import io
import base64
import os
from PIL import Image
import config
from openai import OpenAI
import warnings
import time
from google.generativeai import GenerativeModel
from datetime import datetime
warnings.filterwarnings("ignore", message="IMAGE_SAFETY is not a valid FinishReason")

global_image_data_url = None
global_image_prompt = None
global_image_description = None

def log_execution(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        start_str = datetime.fromtimestamp(start_time).strftime('%Y-%m-%d %H:%M:%S')
        
        result = func(*args, **kwargs)
        
        end_time = time.time()
        end_str = datetime.fromtimestamp(end_time).strftime('%Y-%m-%d %H:%M:%S')
        duration = end_time - start_time

        
        return result
    return wrapper

@log_execution
def generate_image_fn_deprecated (selected_prompt, model="gpt-image-1", output_path="models\benchmark"):
    """
    Generate an image from the prompt via the OpenAI API using gpt-image-1.
    Convert the image to a data URL and optionally save it to a file.

    Args:
        selected_prompt (str): The prompt to generate the image from.
        model (str): Should be "gpt-image-1". Parameter kept for compatibility.
        output_path (str, optional): If provided, saves the image to this path. Defaults to None.

    Returns:
        PIL.Image.Image or None: The generated image as a PIL Image object, or None on error.
    """
    global global_image_data_url, global_image_prompt
    
    MAX_PROMPT_LENGTH = 32000
    if len(selected_prompt) > MAX_PROMPT_LENGTH:
        selected_prompt = smart_truncate_prompt(selected_prompt, MAX_PROMPT_LENGTH)
        print(f"Warning: Prompt was smartly truncated to {len(selected_prompt)} characters while preserving critical details")
    
    global_image_prompt = selected_prompt

    model = "gpt-image-1"

    try:
        client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", config.OPENAI_API_KEY))

        api_params = {
            "model": model,
            "prompt": selected_prompt,
            "size": "1024x1536" ,
            "quality": "high",
            "moderation":"low"
        }

        result = client.images.generate(**api_params)

        

        image_bytes = base64.b64decode(image_base64)

        image = Image.open(io.BytesIO(image_bytes))

        if output_path:
            try:
                os.makedirs(os.path.dirname(output_path), exist_ok=True)
                with open(output_path, "wb") as f:
                    f.write(image_bytes)
                print(f"Successfully saved image to {output_path}")
            except Exception as e:
                print(f"Error saving image to {output_path}: {str(e)}")

        buffered = io.BytesIO()
        image.save(buffered, format="PNG")
        img_bytes = buffered.getvalue()
        img_b64 = base64.b64encode(img_bytes).decode("utf-8")
        global_image_data_url = f"data:image/png;base64,{img_b64}"

        print(f"Successfully generated image with prompt: {selected_prompt[:50]}...")
        return image  
    except Exception as e:
        print(f"Error generating image: {str(e)}")
        return None 
@log_execution
def generate_image_fn(selected_prompt, model="gemini-2.5-flash-image-preview", output_path="models/benchmark"):
    """
    Generate an image from the prompt via the Google Gemini API using vertexai.
    Convert the image to a data URL and optionally save it to a file.

    Args:
        selected_prompt (str): The prompt to generate the image from.
        model (str): The Gemini model to use. Defaults to "gemini-2.5-flash-image-preview".
        output_path (str, optional): If provided, saves the image to this path. Defaults to "models/benchmark".

    Returns:
        PIL.Image.Image or None: The generated image as a PIL Image object, or None on error.
    """
    global global_image_data_url, global_image_prompt
    
    MAX_PROMPT_LENGTH = 32000
    if len(selected_prompt) > MAX_PROMPT_LENGTH:
        selected_prompt = smart_truncate_prompt(selected_prompt, MAX_PROMPT_LENGTH)
        print(f"Warning: Prompt was smartly truncated to {len(selected_prompt)} characters while preserving critical details")
    
    global_image_prompt = selected_prompt

    try:
        from google.generativeai import GenerativeModel
        from PIL import Image
        import io
        import base64
        import os
        
        # Initialize the Gemini model
        gemini_model = GenerativeModel(model)
        
        # Generate content with the prompt
        response = gemini_model.generate_content([selected_prompt])
        
        # Extract the generated image from the response
        image = None
        image_bytes = None
        has_text_response = False
        
        for part in response.candidates[0].content.parts:
            # Check for text responses (ignore these)
            if hasattr(part, 'text') and part.text:
                has_text_response = True
                print(f"Ignoring text response from API: {part.text[:100]}...")
                continue
            
