import os
import torch
import gradio as gr
import numpy as np
from PIL import Image
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoTokenizer, AutoModel
from decord import VideoReader, cpu
import tempfile
import json
from typing import List, Tuple, Optional, Union
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Constants
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
MODEL_PATH = "OpenGVLab/InternVL2_5-4B"

class InternVLChatBot:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.generation_config = dict(max_new_tokens=1024, do_sample=True)
        self.load_model()
    
    def load_model(self):
        """Load the InternVL model and tokenizer"""
        try:
            logger.info("Loading InternVL2.5-4B model...")
            self.model = AutoModel.from_pretrained(
                MODEL_PATH,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
                trust_remote_code=True,
                use_flash_attn=False,
                device_map="auto" if self.device == "cuda" else None
            )
            self.tokenizer = AutoTokenizer.from_pretrained(
                MODEL_PATH, trust_remote_code=True
            )
            logger.info("Model loaded successfully!")
        except Exception as e:
            logger.error(f"Error loading model: {str(e)}")
            raise e
    
    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
        """Find the closest aspect ratio from target ratios"""
        best_ratio_diff = float('inf')
        best_ratio = (1, 1)
        area = width * height
        
        for ratio in target_ratios:
            target_aspect_ratio = ratio[0] / ratio[1]
            ratio_diff = abs(aspect_ratio - target_aspect_ratio)
            if ratio_diff < best_ratio_diff:
                best_ratio_diff = ratio_diff
                best_ratio = ratio
            elif ratio_diff == best_ratio_diff:
                if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                    best_ratio = ratio
        return best_ratio
    
    def dynamic_preprocess(self, image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
        """Dynamically preprocess image based on aspect ratio"""
        orig_width, orig_height = image.size
        aspect_ratio = orig_width / orig_height
        
        # Calculate target ratios
        target_ratios = set(
            (i, j) for n in range(min_num, max_num + 1) 
            for i in range(1, n + 1) 
            for j in range(1, n + 1) 
            if i * j <= max_num and i * j >= min_num
        )
        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
        
        # Find closest aspect ratio
        target_aspect_ratio = self.find_closest_aspect_ratio(
            aspect_ratio, target_ratios, orig_width, orig_height, image_size
        )
        
        # Calculate target dimensions
        target_width = image_size * target_aspect_ratio[0]
        target_height = image_size * target_aspect_ratio[1]
        blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
        
        # Resize and split image
        resized_img = image.resize((target_width, target_height))
        processed_images = []
        
        for i in range(blocks):
            box = (
                (i % (target_width // image_size)) * image_size,
                (i // (target_width // image_size)) * image_size,
                ((i % (target_width // image_size)) + 1) * image_size,
                ((i // (target_width // image_size)) + 1) * image_size
            )
            split_img = resized_img.crop(box)
            processed_images.append(split_img)
        
        if use_thumbnail and len(processed_images) != 1:
            thumbnail_img = image.resize((image_size, image_size))
            processed_images.append(thumbnail_img)
        
        return processed_images
    
    def build_transform(self, input_size):
        """Build image transformation pipeline"""
        transform = T.Compose([
            T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
        ])
        return transform
    
    def load_image(self, image_path, input_size=448, max_num=12):
        """Load and preprocess image"""
        if isinstance(image_path, str):
            image = Image.open(image_path).convert('RGB')
        else:
            image = image_path.convert('RGB')
        
        transform = self.build_transform(input_size=input_size)
        images = self.dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
        pixel_values = [transform(img) for img in images]
        pixel_values = torch.stack(pixel_values)
        return pixel_values
    
    def get_index(self, bound, fps, max_frame, first_idx=0, num_segments=32):
        """Get frame indices for video processing"""
        if bound:
            start, end = bound[0], bound[1]
        else:
            start, end = -100000, 100000
        
        start_idx = max(first_idx, round(start * fps))
        end_idx = min(round(end * fps), max_frame)
        seg_size = float(end_idx - start_idx) / num_segments
        
        frame_indices = np.array([
            int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
            for idx in range(num_segments)
        ])
        return frame_indices
    
    def load_video(self, video_path, bound=None, input_size=448, max_num=1, num_segments=32):
        """Load and preprocess video"""
        vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
        max_frame = len(vr) - 1
        fps = float(vr.get_avg_fps())
        
