gradio_app.py

import os
import gradio as gr
import asyncio
import json
import uuid
import threading
import time
from datetime import datetime
import logging
import traceback
import re
from typing import Dict, List, Optional

from mllm_tools.litellm import LiteLLMWrapper
from src.config.config import Config
from generate_video import EnhancedVideoGenerator, VideoGenerationConfig, allowed_models

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("gradio_app.log"),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

# Create necessary directories
os.makedirs("thumbnails", exist_ok=True)

# Global dictionary to track job status
job_status = {}

# Model descriptions for better user understanding
MODEL_DESCRIPTIONS = {
    "gemini/gemini-1.5-pro-002": "🧠 Advanced reasoning, excellent for complex mathematical concepts",
    "gemini/gemini-2.5-flash-preview-04-17": "⚡ Fast processing, good for quick prototypes",
    "openai/gpt-4": "🎯 Reliable and consistent, great for educational content",
    "openai/gpt-4o": "🚀 Latest OpenAI model with enhanced capabilities",
    "anthropic/claude-3-5-sonnet-20241022": "📚 Excellent at detailed explanations and structured content",
    "openrouter/openai/gpt-4o": "🌐 GPT-4o via OpenRouter - Powerful and versatile",
    "openrouter/openai/gpt-4o-mini": "🌐 GPT-4o Mini via OpenRouter - Fast and cost-effective",
    "openrouter/anthropic/claude-3.5-sonnet": "🌐 Claude 3.5 Sonnet via OpenRouter - Excellent reasoning",
    "openrouter/anthropic/claude-3-haiku": "🌐 Claude 3 Haiku via OpenRouter - Quick responses",
    "openrouter/google/gemini-pro-1.5": "🌐 Gemini Pro 1.5 via OpenRouter - Google's advanced model",
    "openrouter/deepseek/deepseek-chat": "🌐 DeepSeek Chat via OpenRouter - Advanced conversation",
    "openrouter/qwen/qwen-2.5-72b-instruct": "🌐 Qwen 2.5 72B via OpenRouter - Alibaba's flagship model",
    "openrouter/meta-llama/llama-3.1-8b-instruct:free": "🌐 Llama 3.1 8B via OpenRouter - Free open source model",
    "openrouter/microsoft/phi-3-mini-128k-instruct:free": "🌐 Phi-3 Mini via OpenRouter - Free Microsoft model"
}

def cancel_job(job_id):
    """Cancel a running job."""
    if job_id and job_id in job_status:
        if job_status[job_id]['status'] in ['pending', 'initializing', 'planning', 'running']:
            job_status[job_id]['status'] = 'cancelled'
            job_status[job_id]['message'] = 'Job cancelled by user'
            return f"Job {job_id} has been cancelled"
    return "Job not found or cannot be cancelled"

def delete_job(job_id):
    """Delete a job from history."""
    if job_id and job_id in job_status:
        # Remove output files if they exist
        job = job_status[job_id]
        if job.get('output_file') and os.path.exists(job['output_file']):
            try:
                # Remove the entire output directory for this job
                output_dir = os.path.dirname(job['output_file'])
                import shutil
                shutil.rmtree(output_dir, ignore_errors=True)
            except Exception as e:
                logger.error(f"Error removing output files: {e}")
        
        # Remove thumbnail
        if job.get('thumbnail') and os.path.exists(job['thumbnail']):
            try:
                os.remove(job['thumbnail'])
            except Exception as e:
                logger.error(f"Error removing thumbnail: {e}")
        
        # Remove from job status
        del job_status[job_id]
        return f"Job {job_id} deleted successfully"
    return "Job not found"

def get_job_statistics():
    """Get statistics about jobs."""
    total_jobs = len(job_status)
    completed_jobs = sum(1 for job in job_status.values() if job.get('status') == 'completed')
    failed_jobs = sum(1 for job in job_status.values() if job.get('status') == 'failed')
    running_jobs = sum(1 for job in job_status.values() if job.get('status') in ['pending', 'initializing', 'planning', 'running'])
    
    return {
        'total': total_jobs,
        'completed': completed_jobs,
        'failed': failed_jobs,
        'running': running_jobs
    }

def init_video_generator(params):
    """Initialize the EnhancedVideoGenerator with the given parameters."""
    model_name = params.get('model', 'gemini/gemini-2.5-flash-preview-04-17')
    helper_model_name = params.get('helper_model', model_name)
    verbose = params.get('verbose', True)  # Set verbose to True by default for better debugging
    max_scene_concurrency = params.get('max_scene_concurrency', 1)
    
