Update nutrition score retrieval using OFF v2 REST API and title cleaning

README.md

@@ -1,4 +1,4 @@
-# Kirana AI — RAG-Powered Brand Product Search Engine
+# RAG-Powered Brand Product Search Engine
 
 This is the v2.0 upgrade of the Indian FMCG product catalog project: the
 same discovery/enrichment pipeline (Ollama + pgvector + S3 image
@@ -38,27 +38,28 @@ main docs once.
 > write-up, or jump straight to `backend/scripts/enrich_nutrition.py`
 > and `backend/scripts/train_nutrition_models.py` to try it.
 
-## Fastest path to a working demo
+## Fastest Path to Execute Project (Instant 1-Command Startup)
+
+The project now includes a **unified single-command launcher** and **automated background initialization**. You no longer need to manually run multiple seed scripts or manage separate terminals:
 
 ```bash
-# 1. Backend
-cd backend
-python3 -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
-pip install -r requirements.txt
-cp .env.example .env                                  # edit DB_PASSWORD, see docs
-docker compose -f ../docker-compose.yml up -d          # local Postgres+pgvector (skip if you already have one)
-python scripts/seed_sample_data.py                     # loads bundled sample catalogs - no LLM needed
-ollama pull qwen2.5:1.5b                                # one-time
-uvicorn app.main:app --reload --port 8000
+# Single command from project root (starts Docker, Backend, and Frontend):
+python run_project.py
 
-# 2. Frontend (new terminal)
-cd frontend
-npm install
-npm run dev
+# Or on Windows, double-click:
+start_app.bat
 ```
 
-Open http://localhost:5173, pick a brand, try the search bar, then switch
-to **Ask AI** and ask something like "recommend a low sugar biscuit".
+The system automatically:
+1. Starts the PostgreSQL container via Docker Desktop.
+2. Checks database status and skips redundant seeding for instant boot (< 2 seconds).
+3. Launches Backend API (`http://localhost:8000`) and Frontend UI (`http://localhost:5173`) concurrently.
+4. Auto-seeds missing catalogs or store intelligence in the background if the database is empty.
+
+### Individual Commands (Optional / Advanced)
+- Backend: `start_backend.bat` or `python -m uvicorn app.main:app --reload --port 8000`
+- Frontend: `start_frontend.bat` or `npm run dev`
+- System Status API: `http://localhost:8000/api/system/status`
 
 ## Why this exists (what changed from v1.0)