"""
API Client Examples for Binary Segmentation Service

These examples show how to interact with the FastAPI service
from Python, JavaScript, and curl.
"""

import requests
import base64
import json
from pathlib import Path


# =============================================================================
# Python Client Examples
# =============================================================================

class SegmentationClient:
    """Python client for segmentation API"""
    
    def __init__(self, base_url: str = "http://localhost:7860"):
        self.base_url = base_url.rstrip('/')
    
    def segment_image(
        self,
        image_path: str,
        output_path: str,
        model: str = "u2netp",
        threshold: float = 0.5
    ):
        """
        Segment image and save as PNG with transparency
        
        Args:
            image_path: Path to input image
            output_path: Path to save output PNG
            model: Model to use (u2netp, birefnet, rmbg)
            threshold: Segmentation threshold (0.0-1.0)
        """
        with open(image_path, 'rb') as f:
            files = {'file': f}
            data = {
                'model': model,
                'threshold': threshold
            }
            
            response = requests.post(
                f"{self.base_url}/segment",
                files=files,
                data=data
            )
            
            response.raise_for_status()
            
            with open(output_path, 'wb') as out:
                out.write(response.content)
            
            print(f"✓ Saved to: {output_path}")
    
    def get_mask(
        self,
        image_path: str,
        output_path: str,
        model: str = "u2netp",
        threshold: float = 0.5
    ):
        """Get binary mask only"""
        with open(image_path, 'rb') as f:
            files = {'file': f}
            data = {
                'model': model,
                'threshold': threshold
            }
            
            response = requests.post(
                f"{self.base_url}/segment/mask",
                files=files,
                data=data
            )
            
            response.raise_for_status()
            
            with open(output_path, 'wb') as out:
                out.write(response.content)
            
            print(f"✓ Mask saved to: {output_path}")
    
    def segment_base64(
        self,
        image_path: str,
        model: str = "u2netp",
        threshold: float = 0.5,
        return_type: str = "both"
    ):
        """
        Get segmentation results as base64
        
        Returns:
            dict with 'mask' and/or 'rgba' as base64 strings
        """
        with open(image_path, 'rb') as f:
            files = {'file': f}
            data = {
                'model': model,
                'threshold': threshold,
                'return_type': return_type
            }
            
            response = requests.post(
                f"{self.base_url}/segment/base64",
                files=files,
                data=data
            )
            
            response.raise_for_status()
            return response.json()
    
    def batch_segment(
        self,
        image_paths: list[str],
        model: str = "u2netp",
        threshold: float = 0.5
    ):
        """
        Segment multiple images
        
        Args:
            image_paths: List of paths to images (max 10)
            
        Returns:
            dict with results for each image
        """
        files = [
            ('files', open(path, 'rb'))
            for path in image_paths
        ]
        
        data = {
            'model': model,
            'threshold': threshold
        }
        
        try:
            response = requests.post(
                f"{self.base_url}/segment/batch",
                files=files,
                data=data
            )
            
            response.raise_for_status()
            return response.json()
        finally:
            # Close all file handles
            for _, f in files:
                f.close()
    
    def list_models(self):
        """List available models"""
        response = requests.get(f"{self.base_url}/models")
        response.raise_for_status()
        return response.json()
    
    def health_check(self):
        """Check service health"""
        response = requests.get(f"{self.base_url}/health")
        response.raise_for_status()
        return response.json()


# =============================================================================
# Usage Examples
# =============================================================================

def example_basic():
    """Basic usage"""
    client = SegmentationClient("http://localhost:7860")
    
    # Segment image
    client.segment_image(
        image_path="input.jpg",
        output_path="output.png",
        model="u2netp",
        threshold=0.5
    )


def example_mask():
    """Get binary mask"""
    client = SegmentationClient("http://localhost:7860")
    
    client.get_mask(
        image_path="input.jpg",
        output_path="mask.png",
        model="u2netp",
        threshold=0.5
    )


def example_base64():
    """Get base64 results"""
    client = SegmentationClient("http://localhost:7860")
    
    result = client.segment_base64(
        image_path="input.jpg",
        return_type="both"
    )
    
    # Save base64 images
    if 'rgba' in result:
        # Remove data URL prefix
        rgba_data = result['rgba'].split(',')[1]
        with open('output_rgba.png', 'wb') as f:
            f.write(base64.b64decode(rgba_data))
    
    if 'mask' in result:
        mask_data = result['mask'].split(',')[1]
        with open('output_mask.png', 'wb') as f:
            f.write(base64.b64decode(mask_data))


def example_batch():
    """Process multiple images"""
    client = SegmentationClient("http://localhost:7860")
    
    results = client.batch_segment(
        image_paths=["image1.jpg", "image2.jpg", "image3.jpg"],
        model="u2netp",
        threshold=0.5
    )
    
