"""
QuickDraw Classifier API
A simple API wrapper for the CLIP-based drawing classifier
"""

import requests
import base64
import json
from typing import List, Dict, Optional
from PIL import Image
import io

class QuickDrawAPI:
    """
    API client for the QuickDraw Classifier
    """
    
    def __init__(self, base_url: str):
        """
        Initialize the API client
        
        Args:
            base_url: Base URL of the deployed Hugging Face Space
                     (e.g., "https://huggingface.co/spaces/username/quickdraw-classifier")
        """
        self.base_url = base_url.rstrip('/')
    
    def classify_image_file(self, image_path: str, top_k: int = 5) -> Dict:
        """
        Classify a drawing from an image file
        
        Args:
            image_path: Path to the image file
            top_k: Number of top predictions to return
            
        Returns:
            Dictionary with classification results
        """
        with open(image_path, "rb") as f:
            image_data = base64.b64encode(f.read()).decode()
        
        return self.classify_image_base64(image_data, top_k)
    
    def classify_pil_image(self, image: Image.Image, top_k: int = 5) -> Dict:
        """
        Classify a PIL Image
        
        Args:
            image: PIL Image object
            top_k: Number of top predictions to return
            
        Returns:
            Dictionary with classification results
        """
        # Convert PIL image to base64
        buffer = io.BytesIO()
        image.save(buffer, format='PNG')
        image_data = base64.b64encode(buffer.getvalue()).decode()
        
        return self.classify_image_base64(image_data, top_k)
    
    def classify_image_base64(self, image_data: str, top_k: int = 5) -> Dict:
        """
        Classify a base64 encoded image
        
        Args:
            image_data: Base64 encoded image string
            top_k: Number of top predictions to return
            
        Returns:
            Dictionary with classification results
        """
        try:
            response = requests.post(
                f"{self.base_url}/api/predict",
                json={
                    "data": [image_data, top_k],
                    "fn_index": 0
                },
                timeout=30
            )
            
            if response.status_code == 200:
                result = response.json()
                # Parse Gradio response format
                if "data" in result and len(result["data"]) > 0:
                    return {
                        "success": True,
                        "predictions": self._parse_gradio_output(result["data"][0])
                    }
            
            return {
                "success": False,
                "error": f"API request failed with status {response.status_code}"
            }
            
        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }
    
    def _parse_gradio_output(self, output: str) -> List[Dict]:
        """
        Parse the Gradio markdown output to extract predictions
        
        Args:
            output: Markdown formatted output from Gradio
            
        Returns:
            List of prediction dictionaries
        """
        predictions = []
        
        # Simple parsing of the markdown output
        lines = output.split('\n')
        for line in lines:
            if line.strip() and any(char.isdigit() for char in line):
                # Look for lines like "1. **Cat** - 85.2%"
                parts = line.split('-')
                if len(parts) >= 2:
                    # Extract category name
                    left_part = parts[0].strip()
                    category_start = left_part.find('**') + 2
                    category_end = left_part.rfind('**')
                    
                    if category_start > 1 and category_end > category_start:
                        category = left_part[category_start:category_end].strip().lower()
                        
                        # Extract confidence
                        right_part = parts[1].strip()
                        confidence_str = right_part.replace('%', '').strip()
                        try:
                            confidence = float(confidence_str) / 100.0
                            predictions.append({
                                "category": category,
                                "confidence": confidence
                            })
                        except ValueError:
                            continue
        
        return predictions

# Example usage functions
def classify_drawing_simple(image_path: str, space_url: str) -> List[str]:
    """
    Simple function to get top categories for a drawing
    
    Args:
        image_path: Path to the drawing image
        space_url: URL of the deployed Hugging Face Space
        
    Returns:
        List of top category names
    """
    api = QuickDrawAPI(space_url)
    result = api.classify_image_file(image_path)
    
    if result["success"]:
        return [pred["category"] for pred in result["predictions"]]
    else:
        print(f"Error: {result['error']}")
        return []

def batch_classify_drawings(image_paths: List[str], space_url: str) -> Dict[str, List[str]]:
    """
    Classify multiple drawings at once
    
    Args:
        image_paths: List of paths to drawing images
        space_url: URL of the deployed Hugging Face Space
        
    Returns:
        Dictionary mapping image paths to predicted categories
    """
    api = QuickDrawAPI(space_url)
    results = {}
    
    for image_path in image_paths:
        categories = classify_drawing_simple(image_path, space_url)
        results[image_path] = categories
    
    return results

# Example usage
if __name__ == "__main__":
    # Example usage of the API
    SPACE_URL = "https://huggingface.co/spaces/souvikg544/quickdraw-classifier"  # Replace with your space URL
    
    # Initialize API client
    api = QuickDrawAPI(SPACE_URL)
    
    # Example: Classify an image file
    result = api.classify_image_file("temp_drawing.png")
    print(json.dumps(result, indent=2))
    
    # print("QuickDraw API client ready!")
    # print(f"Connect to your space at: {SPACE_URL}")
    # print("\nExample usage:")
    # print("api = QuickDrawAPI('https://your-space-url')")
    # print("result = api.classify_image_file('drawing.png')")
    # print("print(result['predictions'])")