# 🚜 Smart Farm Photo Keyword Tagging AI - API Documentation

## 🌐 Web UI & API Overview

The Smart Farm AI system provides both a **web interface** and **REST API** for agricultural photo keyword generation.

### 🚀 Quick Start

```bash
# Start the web UI and API server
python3 start_ui.py

# Or manually start with uvicorn
uvicorn src.api.main:app --host 0.0.0.0 --port 8000
```

**Access Points:**
- **Web UI**: http://localhost:8000
- **API Docs**: http://localhost:8000/docs (Swagger)
- **Alternative Docs**: http://localhost:8000/redoc
- **System Status**: http://localhost:8000/status

## 📋 API Endpoints

### 1. System Status
**GET** `/status`

Get current system status and capabilities.

**Response:**
```json
{
  "status": "Operational",
  "model_loaded": true,
  "version": "1.0.0",
  "capabilities": [
    "Agricultural keyword generation",
    "Image title creation",
    "Quality validation",
    "Batch processing",
    "Agricultural distinctions (farmer vs rancher)",
    "Location extraction",
    "Performance metrics"
  ]
}
```

### 2. Single Image Analysis
**POST** `/analyze/single`

Analyze a single agricultural image for keywords and title.

**Request:**
- **Content-Type**: `multipart/form-data`
- **Body**: Image file (JPG, PNG, etc.)

**Response:**
```json
{
  "filename": "farm_photo.jpg",
  "keywords": ["farmer", "corn", "field", "agriculture", "tractor"],
  "title": "Agricultural scene: Farmer working in corn field",
  "quality_score": 73.3,
  "processing_time": 2.5,
  "caption": "a farmer working in a corn field with a tractor"
}
```

**cURL Example:**
```bash
curl -X POST "http://localhost:8000/analyze/single" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@farm_photo.jpg"
```

### 3. Batch Image Analysis
**POST** `/analyze/batch`

Analyze multiple agricultural images in a single request.

**Request:**
- **Content-Type**: `multipart/form-data`
- **Body**: Multiple image files

**Response:**
```json
{
  "total_images": 5,
  "successful": 5,
  "failed": 0,
  "results": [
    {
      "filename": "corn_field.jpg",
      "keywords": ["corn", "field", "agriculture", "farming"],
      "title": "Agricultural scene: Corn field at sunset",
      "quality_score": 80.0,
      "processing_time": 2.1,
      "caption": "a corn field at sunset"
    }
  ],
  "average_quality": 75.2,
  "total_processing_time": 12.5
}
```

**cURL Example:**
```bash
curl -X POST "http://localhost:8000/analyze/batch" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "files=@photo1.jpg" \
  -F "files=@photo2.jpg" \
  -F "files=@photo3.jpg"
```

### 4. Demo with Sample Images
**GET** `/demo`

Run demonstration using existing sample agricultural images.

**Response:**
```json
{
  "total_images": 7,
  "successful": 7,
  "failed": 0,
  "results": [
    {
      "filename": "agric-field8.png",
      "keywords": ["corn", "field", "agriculture", "farming", "rural"],
      "title": "Agricultural scene: A corn field with the sun setting",
      "quality_score": 73.3,
      "processing_time": 3.2,
      "caption": "a corn field with the sun setting in the background"
    }
  ],
  "average_quality": 65.2,
  "total_processing_time": 18.7
}
```

## 🎯 Quality Scoring

The system provides quality scores for generated keywords:

| Score Range | Quality Level | Description |
|-------------|---------------|-------------|
| 80-100 | **Excellent** | High agricultural relevance, specific terms |
| 60-79 | **Good** | Relevant agricultural content, some generic terms |
| 40-59 | **Fair** | Basic agricultural recognition, needs improvement |
| 0-39 | **Poor** | Limited agricultural context, mostly generic |

## 🔧 Agricultural Distinctions

The AI system automatically applies agricultural distinctions:

### Farmer vs Rancher Logic
- **Farmer**: Detected when crops, grains, or cultivation mentioned
- **Rancher**: Detected when cattle, livestock, or grazing mentioned
- **Dairy Farmer**: Detected when milk, dairy, or Holstein mentioned
- **Chicken Farmer**: Detected when poultry, chickens, or eggs mentioned

### Gender Identification
- Combines gender detection with agricultural roles
- Examples: "male farmer", "female rancher"

## 📊 Performance Metrics

**Current System Performance:**
- **Processing Speed**: ~3 seconds per image
- **Batch Capability**: 500+ images efficiently
- **Quality Score**: 65.2/100 average
- **Scalability**: 1000 images in ~50 minutes

## 🌐 Web UI Features

### Interactive Interface
- **Drag & Drop**: Upload multiple images easily
- **Real-time Processing**: See results as they're generated
- **Quality Visualization**: Color-coded quality scores
- **Demo Mode**: Test with sample agricultural images

### Visual Elements
- **Green Theme**: Agricultural color scheme
- **Responsive Design**: Works on desktop and mobile
- **Progress Indicators**: Loading states and progress bars
- **Error Handling**: Clear error messages and recovery

## 🔒 Error Handling

### Common Error Responses

**400 Bad Request**
```json
{
  "detail": "Invalid image format. Please upload JPG, PNG, or similar."
}
```

**500 Internal Server Error**
```json
{
  "detail": "AI system not initialized"
}
```

**404 Not Found**
```json
{
  "detail": "Sample images not found"
}
```

## 🧪 Testing the API

### Python Example
```python
import requests

# Test system status
response = requests.get("http://localhost:8000/status")
print(response.json())

# Analyze single image
with open("farm_photo.jpg", "rb") as f:
    files = {"file": f}
    response = requests.post("http://localhost:8000/analyze/single", files=files)
    print(response.json())

# Run demo
response = requests.get("http://localhost:8000/demo")
print(response.json())
```

### JavaScript Example
```javascript
// Analyze image with fetch API
const formData = new FormData();
formData.append('file', imageFile);

fetch('http://localhost:8000/analyze/single', {
    method: 'POST',
    body: formData
})
.then(response => response.json())
.then(data => console.log(data));
```

## 🚀 Production Deployment

### Docker Deployment
```dockerfile
FROM python:3.10-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]
```

### Environment Variables
```bash
# Optional configuration
export MODEL_PATH="/path/to/custom/model"  # Use custom trained model
export MAX_UPLOAD_SIZE="10MB"             # Limit upload size
export BATCH_SIZE_LIMIT="50"              # Limit batch processing
```

## 📈 Integration Examples

### Stock Photo Platform Integration
```python
# Example integration for stock photo workflow
import requests

def process_new_photos(photo_directory):
    files = []
    for photo in os.listdir(photo_directory):
        files.append(('files', open(os.path.join(photo_directory, photo), 'rb')))
    
    response = requests.post("http://localhost:8000/analyze/batch", files=files)
    results = response.json()
    
    # Update database with AI-generated keywords
    for result in results['results']:
        update_photo_keywords(result['filename'], result['keywords'])
```

### Quality Control Workflow
```python
# Filter high-quality results
def filter_high_quality_results(api_response):
    high_quality = []
    for result in api_response['results']:
        if result['quality_score'] >= 70:
            high_quality.append(result)
    return high_quality
```

## 🎯 Next Steps

1. **Start the UI**: `python3 start_ui.py`
2. **Test with Demo**: Click "Run Demo" button
3. **Upload Your Photos**: Drag and drop agricultural images
4. **Integrate API**: Use endpoints in your applications
5. **Scale Up**: Process your 30,000 photo dataset

---

**Ready to demonstrate the system to your team!** 🚜✨