import json
import os
import time
import uuid
import tempfile
import io
from PIL import Image, ImageDraw, ImageFont
import base64
import mimetypes
from google import genai
from google.genai import types
from typing import Dict, Any

def save_binary_file(file_name, data):
    with open(file_name, "wb") as f:
        f.write(data)

def generate(text, file_name, api_key, model="gemini-2.0-flash-exp"):
    # Initialize client using provided api_key (or fallback to env variable)
    client = genai.Client(api_key=(api_key.strip() if api_key and api_key.strip() != ""
                                     else os.environ.get("GEMINI_API_KEY")))
    
    files = [ client.files.upload(file=file_name) ]
    
    contents = [
        types.Content(
            role="user",
            parts=[
                types.Part.from_uri(
                    file_uri=files[0].uri,
                    mime_type=files[0].mime_type,
                ),
                types.Part.from_text(text=text),
            ],
        ),
    ]
    generate_content_config = types.GenerateContentConfig(
        temperature=1,
        top_p=0.95,
        top_k=40,
        max_output_tokens=8192,
        response_modalities=["image", "text"],
        response_mime_type="text/plain",
    )
    text_response = ""
    image_path = None
    # Create a temporary file to potentially store image data.
    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
        temp_path = tmp.name
        for chunk in client.models.generate_content_stream(
            model=model,
            contents=contents,
            config=generate_content_config,
        ):
            if not chunk.candidates or not chunk.candidates[0].content or not chunk.candidates[0].content.parts:
                continue
            candidate = chunk.candidates[0].content.parts[0]
            # Check for inline image data
            if candidate.inline_data:
                save_binary_file(temp_path, candidate.inline_data.data)
                print(f"File of mime type {candidate.inline_data.mime_type} saved to: {temp_path} and prompt input: {text}")
                image_path = temp_path
                # If an image is found, we assume that is the desired output.
                break
            else:
                # Accumulate text response if no inline_data is present.
                text_response += chunk.text + "\n"
    
    del files
    return image_path, text_response

class EndpointHandler:
    def __init__(self, path=""):
        """
        Initialize the handler
        Args:
            path (str): Path to the model directory
        """
        # Nothing to initialize for this custom handler
        pass
            
    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Process the request
        Args:
            data (Dict): The request payload containing:
                - inputs: Dict with 'image', 'prompt', and optionally 'gemini_api_key'
        Returns:
            Dict: Response with processed results
        """
        try:
            inputs = data.get("inputs", {})
            
            # Extract parameters
            image_data = inputs.get("image")
            prompt = inputs.get("prompt", "")
            gemini_api_key = inputs.get("gemini_api_key", "")
            
            if not image_data or not prompt:
                return {"error": "Missing required inputs: 'image' and 'prompt'"}
            
            # Process image - convert from base64 or file path to PIL Image
            if isinstance(image_data, str):
                # If base64 string
                if image_data.startswith('data:image'):
                    image_data = image_data.split(',')[1]
                image_bytes = base64.b64decode(image_data)
                composite_pil = Image.open(io.BytesIO(image_bytes))
            elif isinstance(image_data, dict) and 'path' in image_data:
                # If file path provided
                composite_pil = Image.open(image_data['path'])
            else:
                return {"error": "Invalid image format"}
            
            # Save the composite image to a temporary file
            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
                composite_path = tmp.name
                composite_pil.save(composite_path)
            
            file_name = composite_path  
            input_text = prompt 
            model = "gemini-2.0-flash-exp" 
            image_path, text_response = generate(text=input_text, file_name=file_name, api_key=gemini_api_key, model=model)
            
            if image_path:
                # Load and convert the image if needed
                result_img = Image.open(image_path)
                if result_img.mode == "RGBA":
                    result_img = result_img.convert("RGB")
                
                # Convert to base64 for response
                output_buffer = io.BytesIO()
                result_img.save(output_buffer, format='PNG')
                img_base64 = base64.b64encode(output_buffer.getvalue()).decode()
                
                # Clean up temp files
                os.unlink(composite_path)
                os.unlink(image_path)
                
                return {
                    "generated_outputs": [{
                        "image": {
                            "path": None,
                            "url": f"data:image/png;base64,{img_base64}",
                            "size": len(output_buffer.getvalue()),
                            "orig_name": "edited_image.png",
                            "mime_type": "image/png",
                            "is_stream": False,
                            "meta": {}
                        },
                        "caption": None
                    }],
                    "gemini_output": "",
                    "prompt_used": prompt
                }
            else:
                # Clean up temp file
                os.unlink(composite_path)
                
                # Return text response if no image generated
                return {
                    "generated_outputs": [],
                    "gemini_output": text_response,
                    "prompt_used": prompt
                }
                
        except Exception as e:
            return {"error": f"Processing failed: {str(e)}"}