            # Look for image data
            if hasattr(part, 'inline_data') and part.inline_data is not None:
                image_bytes = part.inline_data.data
                
                # Verify we have valid data
                if not image_bytes or len(image_bytes) == 0:
                    print("Warning: inline_data.data is empty, skipping...")
                    continue
                
                # Try to parse the image
                try:
                    img_io = io.BytesIO(image_bytes)
                    image = Image.open(img_io)
                    image.load()  # Force load to verify it's valid
                    print(f"Successfully loaded image: {len(image_bytes)} bytes")
                    break
                except Exception as img_error:
                    print(f"Invalid image data received, skipping: {img_error}")
                    continue
        
        # If we only got text and no image, return None
        if image is None:
            if has_text_response:
                print("API returned text instead of image - skipping this response")
            else:
                print("No image data found in response")
            return None
        
        # Save image to file if output_path is provided
        if output_path:
            try:
                os.makedirs(os.path.dirname(output_path), exist_ok=True)
                # Ensure output_path has an image extension
                if not output_path.lower().endswith(('.png', '.jpg', '.jpeg')):
                    output_path = f"{output_path}.png"
                
                image.save(output_path)
                print(f"Successfully saved image to {output_path}")
            except Exception as e:
                print(f"Error saving image to {output_path}: {str(e)}")
        
        # Create data URL for the image
        buffered = io.BytesIO()
        image.save(buffered, format="PNG")
        img_bytes = buffered.getvalue()
        img_b64 = base64.b64encode(img_bytes).decode("utf-8")
        global_image_data_url = f"data:image/png;base64,{img_b64}"
        
        print(f"Successfully generated image with prompt: {selected_prompt[:50]}...")
        return image
    
    except Exception as e:
        print(f"Error generating image: {str(e)}")
        import traceback
        traceback.print_exc()
        return None
    

@log_execution
def smart_truncate_prompt(prompt, max_length):
    """
    Smart truncation that preserves critical details and visual consistency information.
    Prioritizes character descriptions, layout specifications, and technical requirements.
    """
    if len(prompt) <= max_length:
        return prompt
    
    critical_sections = [
        "CRITICAL LAYOUT:",
        "🎭 CRITICAL CHARACTER CONSISTENCY PROTOCOL:",
        "CHARACTER 1",
        "CHARACTER 2", 
        "CHARACTER 3",
        "STORY CONTENT:",
        "🏗️ ENVIRONMENTAL CONSISTENCY PROTOCOL:",
        "🎨 COMIC BOOK STYLE MASTERY:",
        "🎨 AUTHENTIC MANGA STYLE:",
        "🎨 PHOTOREALISTIC EXCELLENCE:",
        "🎨 CINEMATIC VISUAL MASTERY:",
        "🎨 HIGH-QUALITY ILLUSTRATION:",
        "📐 PANEL COMPOSITION MASTERY:",
        "🔍 DETAIL PRESERVATION PROTOCOL:",
        "⚡ ADVANCED QUALITY REQUIREMENTS:"
    ]
    
    sections = prompt.split(" || ")
    
    preserved_sections = []
    preserved_length = 0
    
    for section in sections:
        section_trimmed = section.strip()
        if not section_trimmed:
            continue
            
        is_critical = any(critical_marker in section_trimmed for critical_marker in critical_sections[:8])  
        
        if is_critical or (preserved_length + len(section_trimmed) + 4 < max_length - 200):  
            preserved_sections.append(section_trimmed)
            preserved_length += len(section_trimmed) + 4  
        elif preserved_length < max_length * 0.7:  
            available_space = max_length - preserved_length - 200
            if available_space > 100:  
                truncated_section = section_trimmed[:available_space-20] + "..."
                preserved_sections.append(truncated_section)
                break
    
    preserved_prompt = " || ".join(preserved_sections)
    
    final_mandate = " || FINAL MANDATE: Create a masterpiece with perfect character consistency and narrative clarity"
    if len(preserved_prompt) + len(final_mandate) <= max_length:
        preserved_prompt += final_mandate
    
    return preserved_prompt