    # Create configuration for the enhanced video generator
    config = VideoGenerationConfig(
        planner_model=model_name,
        scene_model=model_name,
        helper_model=helper_model_name,
        output_dir=params.get('output_dir', Config.OUTPUT_DIR),
        verbose=verbose,
        use_rag=params.get('use_rag', False),
        use_context_learning=params.get('use_context_learning', False),
        context_learning_path=params.get('context_learning_path', Config.CONTEXT_LEARNING_PATH),
        chroma_db_path=params.get('chroma_db_path', Config.CHROMA_DB_PATH),
        manim_docs_path=params.get('manim_docs_path', Config.MANIM_DOCS_PATH),
        embedding_model=params.get('embedding_model', Config.EMBEDDING_MODEL),
        use_visual_fix_code=params.get('use_visual_fix_code', True),  # Enable visual fix code by default
        use_langfuse=params.get('use_langfuse', False),
        max_scene_concurrency=max_scene_concurrency,
        max_retries=params.get('max_retries', 3)
    )
    
    # Initialize EnhancedVideoGenerator
    video_generator = EnhancedVideoGenerator(config)
    
    return video_generator

async def process_video_generation(job_id, params):
    """Process video generation asynchronously."""
    try:
        # Update job status
        job_status[job_id]['status'] = 'initializing'
        job_status[job_id]['progress'] = 5
        job_status[job_id]['message'] = 'Initializing video generator...'
        
        # Initialize video generator
        video_generator = init_video_generator(params)
        
        # Extract video generation parameters
        topic = params.get('topic')
        description = params.get('description')
        max_retries = int(params.get('max_retries', 3))
        only_plan = params.get('only_plan', False)
        
        # Log job start
        logger.info(f"Starting job {job_id} for topic: {topic}")
        job_status[job_id]['status'] = 'planning'
        job_status[job_id]['progress'] = 10
        job_status[job_id]['message'] = 'Planning video scenes...'
        
        # Generate video pipeline
        start_time = datetime.now()
        logger.info(f"Running generate_video_pipeline for topic: {topic}")
        
        # Create an event loop for the async process
        def update_progress_callback(progress, message):
            job_status[job_id]['progress'] = progress
            job_status[job_id]['message'] = message
            logger.info(f"Job {job_id} progress: {progress}% - {message}")
        
        # Start a background task to periodically update progress
        async def progress_update_task():
            stages = [
                (15, 'Creating scene outline...'),
                (25, 'Generating implementation plans...'),
                (35, 'Generating code for scenes...'),
                (45, 'Compiling Manim code...'),
                (60, 'Rendering scenes...'),
                (80, 'Combining videos...'),
                (90, 'Finalizing video...')
            ]
            
            for progress, message in stages:
                update_progress_callback(progress, message)
                await asyncio.sleep(5)  # Wait between updates
                
                # Stop updating if job is complete or failed
                if job_status[job_id]['status'] in ['completed', 'failed']:
                    break
        
        # Start progress update task
        progress_task = asyncio.create_task(progress_update_task())
        
        # Run the main video generation task with detailed logging
        try:
            logger.info(f"Starting video generation pipeline for job {job_id}")
            update_progress_callback(15, 'Starting video generation pipeline...')
            
            await video_generator.generate_video_pipeline(
                topic=topic,
                description=description,
                only_plan=only_plan
            )
                
            logger.info(f"Video generation pipeline completed for job {job_id}")
        except Exception as e:
            logger.error(f"Error in video generation pipeline for job {job_id}: {str(e)}")
            logger.error(traceback.format_exc())
            raise
        
