import argparse
import os
from datetime import datetime
from pathlib import Path
from typing import List
import numpy as np
import torch
from PIL import Image
import gradio as gr
import json
import imageio

# Mock imports for demo - replace with actual imports when models are available
try:
    from huggingface_hub import snapshot_download
    from diffusers import AutoencoderKL, DDIMScheduler
    from transformers import CLIPVisionModelWithProjection
    from omegaconf import OmegaConf
    import spaces
    HAS_MODELS = True
except ImportError as e:
    print(f"Warning: Some dependencies not available: {e}")
    HAS_MODELS = False

MOTION_TRIGGER_WORD = {
    'sports_basketball_gym': 'Basketball in Gym',
    'sports_nba_pass': 'NBA Pass',
    'sports_nba_dunk': 'NBA Dunk',
    'movie_BruceLee1': 'Bruce Lee Style',
    'shorts_kungfu_match1': 'Kung Fu Match',
    'shorts_kungfu_desert1': 'Desert Kung Fu',
    'parkour_climbing': 'Parkour Climbing',
    'dance_indoor_1': 'Indoor Dance',
}

css_style = "#fixed_size_img {height: 500px;}"

def download_models():
    """Download required models from Hugging Face - simplified for demo"""
    print("Model downloading simulation...")
    
    # Create directory structure
    os.makedirs('./pretrained_weights', exist_ok=True)
    os.makedirs('./assets/masks', exist_ok=True)
    os.makedirs('./assets/test_image', exist_ok=True)
    os.makedirs('./assets/video_template', exist_ok=True)
    
    if HAS_MODELS:
        # Add actual model downloading logic here
        pass
    else:
        print("Skipping model download - dependencies not available")

class MIMODemo():
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Using device: {self.device}")
        
        try:
            download_models()
            print("MIMO demo initialized")
        except Exception as e:
            print(f"Initialization warning: {e}")

    def generate_video(self, image, motion_template):
        """Generate video from image and motion template"""
        try:
            if image is None:
                return None, "⚠️ Please upload an image first."
            
            print(f"Processing with template: {motion_template}")
            
            # Create a simple demo video (replace with actual MIMO inference)
            frames = []
            for i in range(30):  # 30 frames for demo
                # Create a simple animation effect
                img_array = np.array(image)
                # Add some simple transformation for demo
                shift = int(10 * np.sin(i * 0.2))
                transformed = np.roll(img_array, shift, axis=1)
                frames.append(transformed)
            
            # Save video
            save_dir = 'output'
            os.makedirs(save_dir, exist_ok=True)
            case = datetime.now().strftime("%Y%m%d%H%M%S")
            outpath = f"{save_dir}/{case}.mp4"
            
            imageio.mimsave(outpath, frames, fps=15, quality=8)
            print(f'Demo video saved to: {outpath}')
            
            return outpath, f"✅ Generated demo animation for {MOTION_TRIGGER_WORD[motion_template]}!"
            
        except Exception as e:
            print(f"Error in video generation: {e}")
            return None, f"❌ Error: {str(e)}"

def create_interface():
    """Create Gradio interface compatible with v3.41.2"""
    
    # Initialize MIMO
    mimo = MIMODemo()
    
    # Custom CSS
    css = """
    #fixed_size_img {
        height: 500px !important;
        max-height: 500px !important;
    }
    .gradio-container {
        max-width: 1200px !important;
        margin: auto !important;
    }
    """
    
    with gr.Blocks(css=css, title="MIMO Demo") as demo:
        
        # Title
        gr.HTML("""
        <div style="text-align: center; margin-bottom: 20px;">
            <h1>🎭 MIMO Demo - Controllable Character Video Synthesis</h1>
            <p>Transform character images into animated videos with controllable motion and scenes</p>
            <p>
                <a href="https://menyifang.github.io/projects/MIMO/index.html" target="_blank">Project Page</a> | 
                <a href="https://arxiv.org/abs/2409.16160" target="_blank">Paper</a> |
                <a href="https://github.com/menyifang/MIMO" target="_blank">GitHub</a>
            </p>
        </div>
        """)
        
        # Instructions
        with gr.Accordion("🧭 Instructions", open=True):
            gr.Markdown("""
            ### How to use:
            1. **Upload a character image**: Use a full-body, front-facing image with clear visibility
            2. **Select motion template**: Choose from the available motion templates
            3. **Generate**: Click "Generate Animation" to create your character animation
            
            ### Tips:
            - Best results with clear, well-lit character images
            - Processing may take 1-2 minutes depending on video length
            - This is a demo version - full functionality requires GPU resources
            """)
        
        with gr.Row():
            with gr.Column():
                # Input image
                img_input = gr.Image(
                    label='Upload Character Image', 
                    type="pil",
                    elem_id="fixed_size_img"
                )
                
                # Motion template selector
                motion_dropdown = gr.Dropdown(
                    choices=list(MOTION_TRIGGER_WORD.keys()),
                    value=list(MOTION_TRIGGER_WORD.keys())[0],
                    label="Select Motion Template",
                )
                
                # Generate button
                submit_btn = gr.Button("🎬 Generate Animation", variant='primary')
                
                # Status display
                status_text = gr.Textbox(
                    label="Status", 
                    interactive=False, 
                    value="Ready to generate... (Demo mode)"
                )
            
            with gr.Column():
                # Output video
                output_video = gr.Video(
                    label="Generated Animation",
                    elem_id="fixed_size_img"
                )
        
        # Event handlers
        submit_btn.click(
            fn=mimo.generate_video,
            inputs=[img_input, motion_dropdown],
            outputs=[output_video, status_text],
        )
        
        # Example images (if available)
        example_dir = './assets/test_image'
        if os.path.exists(example_dir):
            example_files = [f for f in os.listdir(example_dir) if f.endswith(('.jpg', '.png', '.jpeg'))]
            if example_files:
                example_paths = [[os.path.join(example_dir, f)] for f in example_files[:5]]
                gr.Examples(
                    examples=example_paths,
                    inputs=[img_input],
                    label="Example Images"
                )
    
    return demo

if __name__ == "__main__":
    print("🚀 Starting MIMO Demo...")
    
    # Create and launch interface
    demo = create_interface()
    
    # Launch with settings optimized for HF Spaces
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        quiet=False
    )