Edibility.CONSUMABLE), + ("Health Care - Antiseptic", 2, Edibility.NON_CONSUMABLE), + ("Namkeen", 1, Edibility.CONSUMABLE), + ("Household - Air Freshener", 1, Edibility.NON_CONSUMABLE), + ("Flour & Grains", 1, Edibility.CONSUMABLE), + ("Staples", 1, Edibility.CONSUMABLE), + ("Personal Care", 1, Edibility.NON_CONSUMABLE), + ("Health Care - Digestive", 1, Edibility.NON_CONSUMABLE), +] + +# The two category values that genuinely carry no signal. "General" holds 40 +# rows of mixed food and non-food; "1".."5" are import damage on 17 rows. +# Both are meant to reach the title, so they are excluded here rather than +# given a verdict this file would have to invent. +UNDECIDABLE_CATEGORIES = ("General", "1", "2", "3", "4", "5") + +# Categories holding both food and non-food, so they carry no verdict of their +# own and are excluded from the table above. Found by running the purge audit +# before deleting anything - see test_baby_food_is_food. +MIXED_CATEGORIES = ("Baby Care", "Health Care - Ayurvedic") + + +@pytest.mark.parametrize("category,count,expected", LIVE_CATEGORIES) +def test_every_live_category_has_the_expected_verdict(category, count, expected): + verdict = classify_edibility(category) + assert verdict.edibility is expected, ( + f"{category!r} ({count} live rows) classified {verdict.edibility.value}: " + f"{verdict.reason}" + ) + + +@pytest.mark.parametrize( + "category", + [c for c in sorted(HSN_GST_TABLE) if c not in MIXED_CATEGORIES], +) +def test_every_hsn_category_resolves(category): + """Drift guard. Someone adding a category to the tax table and not here + would otherwise get a silent UNKNOWN, and UNKNOWN means no health score. + + The mixed categories are excluded because their UNKNOWN is the intended + answer - they are decided by the title, not by the category. + """ + assert classify_edibility(category).edibility is not Edibility.UNKNOWN + + +def test_lamp_oil_is_not_a_cooking_oil(): + """These must not collapse into each other. + + `resolve_hsn_gst`'s keyword fallbacks contain a greedy ("oil", "1517") + entry, which is chapter 15 - an edible fat. Reading HSN_GST_TABLE directly + instead of calling that resolver is what keeps lamp oil inedible. + """ + assert is_consumable("Cooking Oils", "Idhayam Sesame Oil 500ml") is True + assert is_non_consumable("Household - Lamp Oil", "Idhayam Lamp Oil 500ml") is True + + +def test_flowers_are_not_food(): + """HSN 0603 is chapter 06, which sits inside the naive 01-24 "food" range. + Flowers reach the Own Products table through the same produce lexicon as the + vegetables, so nothing upstream separates them - this is the separation.""" + assert is_non_consumable("Flowers", "Jasmine Garland") is True + assert is_consumable("Flowers", "Jasmine Garland") is False + + +def test_medicines_are_not_scored(): + """Ingested, but they publish dosage rather than a nutrition panel.""" + for category in ("Health Care - Cold & Cough", "Health Care - Digestive", + "Health Care - Antiseptic"): + assert is_non_consumable(category) is True, category + assert is_non_consumable("Health Care - Cold & Cough", "Dabur Honitus 1L") is True + + +def test_baby_food_is_food(): + """The error this classifier made on its first audit run, caught before a + single row was deleted. + + "Baby Care" held 21 live rows and a blanket non-consumable verdict. They are + Nestle Cerelac, Nan Pro and Lactogen - infant formula and baby cereal, among + the most heavily nutrition-labelled products sold in India - sitting beside a + bottle of baby oil. One verdict cannot cover both, so the category defers to + the title. + """ + assert is_consumable("Baby Care", "Nestle Cerelac 125g") is True + assert is_consumable("Baby Care", "Nestle Nan Pro 400 g") is True + assert is_consumable("Baby Care", "Nestle Lactogen 400 g") is True + assert is_non_consumable("Baby Care", "Johnson Baby Soap 100g") is True + assert is_non_consumable("Baby Care", "Pampers Diaper Large 20s") is True + + +def test_chyawanprash_is_food_but_cough_syrup_is_not(): + """Same shape as Baby Care: "Health Care - Ayurvedic" mixes a spoonable food + supplement with medicine, so it too defers to the title.""" + assert is_consumable("Health Care - Ayurvedic", + "Chyawanprash 100% Natural 70g") is True + + +@pytest.mark.parametrize("category", MIXED_CATEGORIES) +def test_a_mixed_category_carries_no_verdict_of_its_own(category): + """With no title to go on there is nothing to decide, and UNKNOWN neither + enriches nor deletes.""" + assert classify_edibility(category).edibility is Edibility.UNKNOWN + + +def test_health_drinks_are_food_despite_the_name(): + assert is_consumable("Health Drinks", "Horlicks Classic Malt 500g") is True + assert is_consumable("Health Foods", "Manna Health Mix 500g") is True + + +def test_unknown_is_neither_consumable_nor_non_consumable(): + """The asymmetry the purge script depends on: an unrecognised product is + never enriched AND never deleted. If these two ever become inverses, a + classifier miss turns into data loss.""" + verdict = classify_edibility("Zzz Unheard Of Category", "Mystery Item 1kg") + assert verdict.edibility is Edibility.UNKNOWN + assert is_consumable("Zzz Unheard Of Category", "Mystery Item 1kg") is False + assert is_non_consumable("Zzz Unheard Of Category", "Mystery Item 1kg") is False + + +def test_a_missing_category_falls_back_to_the_title(): + assert is_consumable("", "Amul Butter 500g") is True + assert is_consumable(None, "Toor Dhal 1kg") is True + assert is_non_consumable("", "Lifebuoy Soap 100g") is True + assert classify_edibility(None, "").edibility is Edibility.UNKNOWN + + +@pytest.mark.parametrize("junk", UNDECIDABLE_CATEGORIES) +def test_an_uninformative_category_defers_to_the_title(junk): + assert is_consumable(junk, "Aachi Sambar Powder 100g") is True + assert is_non_consumable(junk, "Lifebuoy Soap 100g") is True + + +def test_numeric_categories_are_reported_as_junk(): + """17 live rows carry a bare digit as their category. They are recognised so + the purge audit can report them for catalogue repair, rather than being + quietly accommodated.""" + assert is_junk_category("3") is True + assert is_junk_category("Dairy") is False + + +def test_a_toothpaste_brand_is_not_a_chocolate(): + """The regression that forced `exact_only` into `detect_category_from_text`. + + Its fuzzy fallback scores "colgate" at >=0.8 against the misspelling keyword + "choclate", so "Colgate-Palmolive Palmolive Naturals" - whose stored + category is the uninformative "General" - resolved to Chocolates and would + have been handed a health score. + """ + verdict = classify_edibility("General", "Colgate-Palmolive Palmolive Naturals") + assert verdict.edibility is not Edibility.CONSUMABLE + + +def test_soapnut_is_not_a_soap(): + """The old gate matched NON_FOOD_KEYWORDS by substring, so "soap" hit + *soapnut* (reetha). Matching here is whole-word.""" + assert is_non_consumable("Fruits & Vegetables", "Soapnut Reetha 500g") is False + + +@pytest.mark.parametrize("category,title", [ + ("Bath Soap", "Colgate-Palmolive Palmolive Naturals"), + ("Hair Care", "Cavinkare Nyle"), + ("Hair Care", "Cavinkare Nature's"), + ("Hair Care", "P G Pg Pantene"), + ("Personal Care - Mosquito Repellent", "Godrej Hit Spray"), +]) +def test_the_products_that_are_wrongly_scored_today_all_classify_non_food(category, title): + """Named individually because each one is a live row with a health score.""" + assert is_non_consumable(category, title) is True + + +def test_the_legacy_gate_still_answers(): + """`_looks_non_food` is kept as a delegate; nothing outside its module + imports it, but silently changing a public-looking name is worse than + keeping it honest.""" + assert nds._looks_non_food("Lifebuoy Soap", "Bath Soap") is True + assert nds._looks_non_food("Amul Butter", "Dairy") is False + + +def test_the_verdict_map_has_no_contradictions(): + """A category listed as both consumable and non-consumable would resolve by + dict-ordering luck.""" + assert len(CATEGORY_VERDICTS) == len(set(CATEGORY_VERDICTS)) + assert all(isinstance(v, Edibility) for v in CATEGORY_VERDICTS.values()) diff --git a/tests/test_health_scores_api.py b/tests/test_health_scores_api.py new file mode 100644 index 0000000..2ee1ac5 --- /dev/null +++ b/tests/test_health_scores_api.py @@ -0,0 +1,340 @@ +"""GET /api/nutrition/health-scores - every scored consumable product. + +The endpoint a consumer reads to display health scores across the catalogue. +Three promises are load-bearing, and each is tested against the SQL rather than +the response, because that is where each one can silently stop being true: + + 1. Only CONSUMABLE products. Enforced in the WHERE clause, not by filtering a + fetched page, so `total` and `items` describe the same population. + 2. Only SCORED products - no null health_score, whatever the caller sorts by. + 3. Stable paging. A total ordering, so OFFSET cannot repeat or skip a product + when scores tie. + +No database and no network: the connection is a recorder, so the assertions are +about the exact SQL that would be sent. +""" +from __future__ import annotations + +import pytest + +from app.api.routers import nutrition as nutrition_router +from app.services import nutrition_db + + +# --------------------------------------------------------------------------- +# Recorder +# --------------------------------------------------------------------------- +class FakeCursor: + def __init__(self, calls, rows): + self.calls = calls + self._rows = rows + self.description = None + + def execute(self, sql, params=None): + self.calls.append((" ".join(str(sql).split()), list(params or []))) + + def fetchall(self): + return list(self._rows) + + def fetchone(self): + return (len(self._rows),) + + def __enter__(self): + return self + + def __exit__(self, *exc): + return False + + +class FakeConn: + def __init__(self, rows=()): + self.calls = [] + self.rows = list(rows) + + def cursor(self, *a, **k): + return FakeCursor(self.calls, self.rows) + + def __enter__(self): + return self + + def __exit__(self, *exc): + return False + + def close(self): + pass + + +@pytest.fixture +def db(monkeypatch): + conn = FakeConn() + monkeypatch.setattr(nutrition_db, "_connect", lambda: conn) + return conn + + +def _sql(conn): + assert conn.calls, "no statement was executed" + return conn.calls[-1][0] + + +# --------------------------------------------------------------------------- +# Promise 1: only consumables +# --------------------------------------------------------------------------- +def test_the_default_admits_only_confirmed_consumables(): + where, params = nutrition_db._health_score_filters() + + assert "f.edibility = %s" in where + assert nutrition_db.CONSUMABLE in params + + +def test_include_unknown_still_refuses_confirmed_non_consumables(): + """The opt-in is for products the classifier could not decide on. There is + deliberately no argument that lets soap back in.""" + where, params = nutrition_db._health_score_filters(include_unknown=True) + + assert "f.edibility IS DISTINCT FROM %s" in where + assert nutrition_db.NON_CONSUMABLE in params + assert nutrition_db.CONSUMABLE not in params + + +def test_a_null_edibility_is_treated_as_unknown_not_as_food(): + """Every row written before the column existed carries NULL. A plain != + would drop those rows out of the comparison entirely; IS DISTINCT FROM + keeps them, which is what makes `include_unknown` mean what it says.""" + where, _ = nutrition_db._health_score_filters(include_unknown=True) + edibility_clause = [c for c in where.split(" AND ") if "edibility" in c] + + assert edibility_clause == ["f.edibility IS DISTINCT FROM %s"] + assert "!=" not in edibility_clause[0] + + +@pytest.mark.parametrize("kwargs", [ + {}, {"include_unknown": True}, {"brand": "Amul"}, {"category": "Biscuits"}, + {"min_score": 10}, {"max_score": 90}, {"brand": "Amul", "include_unknown": True}, +]) +def test_edibility_is_constrained_no_matter_which_filters_are_used(kwargs): + where, _ = nutrition_db._health_score_filters(**kwargs) + + assert "f.edibility" in where + + +# --------------------------------------------------------------------------- +# Promise 2: only scored products +# --------------------------------------------------------------------------- +@pytest.mark.parametrize("sort_by", [ + "health_score", "nutrition_score", "protein", "fiber", "sugar", "sodium", + "calcium", "iron", "vitamin_c", "calories", "product_name", "brand", "category", +]) +def test_no_sort_column_can_admit_an_unscored_product(db, sort_by): + """`query_products` derives this predicate from the sort column, so sorting + it by protein returns rows with no health_score at all. Here the promise is + stated outright and cannot be sorted away.""" + nutrition_db.query_health_scores(sort_by=sort_by) + + assert "i.health_score IS NOT NULL" in _sql(db) + + +def test_the_join_is_inner_so_a_product_with_no_insights_row_cannot_appear(db): + nutrition_db.query_health_scores() + sql = _sql(db) + + assert "LEFT JOIN" not in sql + assert "JOIN nutrition_insights" in sql + + +def test_an_unknown_sort_column_falls_back_rather_than_reaching_the_query(db): + """The allow-list is what stops `sort_by` being an injection point.""" + nutrition_db.query_health_scores(sort_by="; DROP TABLE nutrition_facts; --") + sql = _sql(db) + + assert "DROP TABLE" not in sql + assert "ORDER BY i.health_score" in sql + + +# --------------------------------------------------------------------------- +# Promise 3: stable paging +# --------------------------------------------------------------------------- +def test_the_ordering_is_total_so_tied_scores_cannot_shuffle_between_pages(db): + """Without the primary-key tiebreaker Postgres may return equal + health_scores in a different order on each call, and OFFSET paging then + hands the caller one product twice and never shows another.""" + nutrition_db.query_health_scores() + sql = _sql(db) + + order = sql[sql.index("ORDER BY"):] + assert "f.brand ASC" in order and "f.image_id ASC" in order + + +def test_the_count_and_the_listing_are_filtered_identically(db): + """A total that describes a different population from the rows beside it is + worse than no total: it tells a client to keep paging past the end, or to + stop early.""" + kwargs = {"brand": "Amul", "category": "Dairy", "min_score": 20} + + nutrition_db.query_health_scores(**kwargs) + list_where = _sql(db).split("WHERE", 1)[1].split("ORDER BY", 1)[0].strip() + list_params = db.calls[-1][1][:-2] # minus LIMIT/OFFSET + + nutrition_db.count_health_scores(**kwargs) + count_where = _sql(db).split("WHERE", 1)[1].strip() + + assert list_where == count_where + assert list_params == db.calls[-1][1] + + +# --------------------------------------------------------------------------- +# The route +# --------------------------------------------------------------------------- +_ROW = { + "brand": "Own Products", "image_id": "own_products_apple", "product_name": "Apple", + "category": "Fruits & Vegetables", "edibility": "consumable", + "data_status": "verified", "data_source": "usda_fdc", + "source_url": "https://fdc.nal.usda.gov/food-details/171688", + "calories_kcal": 52.0, "protein_g": 0.26, "dietary_fiber_g": 2.4, + "total_sugar_g": 10.4, "sodium_mg": 1.0, + "nutrition_score": 61.2, "health_score": 57.4, + "scoring_version": "v1.0-fsa-eu-thresholds", + "diet_tags": ["High Fiber"], "allergens": [], +} + + +@pytest.fixture +def rows(monkeypatch): + """Serve a fixed page, and record the filters the route passed down.""" + seen = {} + + def _query(**kwargs): + seen.update(kwargs) + return [dict(_ROW)] + + def _export(**kwargs): + return iter([ + dict(_ROW), + dict(_ROW, product_name="Banana", health_score=82.0, diet_tags=["A", "B"]), + ]) + + monkeypatch.setattr(nutrition_router.nutrition_db, "query_health_scores", _query) + monkeypatch.setattr(nutrition_router.nutrition_db, "count_health_scores", lambda **k: 892) + monkeypatch.setattr(nutrition_router.nutrition_db, "iter_health_scores", _export) + return seen + + +def test_the_envelope_carries_the_total_of_all_matches_not_of_the_page(client, rows): + body = client.get("/api/nutrition/health-scores?limit=1").json() + + assert body["total"] == 892 + assert body["limit"] == 1 + assert body["offset"] == 0 + assert len(body["items"]) == 1 + + +def test_the_filters_reach_the_query_layer(client, rows): + client.get("/api/nutrition/health-scores?brand=Amul&category=Dairy" + "&min_score=10&max_score=90&include_unknown=true") + + assert