docs/deployment.md

Deployment Guide

Local Development

Backend

cd aiBatteryLifecycle
.\venv\Scripts\activate          # Windows
source venv/bin/activate          # Linux/Mac

uvicorn api.main:app --host 0.0.0.0 --port 7860 --reload

Frontend (dev mode)

cd frontend
npm install
npm run dev

Frontend proxies /api/* to localhost:7860 in dev mode.

Frontend (production build)

cd frontend
npm run build

Built files go to frontend/dist/ and are served by FastAPI.

---

Docker

Build

docker build -t battery-predictor .

Run

docker run -p 7860:7860 battery-predictor

Build stages

1. frontend-build: node:20-slim — installs npm deps and builds React SPA
2. runtime: python:3.11-slim — installs Python deps, copies source and built frontend

Docker Compose (recommended)

# Production — single container (frontend + API)
docker compose up --build

# Development — backend only with hot-reload  
docker compose --profile dev up api-dev
# then separately:
cd frontend && npm run dev

Environment Variables

Variable	Default	Description
LOG_LEVEL	INFO	Logging verbosity (DEBUG / INFO / WARNING / ERROR)
WORKERS	1	Uvicorn worker count

---

Hugging Face Spaces

Setup

1. Create a new Space on Hugging Face (SDK: Docker)
2. Push the repository to the Space
3. The Dockerfile exposes port 7860 (HF Spaces default)

Dockerfile Requirements

• Must expose port 7860
• Must respond to health checks at /health
• Keep image size manageable (use CPU-only PyTorch/TF)

Files to include

Dockerfile
requirements.txt
api/
src/
frontend/               # Vite builds during Docker image creation
cleaned_dataset/
artifacts/v1/            # v1 model checkpoints (legacy)
artifacts/v2/            # v2 model checkpoints (recommended)
artifacts/models/        # Root-level models (backward compat)

HuggingFace Space URL

https://huggingface.co/spaces/NeerajCodz/aiBatteryLifeCycle

Space configuration (README.md header)

---
title: AI Battery Lifecycle Predictor
emoji: 🔋
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
app_port: 7860
---

---

Production Considerations

Performance

• Use --workers N for multi-core deployment
• Enable GPU passthrough for deep model inference: docker run --gpus all
• Consider preloading all models (not lazy loading)

Security

• Set CORS_ORIGINS to specific domains in production
• Add authentication middleware if needed
• Use HTTPS reverse proxy (nginx, Caddy)

Monitoring

• Health endpoint: /health
• Logs: JSON-per-line rotating log at artifacts/logs/battery_lifecycle.log (10 MB × 5 backups) — set LOG_LEVEL=DEBUG for verbose output, mount volume to persist across container restarts
• Metrics: Add Prometheus endpoint if needed