        pixel_values_list, num_patches_list = [], []
        transform = self.build_transform(input_size=input_size)
        frame_indices = self.get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
        
        for frame_index in frame_indices:
            img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
            img = self.dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
            pixel_values = [transform(tile) for tile in img]
            pixel_values = torch.stack(pixel_values)
            num_patches_list.append(pixel_values.shape[0])
            pixel_values_list.append(pixel_values)
        
        pixel_values = torch.cat(pixel_values_list)
        return pixel_values, num_patches_list
    
    def chat(self, message, history, image=None, video=None):
        """Main chat function"""
        try:
            pixel_values = None
            num_patches_list = None
            
            # Process image if provided
            if image is not None:
                pixel_values = self.load_image(image, max_num=12)
                if self.device == "cuda":
                    pixel_values = pixel_values.to(torch.bfloat16).cuda()
                message = f"<image>\n{message}"
            
            # Process video if provided
            elif video is not None:
                pixel_values, num_patches_list = self.load_video(video, num_segments=8, max_num=1)
                if self.device == "cuda":
                    pixel_values = pixel_values.to(torch.bfloat16).cuda()
                video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
                message = f"{video_prefix}{message}"
            
            # Convert history to the expected format
            chat_history = []
            if history:
                for item in history:
                    if isinstance(item, dict) and "role" in item:
                        if item["role"] == "user":
                            last_user = item["content"]
                        elif item["role"] == "assistant":
                            chat_history.append((last_user, item["content"]))

            
            # Generate response
            if num_patches_list is not None:
                response, new_history = self.model.chat(
                    self.tokenizer, 
                    pixel_values, 
                    message, 
                    self.generation_config,
                    num_patches_list=num_patches_list,
                    history=chat_history,
                    return_history=True
                )
            else:
                response, new_history = self.model.chat(
                    self.tokenizer, 
                    pixel_values, 
                    message, 
                    self.generation_config,
                    history=chat_history,
                    return_history=True
                )
            
            # Update history
            history.append({"role": "user", "content": message})
            history.append({"role": "assistant", "content": response})
            return "", history, None, None
            
            
        except Exception as e:
            logger.error(f"Error in chat: {str(e)}")
            error_msg = f"Sorry, I encountered an error: {str(e)}"
            history.append([message, error_msg])
            return "", history, None, None

# Initialize the chatbot
chatbot = InternVLChatBot()

# Create Gradio interface
def create_interface():
    """Create the Gradio interface"""
    
    # Custom CSS for better styling
    custom_css = """
    .gradio-container {
        font-family: 'Arial', sans-serif;
    }
    .chat-message {
        padding: 10px;
        margin: 5px 0;
        border-radius: 10px;
    }
    .user-message {
        background-color: #e3f2fd;
        margin-left: 20px;
    }
    .bot-message {
        background-color: #f5f5f5;
        margin-right: 20px;
    }
    """
    
    with gr.Blocks(css=custom_css, title="InternVL2.5-4B Chat") as interface:
        gr.Markdown("""
        # 🤖 InternVL2.5-4B Multimodal Chat
        
        Welcome to the InternVL2.5-4B chat interface! This AI assistant can:
        - 💬 Have conversations with text
        - 🖼️ Analyze and describe images
        - 🎥 Process and understand videos
        - 📝 Extract text from images (OCR)
        - 🎯 Answer questions about visual content
        
        **Instructions:**
        1. Type your message in the text box
        2. Optionally upload an image or video
        3. Click Send to get a response
        4. Use "Clear" to reset the conversation
        """)
        
        with gr.Row():
            with gr.Column(scale=3):
                chatbot_interface = gr.Chatbot(
                    label="Chat History",
                    height=500,
                    show_copy_button=True,
                    avatar_images=["👤", "🤖"],
                    type="messages"
                )
                
                with gr.Row():
                    msg = gr.Textbox(
                        label="Your Message",
                        placeholder="Type your message here... You can ask about images, videos, or just chat!",
                        lines=2,
                        scale=4
                    )
                    send_btn = gr.Button("Send 📤", scale=1, variant="primary")
                
                with gr.Row():
                    clear_btn = gr.Button("Clear 🗑️", scale=1)
                    
            with gr.Column(scale=1):
                gr.Markdown("### 📎 Upload Media")
                
                image_input = gr.Image(
                    label="Upload Image",
                    type="pil",
                    height=200
                )
                
                video_input = gr.Video(
                    label="Upload Video",
                    height=200
                )
                
                gr.Markdown("""
                **Supported formats:**
                - Images: JPG, PNG, WEBP, GIF
                - Videos: MP4, AVI, MOV, WEBM
                