rows["brand"] == "Amul" + assert rows["category"] == "Dairy" + assert rows["min_score"] == 10 + assert rows["max_score"] == 90 + assert rows["include_unknown"] is True + + +def test_provenance_travels_with_the_number(client, rows): + """A score is only as good as the source it was computed from, and the + consumer has to be able to show that alongside it.""" + item = client.get("/api/nutrition/health-scores").json()["items"][0] + + assert item["data_source"] == "usda_fdc" + assert item["source_url"].startswith("https://fdc.nal.usda.gov/") + assert item["scoring_version"] == "v1.0-fsa-eu-thresholds" + + +@pytest.mark.parametrize("score,band", [ + (100.0, "excellent"), (80.0, "excellent"), (79.9, "good"), + (60.0, "good"), (59.9, "fair"), (40.0, "fair"), (39.9, "poor"), (0.0, "poor"), +]) +def test_the_band_boundaries(score, band): + assert nutrition_router._health_band(score) == band + + +def test_the_healthy_boundary_matches_the_one_the_rest_of_the_api_uses(): + """`store_healthy_distribution` counts health_score >= 60 as healthy. Two + parts of the same API disagreeing about that would be indefensible.""" + assert nutrition_router._health_band(60.0) == "good" + assert nutrition_router._health_band(59.99) == "fair" + + +def test_over_the_page_ceiling_is_rejected_rather_than_silently_capped(client, rows): + assert client.get("/api/nutrition/health-scores?limit=501").status_code == 422 + assert client.get("/api/nutrition/health-scores?limit=500").status_code == 200 + + +def test_a_negative_offset_is_rejected(client, rows): + assert client.get("/api/nutrition/health-scores?offset=-1").status_code == 422 + + +def test_an_out_of_range_score_filter_is_rejected(client, rows): + assert client.get("/api/nutrition/health-scores?min_score=101").status_code == 422 + + +# --------------------------------------------------------------------------- +# CSV export +# --------------------------------------------------------------------------- +def test_csv_returns_every_row_and_ignores_the_page_size(client, rows): + """One request for the whole list is the entire point of the format.""" + response = client.get("/api/nutrition/health-scores?format=csv&limit=1") + + assert response.status_code == 200 + assert response.headers["content-type"].startswith("text/csv") + assert "attachment" in response.headers["content-disposition"] + + lines = [line for line in response.text.splitlines() if line] + assert len(lines) == 3, "header plus both rows, despite limit=1" + + +def test_the_csv_header_names_the_columns(client, rows): + header = client.get("/api/nutrition/health-scores?format=csv").text.splitlines()[0] + + assert header.split(",")[:4] == ["brand", "image_id", "product_name", "category"] + assert "health_score" in header and "health_band" in header + + +def test_csv_carries_the_derived_band(client, rows): + lines = client.get("/api/nutrition/health-scores?format=csv").text.splitlines() + + assert "fair" in lines[1] # 57.4 + assert "excellent" in lines[2] # 82.0 + + +def test_a_postgres_array_becomes_a_readable_cell(client, rows): + """str() on a TEXT[] renders it with commas inside, which a spreadsheet + then splits across columns.""" + lines = client.get("/api/nutrition/health-scores?format=csv").text.splitlines() + + assert "A|B" in lines[2] + assert "'A'" not in lines[2] + + +def test_an_unrecognised_format_is_rejected(client, rows): + assert client.get("/api/nutrition/health-scores?format=xml").status_code == 422 + + +# --------------------------------------------------------------------------- +# The response model is the contract +# --------------------------------------------------------------------------- +def test_a_null_health_score_is_a_loud_failure_not_a_blank_cell(monkeypatch, client): + """`health_score` is required on the response model precisely so that a + query which lost its `IS NOT NULL` guarantee cannot serve nulls quietly.""" + monkeypatch.setattr(nutrition_router.nutrition_db, "count_health_scores", lambda **k: 1) + monkeypatch.setattr(nutrition_router.nutrition_db, "query_health_scores", + lambda **k: [dict(_ROW, health_score=None)]) + + with pytest.raises(Exception): + client.get("/api/nutrition/health-scores") + + +def test_the_route_is_not_shadowed_by_the_product_detail_catch_all(client, rows): + """`/{brand}/{image_id}` sits below this route in the same router. A static + path registered after it resolves as brand='health-scores'.""" + body = client.get("/api/nutrition/health-scores").json() + + assert "items" in body and "total" in body diff --git a/tests/test_nutrition_consumable_gate.py b/tests/test_nutrition_consumable_gate.py new file mode 100644 index 0000000..1198ed6 --- /dev/null +++ b/tests/test_nutrition_consumable_gate.py @@ -0,0 +1,199 @@ +"""The gate that stops a bar of soap acquiring a health score. + +WHAT WENT WRONG +--------------- +`nutrition_scoring` is careful: it refuses to score when the numbers are +missing, and every sentence it emits traces to a published threshold. None of +that helps if the product is not food. Measured on the live catalogue, these +rows had a health score: + + Colgate-Palmolive Palmolive Naturals soap + Cavinkare Nyle / Nature's, P&G Pantene shampoo + Godrej Hit Spray mosquito repellent, recorded at 291 kcal + +WHERE THE GATE HAD TO GO +------------------------ +Three places, and the third is the one that matters: + + * `fetch_verified_nutrition` - skip the network call + * `fetch_verified_nutrition_by_barcode` - had NO gate at all, and a barcode + match records `match_confidence` 0.95, higher than anything the name + search produces + * `enrich_one_product` - the only place that knows + (brand, image_id) and can therefore refuse to WRITE A ROW + +No network, no database: both boundaries are stubbed. +""" +from __future__ import annotations + +import pytest + +from app.services import nutrition_data_service as nds +from app.services import nutrition_enrichment_service as nes +from app.services.enrichment.barcode.sources import open_food_facts as off + + +class _Resp: + def __init__(self, payload, status_code=200): + self._payload = payload + self.status_code = status_code + + def json(self): + return self._payload + + +# --------------------------------------------------------------------------- +# The retrieval paths +# --------------------------------------------------------------------------- +@pytest.mark.parametrize("title,category", [ + ("Colgate-Palmolive Palmolive Naturals", "Bath Soap"), + ("Cavinkare Nyle", "Hair Care"), + ("P G Pg Pantene", "Hair Care"), + ("Godrej Hit Spray", "Personal Care - Mosquito Repellent"), + ("Jasmine Garland", "Flowers"), +]) +def test_the_name_search_makes_no_call_for_a_non_consumable(monkeypatch, title, category): + called = [] + monkeypatch.setattr(nds, "_search_openfoodfacts", + lambda *a, **k: called.append(1) or []) + + facts = nds.fetch_verified_nutrition("Whoever", title, category) + + assert facts["data_status"] == "unavailable" + assert not called, f"queried Open Food Facts about {title!r}" + + +def test_the_barcode_path_also_refuses_a_non_consumable(monkeypatch): + """The hole. This path checked only USE_OPEN_FACTS and barcode presence, so + a shampoo whose barcode Open Beauty Facts happens to know acquired a full + nutrition row at confidence 0.95.""" + called = [] + monkeypatch.setattr(off.requests, "get", + lambda *a, **k: called.append(1) or _Resp({"status": 0})) + + facts = nds.fetch_verified_nutrition_by_barcode( + "8901030865278", "Dove", "Dove Shampoo 340ml", "340ml", "Hair Care") + + assert facts["data_status"] == "unavailable" + assert not called + + +def test_a_real_food_still_reaches_open_food_facts(monkeypatch): + """The gate must not be so eager that it starves the happy path.""" + called = [] + monkeypatch.setattr(nds, "_search_openfoodfacts", + lambda *a, **k: called.append(1) or []) + + nds.fetch_verified_nutrition("Amul", "Amul Butter 500g", "Dairy") + + assert called, "a dairy product should still be looked up" + + +# --------------------------------------------------------------------------- +# The write gate +# --------------------------------------------------------------------------- +@pytest.fixture +def no_writes(monkeypatch): + """Records every attempted