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ProtoRyde Backend

Feature-complete backend repository for the hackathon.

Structure

  • app/ -> active FastAPI backend
    • api/ endpoints
    • core/ database models + scheduler
    • triggers/ weather + fraud trigger logic
    • services/ pricing, ML+SHAP prediction, PDF generation, model training utility
    • tests/ contract tests
  • docs/API_CONTRACT.md -> frontend/backend integration contract
  • docs/BACKEND_RUNBOOK.md -> backend owner runbook + curl payloads + error handling
  • main.py -> root ASGI entrypoint (app.main:app)
  • requirements.txt -> single dependency source

Run

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

ML + PDF now integrated in active app

  • ML status: GET /api/premium/model-status
  • Premium prediction (ML-first with fallback): POST /api/premium/predict
  • Policy PDF download: GET /api/policies/{rider_id}/current/document
  • Optional local model training: python app/services/train_model.py
  • Forecast: GET /api/forecast/{zone} (Prophet-first, fallback when Prophet unavailable)
  • Model monitoring summary: GET /api/admin/model-monitoring
  • Resolve actual outcome for a prediction: POST /api/admin/model-monitoring/resolve
  • Retrain all models immediately: POST /api/admin/model-retrain

Demo-Critical Endpoints

  • POST /api/demo/bootstrap: demo setup
  • POST /api/triggers/simulate: claim simulation
  • GET /api/claims/{rider_id}: rider claims history
  • GET /api/policies/{rider_id}/current: policy status
  • POST /api/policy/{id}/upgrade: Enhanced Coverage upsell (Phase 3)
  • GET /api/rider/{id}/calendar: Earnings timeline (Phase 3)
  • GET /api/admin/metrics: Platform health KPIs (Phase 3)
  • GET /api/admin/claims_map: Geolocation visualization (Phase 3)
  • GET /api/admin/fraud_flags: Suspicious claim review (Phase 3)
  • GET /api/admin/predictions: Global risk forecasting (Phase 3)

Reliability Tests

python3 -m unittest app.tests.test_phase2_contracts -v
python3 -m unittest app.tests.test_api_endpoints -v

Deploy on Render

This repo includes render.yaml and Procfile for Render deployment.

  1. Create a Blueprint service in Render and point it to this repository.
  2. Set service root to protoryde-backend if deploying from the umbrella repo.
  3. Render provisions protoryde-db from render.yaml and injects DATABASE_URL.
  4. Keep ENABLE_SCHEDULER=false in production unless you intentionally want live polling.

Beginner flow (recommended)

  1. Run preflight locally: 
    ./scripts/render_preflight.sh
  2. Deploy from Render Blueprint.
  3. Run live smoke checks against Render URL: 
    BACKEND_URL=https://<your-service>.onrender.com ./scripts/render_live_smoke.sh

See full guide: docs/RENDER_FIRST_DEPLOY.md

Notes

  • The app normalizes postgres:// to postgresql:// automatically for SQLAlchemy compatibility.
  • model.pkl is optional; premium prediction falls back to the rule engine when the model is absent.
  • Scheduled retraining is optional and controlled by ENABLE_ML_RETRAIN and ML_RETRAIN_INTERVAL_HOURS.