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README.md

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-# MatchLens backend
-
-This FastAPI service powers the existing React replay UI. The initial data mode is
-explicitly a **historical replay**, not a live feed. Cricsheet JSON files can be
-placed in `app/data/matches`; the normalized parser loads them at startup.
-
-## Load multiple real matches
-
-The backend does not scrape an HTML scoreboard. It uses Cricsheet's structured,
-historical ball-by-ball JSON source, which is more reliable for replay and
-statistics. From the `backend` directory, download a bounded set of recent
-men's T20 matches:
-
-```powershell
-python scripts\download_cricsheet.py --count 12
-```
-
-Restart Uvicorn after downloading. `GET /api/matches` will then expose the
-downloaded matches alongside the bundled fallback. The downloader is
-repeatable and only stores selected JSON files locally; do not commit the
-downloaded match data unless your distribution policy permits it.
-
-## Run locally
-
-```powershell
-cd backend
-python -m venv .venv
-.venv\Scripts\Activate.ps1
-pip install -r requirements.txt
-Copy-Item .env.example .env
-uvicorn app.main:app --reload
-```
-
-OpenAPI is available at `http://localhost:8000/docs`. Configure `GEMINI_API_KEY`
-for grounded natural-language explanations. Without it, the API returns a
-deterministic evidence-based fallback and explicitly warns that Gemini is not
-configured. Configure Qdrant before running the ingestion tooling; rule
-documents are never used to store changing match scores.
-
-## Frontend
-
-Use the real API by setting:
-
-```env
-VITE_DEMO_MODE=false
-VITE_API_BASE_URL=http://localhost:8000
-```
-
-The frontend then calls `/api/matches`, `/api/matches/{id}/state`,
-`/api/matches/{id}/timeline`, replay, catch-up, and assistant routes. Demo
-fixtures remain available when `VITE_DEMO_MODE` is not `false`.
-
-## Offline experiments and deployment
-
-`app/fine_tuning/train.py` contains the PEFT/LoRA training entry point and
-`evaluate.py` reports measured accuracy, macro-F1, and a confusion matrix.
-Training is intentionally offline and the API does not train at startup.
-`app/rag/ingestion.py` prepares idempotent document chunks for Qdrant.
-
-Build the Cloud Run image with `docker build -t matchlens-api backend` and pass
-secrets as environment variables. Netlify should use the same
-`VITE_API_BASE_URL` setting and an SPA fallback for `index.html`. Render can
-run the same Docker image with the configured `PORT`.