                **Tips:**
                - For images: Ask about content, extract text, or describe what you see
                - For videos: Ask for descriptions, analysis, or specific details
                - You can upload one media file at a time
                """)
        
        # Example prompts
        gr.Markdown("### 💡 Example Prompts")
        with gr.Row():
            example_btn1 = gr.Button("👋 Hello, introduce yourself")
            example_btn2 = gr.Button("🖼️ Describe this image")
            example_btn3 = gr.Button("📝 Extract text from image")
            example_btn4 = gr.Button("🎥 Analyze this video")
        
        # Event handlers
        def submit_message(message, history, image, video):
            if not message.strip():
                return "", history, image, video
            return chatbot.chat(message, history, image, video)
        
        def clear_chat():
            return [], None, None
        
        def set_example_prompt(prompt):
            return prompt
        
        # Wire up the interface
        send_btn.click(
            fn=submit_message,
            inputs=[msg, chatbot_interface, image_input, video_input],
            outputs=[msg, chatbot_interface, image_input, video_input]
        )
        
        msg.submit(
            fn=submit_message,
            inputs=[msg, chatbot_interface, image_input, video_input],
            outputs=[msg, chatbot_interface, image_input, video_input]
        )
        
        clear_btn.click(
            fn=clear_chat,
            outputs=[chatbot_interface, image_input, video_input]
        )
        
        # Example button handlers
        example_btn1.click(
            fn=set_example_prompt,
            inputs=[gr.State("Hello, who are you?")],
            outputs=[msg]
        )
        
        example_btn2.click(
            fn=set_example_prompt,
            inputs=[gr.State("Please describe this image in detail.")],
            outputs=[msg]
        )
        
        example_btn3.click(
            fn=set_example_prompt,
            inputs=[gr.State("Extract the exact text provided in the image.")],
            outputs=[msg]
        )
        
        example_btn4.click(
            fn=set_example_prompt,
            inputs=[gr.State("Describe this video in detail.")],
            outputs=[msg]
        )
        
        # Footer
        gr.Markdown("""
        ---
        **About InternVL2.5-4B:** A powerful multimodal AI model developed by Shanghai AI Lab, Tsinghua University and partners.
        
        **API Usage:** This interface supports API calls. The chat endpoint accepts JSON with `message`, `image`, and `video` fields.
        """)
    
    return interface

# API endpoint for external integrations
def api_chat(message: str, image: Optional[str] = None, video: Optional[str] = None, history: Optional[List] = None):
    """
    API endpoint for chat functionality
    
    Args:
        message: Text message
        image: Base64 encoded image or image path
        video: Video file path
        history: Chat history as list of [user_msg, bot_msg] pairs
    
    Returns:
        Dictionary with response and updated history
    """
    try:
        if history is None:
            history = []
        
        # Process image if provided (handle base64 or file path)
        image_obj = None
        if image:
            try:
                if image.startswith('data:image'):
                    # Handle base64 image
                    import base64
                    from io import BytesIO
                    image_data = image.split(',')[1]
                    image_bytes = base64.b64decode(image_data)
                    image_obj = Image.open(BytesIO(image_bytes))
                else:
                    # Handle file path
                    image_obj = Image.open(image)
            except Exception as e:
                logger.error(f"Error processing image: {str(e)}")
        
        # Chat with the model
        _, updated_history, _, _ = chatbot.chat(message, history, image_obj, video)
        
        return {
            "response": updated_history[-1][1] if updated_history else "",
            "history": updated_history,
            "status": "success"
        }
    except Exception as e:
        logger.error(f"API Error: {str(e)}")
        return {
            "response": f"Error: {str(e)}",
            "history": history,
            "status": "error"
        }

if __name__ == "__main__":
    # Create and launch the interface
    interface = create_interface()
    
    # Launch with API access enabled
    interface.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True,
        show_api=True,
    )