# Product Classification Service - Setup Guide

## Overview
This service uses Google Gemini Vision AI to classify product condition from uploaded images. It determines whether products are **resellable**, **refurbishable**, or **scrap**.

## Prerequisites
- Python 3.8+
- Google Gemini API key

## Installation

### 1. Install Dependencies
```bash
cd backend
pip install -r requirements.txt
```

### 2. Get Gemini API Key
1. Visit: https://makersuite.google.com/app/apikey
2. Sign in with your Google account
3. Click "Create API Key"
4. Copy the generated key

### 3. Configure Environment
```bash
# Copy the example env file
cp .env.example .env

# Edit .env and add your Gemini API key
# GEMINI_API_KEY=your_actual_api_key_here
```

### 4. Test the Service
```bash
python manage.py shell
```

In the Python shell:
```python
from store.gemini_classification_service import get_classification_service

# This will test if your API key works
service = get_classification_service()
print("Service initialized successfully!")
```

## Usage

### API Endpoint
**POST** `/api/inference/`

**Request:**
- Content-Type: `multipart/form-data`
- Body: `image` (file)

**Response:**
```json
{
  "status": "resellable"  // or "refurb" or "scrap"
}
```

### Classification Logic

#### RESELLABLE
- Pristine or near-pristine condition
- No visible damage
- Can be sold as-is

#### REFURB (Refurbishable)
- Minor to moderate damage
- Repairable economically
- Needs cleaning or minor repairs

#### SCRAP
- Severe damage
- Not economically viable to repair
- Safety hazards

## Frontend Integration

The Exchange.jsx page already integrates with this service:

```javascript
const formData = new FormData();
formData.append('image', imageFile);

const response = await api.post('/inference/', formData, {
  headers: { 'Content-Type': 'multipart/form-data' },
});

console.log(response.data.status); // "resellable", "refurb", or "scrap"
```

## Error Handling

The service includes robust error handling:
- If Gemini API fails, defaults to "refurb" (safe middle ground)
- Invalid responses are logged and fallback to "refurb"
- All errors are logged for monitoring

## Cost Optimization

- Uses `gemini-1.5-flash` for cost-effective analysis
- Low temperature (0.1) for consistent results
- Max 10 tokens output (only need one word)

To use higher accuracy model, edit `gemini_classification_service.py`:
```python
self.model = genai.GenerativeModel('gemini-1.5-pro')  # More accurate, higher cost
```

## Monitoring

Check logs for classification results:
```python
logger.info(f"Product classified as: {condition}")
```

## Troubleshooting

### "GEMINI_API_KEY not found"
- Ensure `.env` file exists in backend directory
- Check that `GEMINI_API_KEY` is set correctly
- Restart Django server after updating `.env`

### "google-generativeai not installed"
```bash
pip install google-generativeai
```

### Classification always returns "refurb"
- Check API key is valid
- Check internet connection
- Review logs for error messages
- Ensure image file is valid (JPEG, PNG)

## Security Notes

- Never commit `.env` file to git (already in .gitignore)
- Never share your API key publicly
- Rotate API keys regularly
- Use environment variables in production

## Production Deployment

1. Set `DEBUG=False` in settings
2. Use production-grade API key with rate limits
3. Add monitoring/alerting for classification failures
4. Consider adding image size limits (max 10MB recommended)
5. Add rate limiting to prevent API abuse