write instead of performing it.""" + writes = {"facts": [], "insights": []} + monkeypatch.setattr(nes.nutrition_db, "upsert_nutrition_facts", + lambda f: writes["facts"].append(f) or True) + monkeypatch.setattr(nes.nutrition_db, "upsert_nutrition_insights", + lambda i: writes["insights"].append(i) or True) + monkeypatch.setattr(nes.nutrition_db, "get_nutrition_facts", lambda b, i: None) + return writes + + +def test_a_non_consumable_never_gets_a_row_written(no_writes, monkeypatch): + monkeypatch.setattr(nes.nutrition_data_service, "fetch_verified_nutrition", + lambda *a, **k: pytest.fail("retrieval should not be reached")) + + status = nes.enrich_one_product( + "Colgate-Palmolive", "colgate_palmolive_palmolive_naturals", + "Colgate-Palmolive Palmolive Naturals", "Bath Soap") + + assert status == nes.SKIPPED_NON_CONSUMABLE + assert no_writes["facts"] == [] + assert no_writes["insights"] == [], ( + "an insights row with a null score still makes the product appear in " + "query_products and the analytics leaderboards" + ) + + +def test_skipping_is_not_reported_as_unavailable(no_writes, monkeypatch): + """'We did not look' and 'we looked and found nothing' are different facts, + and the second invites a pointless retry.""" + monkeypatch.setattr(nes, "_brands_to_enrich", lambda brands, inc: ["Acme"]) + monkeypatch.setattr(nes, "get_products_by_brand", lambda brand: [ + {"image_id": "a1", "title": "Acme Shampoo 200ml", "category": "Hair Care"}, + {"image_id": "a2", "title": "Acme Marigold Garland", "category": "Flowers"}, + ]) + + result = nes.enrich_all_products(generate_narrative=False) + + assert result.skipped_non_consumable == 2 + assert result.unavailable == 0 + assert result.total_products == 0, ( + "total_products should describe the work actually done, so the progress " + "percentage means something" + ) + + +def test_a_consumable_is_still_enriched(no_writes, monkeypatch): + monkeypatch.setattr(nes.nutrition_data_service, "fetch_verified_nutrition", + lambda *a, **k: {"data_status": "verified", + "data_source": "openfoodfacts", + "protein_g": 8.0, "calories_kcal": 717.0}) + + status = nes.enrich_one_product( + "Amul", "amul_butter_500g", "Amul Butter 500g", "Dairy", + generate_narrative=False) + + assert status == "verified" + assert len(no_writes["facts"]) == 1 + assert no_writes["facts"][0]["brand"] == "Amul" + + +def test_allergen_source_names_the_database_the_facts_came_from(no_writes, monkeypatch): + """It was hardcoded to "openfoodfacts", which was true when OFF was the only + source and would credit the wrong database once USDA joined the cascade.""" + monkeypatch.setattr(nes.nutrition_data_service, "fetch_verified_nutrition", + lambda *a, **k: {"data_status": "verified", + "data_source": "usda_fdc", + "allergens": ["Milk"], + "calories_kcal": 52.0}) + + nes.enrich_one_product("Own Products", "own_products_apple", "Apple", + "Fruits & Vegetables", generate_narrative=False) + + assert no_writes["insights"][0]["allergen_source"] == "usda_fdc" + + +def test_no_allergens_still_reads_unavailable(no_writes, monkeypatch): + monkeypatch.setattr(nes.nutrition_data_service, "fetch_verified_nutrition", + lambda *a, **k: {"data_status": "verified", + "data_source": "usda_fdc", + "allergens": [], "calories_kcal": 52.0}) + + nes.enrich_one_product("Own Products", "own_products_apple", "Apple", + "Fruits & Vegetables", generate_narrative=False) + + assert no_writes["insights"][0]["allergen_source"] == "unavailable" + + +# --------------------------------------------------------------------------- +# Reaching brands ACTIVE_BRANDS hides +# --------------------------------------------------------------------------- +def test_an_explicit_brand_list_bypasses_active_brands(monkeypatch): + """ACTIVE_BRANDS is right for what the app serves and wrong for a backfill: + with it set to four brands, Britannia and Parle can never be enriched.""" + monkeypatch.setattr(nes, "list_available_brands", + lambda: pytest.fail("should not consult ACTIVE_BRANDS")) + + assert nes._brands_to_enrich(["Britannia", " Parle "], False) == ["Britannia", "Parle"] + + +def test_the_default_still_honours_active_brands(monkeypatch): + monkeypatch.setattr(nes, "list_available_brands", lambda: ["Amul", "Cadbury"]) + + assert nes._brands_to_enrich(None, False) == ["Amul", "Cadbury"] diff --git a/tests/test_nutrition_usda.py b/tests/test_nutrition_usda.py new file mode 100644 index 0000000..43dbe55 --- /dev/null +++ b/tests/test_nutrition_usda.py @@ -0,0 +1,290 @@ +"""Nutrition for loose commodities, from USDA FoodData Central. + +WHY THIS SOURCE EXISTS +---------------------- +Open Food Facts catalogues PACKAGED products. It has no entry for a raw apple, +so every one of the 159 rows in `brand_own_products` - all the fruit, +vegetables, greens, fish and eggs the shop sells loose - had no nutrition, no +benefits and no health score at all. + +NO NETWORK ANYWHERE IN THIS FILE. The pinned snapshot is built from USDA's open +bulk download, so the default path makes no calls and needs no API key. Several +tests below prove that by making `requests.get` raise. +""" +from __future__ import annotations + +import pytest + +from app.services import nutrition_data_service as nds +from app.services import nutrition_usda_service as usda +from app.services import produce_reference + + +def _name_path_keys(): + """The keys `fetch_verified_nutrition` produces on a successful match. + Duplicated from test_nutrition_by_barcode.py, matching that file's + self-contained style.""" + return { + "data_status", "data_source", "source_ref", "source_url", + "match_confidence", "serving_size_g", "serving_size_label", + "extended_nutrients", "per_serving", "ingredients_text", + "off_nutriscore", "allergens", "off_labels_tags", + "off_ingredients_analysis_tags", "off_categories_tags", "fetched_at", + } | set(nds._build_flat_fields({}, "100g")) + + +@pytest.fixture +def no_network(monkeypatch): + """Any outbound call is a failure, not a slow test.""" + def _boom(*a, **k): + raise AssertionError("the USDA path must not touch the network") + monkeypatch.setattr(usda.requests, "get", _boom) + + +# --------------------------------------------------------------------------- +# Shape +# --------------------------------------------------------------------------- +def test_the_usda_path_returns_the_same_shape_as_the_name_path(no_network): + """Both feed the same `upsert_nutrition_facts`, whose INSERT is built from a + fixed column list - a key either path grew alone would be silently dropped.""" + facts = usda.fetch_verified_nutrition_usda("Apple", "Fruits & Vegetables") + + assert set(facts) == _name_path_keys() + + +def test_the_off_only_fields_are_present_but_empty(no_network): + """USDA has no Nutri-Score and no ingredient-derived vegan flag. `[]` says + "USDA told us nothing", which is what `classify_diet_tags` reads; omitting + the keys would break shape parity.""" + facts = usda.fetch_verified_nutrition_usda("Apple", "Fruits & Vegetables") + + assert facts["off_nutriscore"] is None + assert facts["ingredients_text"] is None + assert facts["allergens"] == [] + assert facts["off_labels_tags"] == [] + + +# --------------------------------------------------------------------------- +# Values +# --------------------------------------------------------------------------- +def test_a_curated_commodity_resolves_offline(no_network): + """The strongest test here: it proves the offline path is genuinely offline. + + Values are USDA 171688, "Apples, raw, with skin". + """ + facts = usda.fetch_verified_nutrition_usda("Apple", "Fruits & Vegetables") + + assert facts["data_status"] == "verified" + assert facts["data_source"] == "usda_fdc" + assert facts["source_ref"] == "171688" + assert facts["source_url"] == "https://fdc.nal.usda.gov/food-details/171688" + assert facts["calories_kcal"] == 52.0 + assert facts["dietary_fiber_g"] == 2.4 + assert facts["vitamin_c_mg"] == 4.6 + + +@pytest.mark.parametrize("name,category,kcal", [ + ("Banana", "Fruits & Vegetables", 89.0), + ("Spinach", "Fresh Herbs & Greens", 23.0), + ("Fish Salmon", "Fish & Seafood", 208.0), + ("Egg", "Eggs", 143.0), +]) +def test_the_headline_commodities_all_resolve(no_network, name, category, kcal): + facts = usda.fetch_verified_nutrition_usda(name, category) + + assert facts["data_status"] == "verified" + assert facts["calories_kcal"] == kcal + + +def test_a_size_variant_finds_its_base_commodity(no_network): + """Catalogue rows carry varietal and pack suffixes; the curated table is + keyed on the commodity.""" + for name in ("Mango Alphonso", "Mango Totapuri 1kg", "Banana Robusta"): + assert usda.fetch_verified_nutrition_usda(name)["data_status"] == "verified", name + + +def test_an_uncurated_commodity_is_unavailable_not_invented(no_network): + """USDA has no entry for amla (Phyllanthus emblica); its "Gooseberries, + raw" is Ribes, a different genus. Substituting it would put the wrong + vitamin C on the row by an order of magnitude.""" + facts = usda.fetch_verified_nutrition_usda("Amla", "Fruits & Vegetables") + + assert facts["data_status"] == "unavailable" + assert "calories_kcal" not in facts + + +def test_a_missing_nutrient_stays_none_rather_than_zero(no_network): + """The Feature-15 rule: absent is not zero.""" + facts = usda.fetch_verified_nutrition_usda("Apple", "Fruits & Vegetables") + + assert facts["added_sugar_g"] is None + assert facts["calories_kcal"] is not None + + +# --------------------------------------------------------------------------- +# The mapping layer +# --------------------------------------------------------------------------- +def _food(nid, unit, amount): + return {"description": "test", "nutrients": [{"id": nid, "unit": unit, "amount": amount}]} + + +def test_the_unit_is_asserted_not_assumed(): + """USDA has served the same nutrient id in different units across dataset + revisions. Sodium read as grams and scaled would be wrong by 1000x and + would still look like a plausible number.""" + good = usda._build_fields(_food(1093, "MG", 79.0)) + assert good["sodium_mg"] == 79.0 + + bad = usda._build_fields(_food(1093, "G", 79.0)) + assert bad["sodium_mg"] is None, "a wrong unit must drop the value, not rescale it" + + +def test_international_units_are_refused_rather_than_converted(): + """The IU-to-microgram factor depends on the vitamer (retinol vs + beta-carotene), so any single factor is an estimate.""" + facts = usda._build_fields(_food(1104, "IU", 3000)) + + assert facts["vitamin_a_mcg"] is None + + +def test_both_api_response_shapes_are_understood(): + """/food/{id} nests the nutrient; /foods/search flattens it; the snapshot + stores a third, trimmed shape.""" + nested = {"foodNutrients": [{"nutrient": {"id": 1008, "unitName": "KCAL"}, "amount": 52.0}]} + flat = {"foodNutrients": [{"nutrientId": 1008, "unitName": "KCAL", "value": 52.0}]} + pinned = {"nutrients": [{"id": 1008, "unit": "kcal", "amount": 52.0}]} + + for shape in (nested, flat, pinned): + assert usda._build_fields(shape)["calories_kcal"] == 52.0 + + +def test_micro_sign_and_ascii_micrograms_both_parse(): + """The bulk download writes "µg"; the live API writes "UG".""" + assert usda._build_fields(_food(1106, "µg", 12.0))["vitamin_a_mcg"] == 12.0 + assert usda._build_fields(_food(1106, "UG", 12.0))["vitamin_a_mcg"] == 12.0 + + +def test_per_serving_is_arithmetic_on_given_values(no_network): + facts = usda.fetch_verified_nutrition_usda("Apple", "Fruits & Vegetables") + + if facts["serving_size_g"]: + factor = facts["serving_size_g"] / 100.0 + assert facts["per_serving"]["calories_kcal"] == round(52.0 * factor, 3) + + +def test_disabled_usda_makes_no_lookup(monkeypatch): + monkeypatch.setattr(usda, "USE_USDA_FDC", False) + monkeypatch.setattr(usda, "get_food", + lambda *a, **k: pytest.fail("should not have looked up")) + + assert usda.fetch_verified_nutrition_usda("Apple")["data_status"] == "unavailable" + + +def test_the_live_api_is_not_called_without_a_key(monkeypatch): + """The snapshot is the default path; the key is only for ids it lacks.""" + monkeypatch.setattr(usda, "USDA_FDC_API_KEY", "") + monkeypatch.setattr(usda.requests, "get", + lambda *a, **k: pytest.fail("called the API with no key")) + + assert usda.get_food(999999999) is None + + +# --------------------------------------------------------------------------- +# Dispatch +# --------------------------------------------------------------------------- +def test_produce_goes_to_usda_and_not_to_open_food_facts(monkeypatch): + monkeypatch.setattr(nds, "_search_openfoodfacts", + lambda *a, **k: pytest.fail("OFF must not be asked about a raw banana")) + + facts = nds.fetch_verified_nutrition("Own Products", "Banana", "Fruits & Vegetables") + + assert facts["data_source"] == "usda_fdc" + + +def test_a_branded_packaged_product_never_reaches_usda(monkeypatch): + """The reverse fallback is deliberately absent: a packaged biscuit must + never be handed a raw-commodity number.""" + monkeypatch.setattr(usda, "fetch_verified_nutrition_usda", + lambda *a, **k: pytest.fail("USDA must not be asked about a biscuit")) + monkeypatch.setattr(nds, "_search_openfoodfacts", lambda *a, **k: []) + + nds.fetch_verified_nutrition("Britannia", "Britannia Good Day 200g", "Biscuits & Cookies") + + +def test_unbranded_pantry_staples_still_use_open_food_facts(monkeypatch): + """`Own Products` is the bucket for EVERY unbranded row, including "Toor + Dhal 1kg" and "Sugar 1kg". Dispatching on the brand rather than the category + would send those to the wrong source.""" + called = [] + monkeypatch.setattr(nds, "_search_openfoodfacts", + lambda *a, **k: called.append(1) or []) + + nds.fetch_verified_nutrition("Own Products", "Toor Dhal 1kg", "Pulses, Grains & Spices") + + assert called + + +def test_produce_misfiled_under_a_junk_category_still_reaches_usda(monkeypatch): + """17 live rows carry a bare digit as their category. The commodity lexicon + is the second opinion that rescues them.""" + monkeypatch.setattr(nds, "_search_openfoodfacts", lambda *a, **k: []) + + facts = nds.fetch_verified_nutrition("Own Products", "Banana", "3") + + assert facts["data_source"] == "usda_fdc" + + +# --------------------------------------------------------------------------- +# The curated table itself +# --------------------------------------------------------------------------- +def test_every_snapshot_reference_is_pinned(): + """A half-refreshed pair - an id in the map with no snapshot entry - would + silently fall back to the live API, or to nothing.""" + for name, entry in produce_reference.all_entries().items(): + fdc_id = entry.get("usda_fdc_id") + if fdc_id: + assert usda.get_food(fdc_id), f"{name} references unpinned FDC id {fdc_id}" + + +def test_every_pinned_food_description_matches_the_curated_one(): + """Catches a snapshot rebuilt against a USDA revision that reassigned an id.""" + for name, entry in produce_reference.all_entries().items(): + fdc_id = entry.get("usda_fdc_id") + if not fdc_id: + continue + assert usda.get_food(fdc_id)["description"] == entry["usda_description"], name + + +def test_every_catalogue_row_has_an_image(): + """159 rows, 159 images. The whole point of the table.""" + entries = produce_reference.all_entries() + missing = [n for n, e in entries.items() if not e.get("image_url")] + + assert missing == [] + assert len(entries) == 159 + + +def test_no_image_comes_from_a_cosmetics_or_packaged_goods_database(): + """openbeautyfacts supplied the stored photo for Banana, Orange, Papaya, + Guava, Lemon and twenty more.""" + for name, entry in produce_reference.all_entries().items(): + url = entry["image_url"].lower() + assert "openbeautyfacts" not in url, name + assert "openproductsfacts" not in url, name + assert "openfoodfacts" not in url, name + + +def test_every_image_is_https(): + """An http image is blocked as mixed content and renders blank.""" + for name, entry in produce_reference.all_entries().items(): + assert entry["image_url"].startswith("https://"), name + + +def test_lookup_ignores_pack_sizes(): + assert produce_reference.usda_fdc_id("Apple 1kg") == 171688 + assert produce_reference.usda_fdc_id("Apple") == 171688 + + +def test_an_unknown_commodity_returns_nothing(): + assert produce_reference.lookup("Britannia Good Day Biscuits") is None + assert produce_reference.lookup("") is None diff --git a/tests/test_produce_image_search.py b/tests/test_produce_image_search.py new file mode 100644 index 0000000..5f25916 --- /dev/null +++ b/tests/test_produce_image_search.py @@ -0,0 +1,163 @@ +"""Why a search for an apple returned a bottle of juice. + +THE DEFECT CHAIN, as measured against the live catalogue +-------------------------------------------------------- +1. `image_search._AMBIQUITY_HINTS` maps the token "apple" to the context hint + "fruit juice". The table was written for packaged sub-brands - Cadbury Perk, + Britannia Tiger - where the word needs steering away from its everyday + meaning. For the Own Products bucket the word IS the item, so the hint + inverts the query. + +2. The hint was applied by SUBSTRING, so `Pineapple`, `Custard Apple`, + `Buttermilk`, `Baby Corn` and `Eggs White` inherited hints too. + +3. `repair_brand_images` passed the table suffix "own_products" as the brand, so + the query became "own_products Apple fruit juice" - and its corroboration + guard derived the token "products", which matches `/images/products/` and + `/cdn/shop/products/`, i.e. every Open*Facts and Shopify URL ever served. + +The stored primary image for `Apple` was a 2-litre juice bottle. For `Palak` it +was a photograph of a person. Every URL asserted in this file is one that was +actually in the database. + +No network: nothing here calls out. +""" +from __future__ import annotations + +import pytest + +from app.services import image_search +from app.services.image_search import _clean_search_title + + +# --------------------------------------------------------------------------- +# The hint table +# --------------------------------------------------------------------------- +@pytest.mark.parametrize("title,unwanted", [ + ("Pineapple", "fruit juice"), + ("Custard Apple", "fruit juice"), + ("Apple Green", "fruit juice"), + ("Buttermilk", "chocolate dairy"), + ("Baby Corn", "snack flakes"), + ("Sweet Corn", "snack flakes"), + ("Eggs White", "toothpaste dental"), +]) +def test_produce_never_inherits_a_commodity_hint(title, unwanted): + """In produce mode the table is skipped wholesale, because every key in it + assumes the word names a brand rather than the thing itself.""" + assert unwanted not in _clean_search_title(title, produce=True) + assert _clean_search_title(title, produce=True) == title + + +def test_a_substring_no_longer_drags_in_a_hint(): + """`"apple" in "pineapple"` is True, which is how a pineapple came to be + searched for as juice. Whole-word matching is the fix.""" + assert _clean_search_title("Pineapple") == "Pineapple" + assert _clean_search_title("Buttermilk") == "Buttermilk" + + +def test_the_hint_table_still_works_for_the_brands_it_was_written_for(): + """The fix must not disarm the table - these are real disambiguations.""" + assert "chocolate wafer" in _clean_search_title("Cadbury Perk Crunch") + assert "biscuit cookies" in _clean_search_title("Britannia Good Day Biscuits") + assert "fruit juice" in _clean_search_title("Apple"), ( + "a bare 'Apple' outside produce mode is still an ambiguous brand word" + ) + + +# --------------------------------------------------------------------------- +# Source selection +# --------------------------------------------------------------------------- +def test_produce_does_not_query_the_packaged_goods_databases(monkeypatch): + """Open*Facts photographs PACKAGING. Asked about a banana it answers with + whatever carton mentions one - which is how openbeautyfacts, a COSMETICS + database, supplied the stored image for Banana, Orange, Papaya, Guava, + Lemon, Pear, Plum, Apricot, Cherry, Litchi and more. + """ + calls = [] + monkeypatch.setattr(image_search, "find_images_openfacts", + lambda *a, **k: calls.append("openfacts") or []) + monkeypatch.setattr(image_search, "find_images_wikimedia", lambda *a, **k: []) + monkeypatch.setattr(image_search, "find_all_image_urls_ddg", lambda *a, **k: []) + monkeypatch.setattr(image_search, "find_all_image_urls_google_cse", lambda *a, **k: []) + monkeypatch.setattr(image_search, "find_images_playwright", lambda *a, **k: []) + + image_search.find_all_image_urls("Banana", brand="", validate=False, produce=True) + + assert "openfacts" not in calls + + +def test_a_branded_product_still_queries_open_facts(monkeypatch): + calls = [] + monkeypatch.setattr(image_search, "find_images_openfacts", + lambda *a, **k: calls.append("openfacts") or []) + monkeypatch.setattr(image_search, "find_images_wikimedia", lambda *a, **k: []) + monkeypatch.setattr(image_search, "find_all_image_urls_ddg", lambda *a, **k: []) + monkeypatch.setattr(image_search, "find_all_image_urls_google_cse", lambda *a, **k: []) + monkeypatch.setattr(image_search, "find_images_playwright", lambda *a, **k: []) + + image_search.find_all_image_urls("Amul Butter 500g", brand="Amul", validate=False) + + assert "openfacts" in calls + + +def test_wikimedia_stops_excluding_what_produce_actually_is(monkeypatch): + """The standing exclusion list contains -plant -garden -nature -tree -herb + -fish -animal -flower. Every one of those describes what a fruit, a + vegetable or a fish IS, so the query asks the index to rule out the answer. + """ + seen = {} + + class _Resp: + status_code = 200 + + @staticmethod + def json(): + return {"query": {"pages": {}}} + + def _fake_get(url, params=None, **kwargs): + seen["search"] = (params or {}).get("gsrsearch", "") + return _Resp() + + monkeypatch.setattr(image_search.requests, "get", _fake_get) + + image_search.find_images_wikimedia("Tomato", brand="", produce=True) + assert "-plant" not in seen["search"] + assert "-fish" not in seen["search"] + + image_search.find_images_wikimedia("Dettol Soap", brand="Dettol") + assert "-plant" in seen["search"], "branded searches keep their exclusions" + + +# --------------------------------------------------------------------------- +# The repair script's corroboration guard +# --------------------------------------------------------------------------- +def test_the_bucket_name_no_longer_corroborates_every_cdn_url(): + """`_brand_tokens("own_products")` used to yield ["own", "products"], and + "products" appears in the path of essentially every product CDN. The guard + it feeds was therefore satisfied by URLs naming nothing about the item.""" + from scripts.repair_brand_images import _brand_tokens, _names_product + + assert _brand_tokens("own_products") == [] + + off_url = "https://images.openbeautyfacts.org/images/products/360/054/214/0775/front_es.27.400.jpg" + assert _names_product(off_url, "Banana", "own_products") is False, ( + "this exact URL was the stored image for Banana" + ) + + +def test_a_url_that_names_the_item_still_corroborates(): + from scripts.repair_brand_images import _names_product + + good = "https://upload.wikimedia.org/wikipedia/commons/8/8a/Banana-Single.jpg" + assert _names_product(good, "Banana", "own_products") is True + + +def test_the_bucket_is_recognised_under_either_spelling(): + """`repair()` passes the table suffix; the pipeline and UI use the display + name. Both have to reach produce mode.""" + from scripts.repair_brand_images import _is_own_products + + assert _is_own_products("own_products") is True + assert _is_own_products("Own Products") is True + assert _is_own_products("Amul") is False diff --git a/tests/test_upload_data_integrity.py b/tests/test_upload_data_integrity.py index 6e991f9..26ba777 100644 --- a/tests/test_upload_data_integrity.py +++ b/tests/test_upload_data_integrity.py @@ -265,13 +265,62 @@ NUTRITION_FULL = ( ) +def _split_top_level(text): + """Split a comma-separated SQL list, ignoring commas inside parentheses.""" + parts, depth, current = [], 0, [] + for ch in text: + if ch == "(": + depth += 1 + elif ch == ")": + depth -= 1 + if ch == "," and depth == 0: + parts.append("".join(current).strip()) + current = [] + else: + current.append(ch) + if "".join(current).strip(): + parts.append("".join(current).strip()) + return parts + + +def _by_column(sql, params): + """Map an INSERT's column names to the values it will actually write. + + Indexing recorded parameters by POSITION - which is what these tests used to + do - couples every assertion to the column order of a statement none of them + are about. Adding one column to nutrition_facts broke six unrelated tests + that had nothing to say about that column. Column names are the stable + thing, so match on those. + + Walks