DEPLOYMENT.md

# Intel Image Classifier - Deployment Guide

## Overview

This application is a full-stack web classifier for natural scene images using two CNN models:
- **PyTorch Model**: Custom CNN architecture
- **TensorFlow Model**: Custom CNN architecture

The application combines:
- **Backend**: Django REST API
- **Frontend**: React with Material-UI
- **Models**: Two Deep Learning models for image classification

## Quick Start for Hugging Face Spaces

### Prerequisites

- Git
- Docker & Docker Compose (for local development)
- Or access to Hugging Face Spaces

### Option 1: Deploy to Hugging Face Spaces (Recommended)

1. **Fork/Clone the Repository**
   ```bash
   git clone https://github.com/danielle2035/Intel_classification.git
   cd Intel_classification
   ```

2. **Add Your Trained Models**
   
   Place your trained model files in the `backend/api/models/` directory:
   ```
   backend/api/models/
   ├── pytorch_model.pth      (PyTorch model)
   └── model_best.keras       (TensorFlow model)
   ```

3. **Push to Hugging Face**
   ```bash
   # Add HF as remote
   git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/Intel_classification
   
   # Push to deploy
   git push hf main
   ```

4. **Access Your App**
   - Go to: `https://huggingface.co/spaces/YOUR_USERNAME/Intel_classification`
   - The app will build and deploy automatically!

### Option 2: Build and Run Locally

#### With Docker Compose (Separate Services)

```bash
docker-compose up --build
```

Services will be available at:
- Frontend: `http://localhost:3000`
- Backend API: `http://localhost:8000`
- API Docs: `http://localhost:8000/swagger`

#### With Docker (Unified Container - HF Mode)

```bash
docker build -t intel-classifier .
docker run -p 7860:7860 intel-classifier
```

Access at: `http://localhost:7860`

#### Without Docker (Development)

1. **Backend Setup**
   ```bash
   cd backend/api
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r ../requirements.txt
   python manage.py migrate
   python manage.py runserver 0.0.0.0:8000
   ```

2. **Frontend Setup (separate terminal)**
   ```bash
   cd frontend
   npm install
   npm start
   ```

3. **Access**
   - Frontend: `http://localhost:3000`
   - Backend API: `http://localhost:8000`

## API Endpoints

### Classification

**POST** `/api/classify/`

Classify an image using either PyTorch or TensorFlow model.

**Response:**
```json
{
  "class": "mountain",
  "confidence": 0.95,
  "model_used": "pytorch",
  "probabilities": {
    "buildings": 0.02,
    "forest": 0.01,
    "glacier": 0.01,
    "mountain": 0.95,
    "sea": 0.01,
    "street": 0.00
  }
}
```

## Models & Classes

### Supported Classes
- buildings / Bâtiments / Kër yi
- forest / Forêt / Géej bu wees
- glacier / Glacier / Dëkk bu sedd
- mountain / Montagne / Tund bi
- sea / Mer / Géej bi
- street / Rue / Yoon bi

## Deployment Checklist

- [ ] Add trained models to `backend/api/models/`
- [ ] Update `ALLOWED_HOSTS` in settings if needed
- [ ] Test locally with Docker
- [ ] Push to Hugging Face Spaces
- [ ] Test on HF Space URL

## Troubleshooting

**Port Already in Use**
```bash
lsof -i :7860  # Find process
kill -9 <PID>   # Kill it
```

**Models Not Loading**
- Ensure files are in `backend/api/models/`
- Check file names: `pytorch_model.pth`, `model_best.keras`

**CORS Errors**
- Verify backend and frontend are accessible
- Check Django CSRF_TRUSTED_ORIGINS includes your HF URL

## Performance Tips

1. Resize images before upload (< 10MB)
2. PyTorch is generally faster on CPU
3. Adjust confidence threshold in `api_views.py` if needed