    # Save results
    for i, result in enumerate(results['results']):
        if result['success']:
            rgba_data = result['rgba'].split(',')[1]
            with open(f'output_{i}.png', 'wb') as f:
                f.write(base64.b64decode(rgba_data))


def example_models():
    """List available models"""
    client = SegmentationClient("http://localhost:7860")
    
    models = client.list_models()
    print(json.dumps(models, indent=2))


# =============================================================================
# JavaScript Examples (for frontend)
# =============================================================================

JAVASCRIPT_EXAMPLES = """
// Example 1: Basic fetch
async function segmentImage(file) {
    const formData = new FormData();
    formData.append('file', file);
    formData.append('model', 'u2netp');
    formData.append('threshold', '0.5');
    
    const response = await fetch('/segment', {
        method: 'POST',
        body: formData
    });
    
    const blob = await response.blob();
    return URL.createObjectURL(blob);
}

// Example 2: Get base64
async function segmentBase64(file) {
    const formData = new FormData();
    formData.append('file', file);
    formData.append('model', 'u2netp');
    formData.append('threshold', '0.5');
    formData.append('return_type', 'rgba');
    
    const response = await fetch('/segment/base64', {
        method: 'POST',
        body: formData
    });
    
    const data = await response.json();
    return data.rgba; // data:image/png;base64,...
}

// Example 3: Batch processing
async function segmentBatch(files) {
    const formData = new FormData();
    
    for (const file of files) {
        formData.append('files', file);
    }
    formData.append('model', 'u2netp');
    formData.append('threshold', '0.5');
    
    const response = await fetch('/segment/batch', {
        method: 'POST',
        body: formData
    });
    
    return await response.json();
}

// Example 4: With progress
async function segmentWithProgress(file, onProgress) {
    const formData = new FormData();
    formData.append('file', file);
    formData.append('model', 'u2netp');
    formData.append('threshold', '0.5');
    
    const xhr = new XMLHttpRequest();
    
    return new Promise((resolve, reject) => {
        xhr.upload.addEventListener('progress', (e) => {
            if (e.lengthComputable) {
                onProgress(e.loaded / e.total);
            }
        });
        
        xhr.addEventListener('load', () => {
            if (xhr.status === 200) {
                const blob = xhr.response;
                resolve(URL.createObjectURL(blob));
            } else {
                reject(new Error('Upload failed'));
            }
        });
        
        xhr.addEventListener('error', () => reject(new Error('Upload failed')));
        
        xhr.open('POST', '/segment');
        xhr.responseType = 'blob';
        xhr.send(formData);
    });
}
"""


# =============================================================================
# cURL Examples
# =============================================================================

CURL_EXAMPLES = """
# Example 1: Basic segmentation
curl -X POST "http://localhost:7860/segment" \\
  -F "file=@input.jpg" \\
  -F "model=u2netp" \\
  -F "threshold=0.5" \\
  --output result.png

# Example 2: Get mask
curl -X POST "http://localhost:7860/segment/mask" \\
  -F "file=@input.jpg" \\
  -F "model=u2netp" \\
  -F "threshold=0.5" \\
  --output mask.png

# Example 3: Get base64 JSON
curl -X POST "http://localhost:7860/segment/base64" \\
  -F "file=@input.jpg" \\
  -F "model=u2netp" \\
  -F "threshold=0.5" \\
  -F "return_type=both"

# Example 4: Batch processing
curl -X POST "http://localhost:7860/segment/batch" \\
  -F "files=@image1.jpg" \\
  -F "files=@image2.jpg" \\
  -F "files=@image3.jpg" \\
  -F "model=u2netp" \\
  -F "threshold=0.5"

# Example 5: List models
curl -X GET "http://localhost:7860/models"

# Example 6: Health check
curl -X GET "http://localhost:7860/health"
"""


if __name__ == "__main__":
    print("API Client Examples")
    print("=" * 50)
    print("\nPython Examples:")
    print("  example_basic()     - Basic segmentation")
    print("  example_mask()      - Get binary mask")
    print("  example_base64()    - Get base64 results")
    print("  example_batch()     - Batch processing")
    print("  example_models()    - List models")
    print("\nUncomment the example you want to run!")
    
    # Uncomment to run:
    # example_basic()
    # example_mask()
    # example_base64()
    # example_batch()
    # example_models()