the column list and the VALUES list together, consuming a parameter + only for a `%s` placeholder, so inline literals (`'excel_upload'`, + `CURRENT_TIMESTAMP`) stay aligned instead of shifting everything after them. + """ + head, _, rest = sql.partition("VALUES") + columns = _split_top_level(head[head.index("(") + 1:head.rindex(")")]) + values = _split_top_level(rest[rest.index("(") + 1:rest.index(")")]) + + mapped, i = {}, 0 + for column, value in zip(columns, values): + if value.strip() == "%s": + mapped[column] = params[i] + i += 1 + else: + mapped[column] = value.strip() + return mapped + + def _facts(db): - return _find(db, "INSERT INTO nutrition_facts")[0][1] + sql, params = _find(db, "INSERT INTO nutrition_facts")[0] + return _by_column(sql, params) def _insights(db): hits = _find(db, "INSERT INTO nutrition_insights") - return hits[0][1] if hits else None + if not hits: + return None + sql, params = hits[0] + return _by_column(sql, params) def test_an_absent_allergen_column_is_null_never_none(client, admin_headers, db): @@ -282,7 +331,7 @@ def test_an_absent_allergen_column_is_null_never_none(client, admin_headers, db) params = _insights(db) assert params is not None - allergens, allergen_source = params[8], params[9] + allergens, allergen_source = params["allergens"], params["allergen_source"] assert allergens is None, "allergens were invented" assert allergen_source == "unavailable" @@ -291,7 +340,7 @@ def test_an_absent_diet_tag_column_is_null(client, admin_headers, db): """It used to default to ['High Protein', 'Gluten Free'] for everything.""" body = "brand,image_id,product_name,protein_g\namul,x,Butter,5.0\n" client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) - assert _insights(db)[7] is None + assert _insights(db)["diet_tags"] is None def test_absent_nutrients_are_null_not_plausible_numbers(client, admin_headers, db): @@ -299,16 +348,17 @@ def test_absent_nutrients_are_null_not_plausible_numbers(client, admin_headers, client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) params = _facts(db) - # (brand, image_id, product_name, category, data_status, then 10 nutrients) - assert params[3] is None # category - assert all(v is None for v in params[5:]), "a nutrient was invented" + assert params["category"] is None + nutrients = [c for c in params if c.endswith(("_g", "_mg", "_kcal"))] + assert nutrients, "the column-name map found no nutrient columns to check" + assert all(params[c] is None for c in nutrients), "a nutrient was invented" def test_a_row_with_no_nutrients_is_unavailable_not_verified(client, admin_headers, db): body = "brand,image_id,product_name\namul,x,Butter\n" response = client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) - assert _facts(db)[4] == "unavailable" + assert _facts(db)["data_status"] == "unavailable" assert response.json()["data_status_counts"]["unavailable"] == 1 @@ -316,19 +366,19 @@ def test_a_row_with_some_nutrients_is_partial(client, admin_headers, db): body = "brand,image_id,product_name,protein_g,total_sugar_g\namul,x,Butter,5.0,2.0\n" response = client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) - assert _facts(db)[4] == "partial" + assert _facts(db)["data_status"] == "partial" assert response.json()["data_status_counts"]["partial"] == 1 def test_only_a_complete_core_set_is_verified(client, admin_headers, db): response = client.post("/api/upload/nutrition", files=_csv(NUTRITION_FULL), headers=admin_headers) - assert _facts(db)[4] == "verified" + assert _facts(db)["data_status"] == "verified" assert response.json()["data_status_counts"]["verified"] == 1 params = _insights(db) - assert params[8] == ["Milk"] - assert params[9] == "upload" + assert params["allergens"] == ["Milk"] + assert params["allergen_source"] == "upload" def test_insights_are_built_only_from_supplied_values(client, admin_headers, db): @@ -336,9 +386,13 @@ def test_insights_are_built_only_from_supplied_values(client, admin_headers, db) client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) params = _insights(db) - positives, cautions = params[5], params[6] - assert positives == ["Contains 5.0g protein per 100g"] - assert cautions is None, "a sugar caution was invented from no sugar value" + # Wording comes from `nutrition_scoring.generate_positive_insights`, the + # same function that phrases every other product's insights. This endpoint + # used to hand-roll its own strings, so an uploaded product read differently + # from an identical one enriched from a source. + assert params["positive_insights"] == ["Good source of protein (5.0 g per 100 g)."] + assert params["nutritional_cautions"] is None, ( + "a sugar caution was invented from no sugar value") def test_no_insight_row_at_all_when_nothing_was_supplied(client, admin_headers, db): diff --git a/tests/test_upload_nutrition_scoring.py b/tests/test_upload_nutrition_scoring.py new file mode 100644 index 0000000..27ed232 --- /dev/null +++ b/tests/test_upload_nutrition_scoring.py @@ -0,0 +1,295 @@ +"""POST /api/upload/nutrition computes the health score; it does not copy one. + +TWO DEFECTS THIS FILE PINS SHUT. + +1. NO EDIBILITY GATE. Every other write path into `nutrition_facts` refuses + non-food. This one had no such check - it imported only `store_db`, + `nutrition_db` and `_connect` - so a spreadsheet could put a health score on + a shampoo, and the public listing endpoints and analytics leaderboards then + served it. + +2. THE SCORE CAME OFF THE SHEET. `health_score = _opt_float(row, [...])` stored + whatever number a column happened to contain, into the same column that + holds scores computed by `nutrition_scoring.compute_scores` from USDA and + Open Food Facts data. Two scales in one column makes every ranking that + sorts on it meaningless: an uploaded 95 outranks a computed 80 without the + two having been measured the same way. + +No database: `_connect` is a recorder, so the assertions are about the exact +parameters the handler would send. +""" +from __future__ import annotations + +import io + +import pytest + +from app.api.routers import upload as upload_router +from app.services import nutrition_scoring + + +class FakeCursor: + def __init__(self, calls): + self.calls = calls + + def execute(self, sql, params=None): + self.calls.append((" ".join(str(sql).split()), params)) + + def __enter__(self): + return self + + def __exit__(self, *exc): + return False + + +class FakeConn: + def __init__(self): + self.calls = [] + + def cursor(self): + return FakeCursor(self.calls) + + def __enter__(self): + return self + + def __exit__(self, *exc): + return False + + def close(self): + pass + + +@pytest.fixture +def db(monkeypatch): + conn = FakeConn() + monkeypatch.setattr(upload_router, "_connect", lambda: conn) + monkeypatch.setattr(upload_router.nutrition_db, "ensure_nutrition_schema", lambda: None) + return conn + + +def _csv(text: str): + return {"file": ("sheet.csv", io.BytesIO(text.encode()), "text/csv")} + + +def _split_top_level(text): + parts, depth, current = [], 0, [] + for ch in text: + if ch == "(": + depth += 1 + elif ch == ")": + depth -= 1 + if ch == "," and depth == 0: + parts.append("".join(current).strip()) + current = [] + else: + current.append(ch) + if "".join(current).strip(): + parts.append("".join(current).strip()) + return parts + + +def _by_column(conn, table): + """An INSERT's column names mapped to the values it would write. + + Matched on column NAME, never on position: these tests are about two + specific columns, and should not break when a third is added beside them. + """ + hits = [(sql, params) for sql, params in conn.calls if "INSERT INTO " + table in sql] + if not hits: + return None + sql, params = hits[0] + head, _, rest = sql.partition("VALUES") + columns = _split_top_level(head[head.index("(") + 1:head.rindex(")")]) + values = _split_top_level(rest[rest.index("(") + 1:rest.index(")")]) + + mapped, i = {}, 0 + for column, value in zip(columns, values): + if value.strip() == "%s": + mapped[column] = params[i] + i += 1 + else: + mapped[column] = value.strip() + return mapped + + +# A complete core set, so `compute_scores` has enough to work with. The +# health_score column is deliberately a number no rule could ever produce from +# these nutrients, so a test asserting it was NOT used cannot pass by accident. +SHEET_SCORE = 95.0 +BISCUIT = ( + "brand,image_id,product_name,category,calories_kcal,protein_g,carbohydrates_g," + "total_sugar_g,dietary_fiber_g,total_fat_g,sodium_mg,health_score\n" + "britannia,b1,Britannia Good Day 200g,Biscuits & Cookies," + "480,6.5,66.0,28.0,2.1,21.0,350,95\n" +) +BISCUIT_VALUES = { + "calories_kcal": 480.0, "protein_g": 6.5, "carbohydrates_g": 66.0, + "total_sugar_g": 28.0, "dietary_fiber_g": 2.1, "total_fat_g": 21.0, + "sodium_mg": 350.0, "calcium_mg": None, "iron_mg": None, "vitamin_c_mg": None, +} + + +# --------------------------------------------------------------------------- +# The gate +# --------------------------------------------------------------------------- +def test_a_shampoo_row_writes_nothing_at_all(client, admin_headers, db): + """Not "writes a row with a null score" - no row. An insights row with a + null score still makes the product appear in the listing endpoints and the + leaderboards, which is the whole problem.""" + body = ("brand,image_id,product_name,category,protein_g,health_score\n" + "pantene,p1,Pantene Pro-V Shampoo 340ml,Hair Care,5.0,88\n") + response = client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) + + assert response.status_code == 422, "nothing importable means nothing imported" + assert _by_column(db, "nutrition_facts") is None + assert _by_column(db, "nutrition_insights") is None + + +@pytest.mark.parametrize("category,product", [ + ("Bath Soap", "Palmolive Naturals"), + ("Hair Care", "Nyle Anti-Hairfall Shampoo"), + ("Personal Care - Mosquito Repellent", "Godrej Hit Spray"), + ("Flowers", "Marigold Garland"), +]) +def test_the_products_that_carry_a_live_health_score_today_are_all_refused( + client, admin_headers, db, category, product): + body = ("brand,image_id,product_name,category,calories_kcal,health_score\n" + "x,x1,{},{},291,72\n".format(product, category)) + client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) + + assert _by_column(db, "nutrition_facts") is None + + +def test_a_refused_row_is_reported_rather_than_silently_dropped(client, admin_headers, db): + """The uploader has to be able to see that a row in their sheet was not + imported, and why.""" + body = ("brand,image_id,product_name,category,calories_kcal,protein_g,carbohydrates_g," + "total_sugar_g,dietary_fiber_g,total_fat_g,sodium_mg\n" + "britannia,b1,Britannia Good Day 200g,Biscuits & Cookies,480,6.5,66,28,2.1,21,350\n" + "pantene,p1,Pantene Shampoo,Hair Care,5,1,1,1,1,1,1\n") + body_json = client.post("/api/upload/nutrition", files=_csv(body), + headers=admin_headers).json() + + assert body_json["rows_imported"] == 1 + assert body_json["skipped_non_consumable"] == 1 + assert "not a food or drink" in body_json["errors"][0]["error"] + + +def test_the_refusal_count_is_separate_from_the_malformed_row_count(client, admin_headers, db): + """`rows_skipped` reads as "your sheet was wrong". A non-consumable row was + perfectly well-formed and deliberately not imported - a different fact.""" + body_json = client.post("/api/upload/nutrition", files=_csv(BISCUIT), + headers=admin_headers).json() + + assert body_json["skipped_non_consumable"] == 0 + assert body_json["rows_skipped"] == 0 + + +def test_the_verdict_is_stored_so_the_listing_endpoint_can_filter_on_it( + client, admin_headers, db): + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + + assert _by_column(db, "nutrition_facts")["edibility"] == "consumable" + + +# --------------------------------------------------------------------------- +# The score +# --------------------------------------------------------------------------- +def test_the_stored_score_is_computed_from_the_nutrients(client, admin_headers, db): + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + + expected = nutrition_scoring.compute_scores( + dict(BISCUIT_VALUES, data_status="verified", allergens=[])) + insights = _by_column(db, "nutrition_insights") + + assert insights["health_score"] == expected["health_score"] + assert insights["nutrition_score"] == expected["nutrition_score"] + + +def test_the_number_in_the_sheet_is_not_the_number_that_is_stored(client, admin_headers, db): + """The regression this endpoint shipped with. 95 is not a score any rule + could derive from a biscuit with 28 g of sugar.""" + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + + assert _by_column(db, "nutrition_insights")["health_score"] != SHEET_SCORE + + +def test_the_scoring_version_identifies_the_rules_that_produced_it(client, admin_headers, db): + """Without this, a computed score and a copied one are indistinguishable + once they are both sitting in the same column.""" + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + + assert _by_column(db, "nutrition_insights")["scoring_version"] == \ + nutrition_scoring.SCORING_VERSION + + +def test_the_two_scores_are_no_longer_the_same_number_written_twice(client, admin_headers, db): + """`nutrition_score` is nutrient density; `health_score` is that adjusted + for calorie density. This endpoint used to write the sheet's one number + into both columns, so the calorie penalty silently vanished.""" + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + insights = _by_column(db, "nutrition_insights") + + assert insights["health_score"] < insights["nutrition_score"], ( + "480 kcal per 100 g should carry a calorie-density penalty") + + +def test_an_operator_score_survives_when_the_rules_cannot_produce_one( + client, admin_headers, db): + """Too few nutrients to score fairly. Their number is kept - they may have + it from a source this sheet does not carry - but the provenance says so.""" + body = ("brand,image_id,product_name,category,health_score\n" + "britannia,b1,Britannia Marie Gold,Biscuits & Cookies,71\n") + client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) + insights = _by_column(db, "nutrition_insights") + + assert insights["health_score"] == 71.0 + assert insights["nutrition_score"] is None, "nothing was computed, so nothing is claimed" + assert insights["scoring_version"] == "excel_upload" + + +# --------------------------------------------------------------------------- +# The insights beside the score +# --------------------------------------------------------------------------- +def test_insights_come_from_the_same_rules_as_every_other_product( + client, admin_headers, db): + """This endpoint used to hand-roll its own two sentences, so an uploaded + product read differently from an identical one enriched from a source.""" + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + facts = dict(BISCUIT_VALUES, data_status="verified", allergens=[]) + insights = _by_column(db, "nutrition_insights") + + assert insights["nutritional_cautions"] == nutrition_scoring.generate_cautions(facts) + + +def test_a_sheet_supplied_diet_tag_is_kept_alongside_the_derived_ones( + client, admin_headers, db): + """"Vegan" is an ingredient fact; the rules work on numbers and cannot + derive it. Replacing the operator's tags with the derived set would throw + away the only place that information exists.""" + body = BISCUIT.replace("health_score\n", "health_score,diet_tags\n") + body = body.replace(",350,95\n", ",350,95,Vegan\n") + client.post("/api/upload/nutrition", files=_csv(body), headers=admin_headers) + + assert "Vegan" in _by_column(db, "nutrition_insights")["diet_tags"] + + +def test_an_absent_allergen_column_is_still_null_not_an_empty_list( + client, admin_headers, db): + """The rule this module exists for, re-checked after the rewrite: "we were + not told" must not become "contains no allergens".""" + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + insights = _by_column(db, "nutrition_insights") + + assert insights["allergens"] is None + assert insights["allergen_source"] == "unavailable" + + +def test_absent_nutrients_are_still_null_not_zero(client, admin_headers, db): + """`compute_scores` reads absent as "do not score this component". A zero + would score it, badly, against a value nobody supplied.""" + client.post("/api/upload/nutrition", files=_csv(BISCUIT), headers=admin_headers) + facts = _by_column(db, "nutrition_facts") + + assert facts["calcium_mg"] is None + assert facts["vitamin_c_mg"] is None