        # Cancel progress update task
        if not progress_task.done():
            progress_task.cancel()
        
        # Calculate processing time
        end_time = datetime.now()
        processing_time = (end_time - start_time).total_seconds()
        
        # Get output file path
        file_prefix = topic.lower()
        file_prefix = re.sub(r'[^a-z0-9_]+', '_', file_prefix)
        output_file = os.path.join(
            params.get('output_dir', Config.OUTPUT_DIR),
            file_prefix,
            f"{file_prefix}_combined.mp4"
        )
        
        # Check if output file actually exists
        if not os.path.exists(output_file):
            alternative_output = None
            # Look for any MP4 files that might have been generated
            scene_dir = os.path.join(params.get('output_dir', Config.OUTPUT_DIR), file_prefix)
            if os.path.exists(scene_dir):
                for root, dirs, files in os.walk(scene_dir):
                    for file in files:
                        if file.endswith('.mp4'):
                            alternative_output = os.path.join(root, file)
                            logger.info(f"Combined video not found, but found alternative: {alternative_output}")
                            break
                    if alternative_output:
                        break
            
            if alternative_output:
                output_file = alternative_output
            else:
                logger.error(f"No video output file found for job {job_id}")
                raise Exception("No video output was generated. Check Manim execution logs.")
        
        # Create a thumbnail from the video if it exists
        thumbnail_path = None
        if os.path.exists(output_file):
            thumbnail_path = os.path.join("thumbnails", f"{job_id}.jpg")
            try:
                import subprocess
                result = subprocess.run([
                    'ffmpeg', '-i', output_file, 
                    '-ss', '00:00:05', '-frames:v', '1', 
                    thumbnail_path
                ], capture_output=True, text=True)
                
                if result.returncode != 0:
                    logger.error(f"Error creating thumbnail: {result.stderr}")
                    thumbnail_path = None
            except Exception as e:
                logger.error(f"Error creating thumbnail: {str(e)}")
                thumbnail_path = None
        
        # Get scene snapshots
        scene_snapshots = []
        scene_dir = os.path.join(params.get('output_dir', Config.OUTPUT_DIR), file_prefix)
        if os.path.exists(scene_dir):
            for i in range(1, 10):  # Check up to 10 possible scenes
                scene_snapshot_dir = os.path.join(scene_dir, f"scene{i}")
                if os.path.exists(scene_snapshot_dir):
                    img_files = [f for f in os.listdir(scene_snapshot_dir) if f.endswith('.png')]
                    if img_files:
                        img_path = os.path.join(scene_snapshot_dir, img_files[-1])  # Get the last image
                        scene_snapshots.append(img_path)
        
        # Update job status to completed
        job_status[job_id].update({
            'status': 'completed',
            'progress': 100,
            'message': 'Video generation completed',
            'output_file': output_file if os.path.exists(output_file) else None,
            'processing_time': processing_time,
            'thumbnail': thumbnail_path,
            'scene_snapshots': scene_snapshots
        })
        
        logger.info(f"Job {job_id} completed successfully in {processing_time:.2f} seconds")
        
    except Exception as e:
        # Handle exceptions
        error_msg = str(e)
        stack_trace = traceback.format_exc()
        logger.error(f"Error in job {job_id}: {error_msg}\n{stack_trace}")
        
        job_status[job_id].update({
            'status': 'failed',
            'error': error_msg,
            'stack_trace': stack_trace,
            'message': f'Error: {error_msg[:100]}...' if len(error_msg) > 100 else f'Error: {error_msg}'
        })

def start_async_job(job_id, params):
    """Start an async job in a separate thread."""
    def run_async():
        asyncio.run(process_video_generation(job_id, params))
    
    thread = threading.Thread(target=run_async)
    thread.daemon = True
    thread.start()
    return thread

def submit_job(topic, description, model, helper_model, max_retries, use_rag, use_visual_fix_code, temperature, use_context_learning, verbose, max_scene_concurrency):
    """Submit a new video generation job."""
    # Input validation
    if not topic.strip():
        return "❌ Error: Topic is required", None, gr.update(visible=False)
    
    if not description.strip():
        return "❌ Error: Description is required", None, gr.update(visible=False)
    
    if len(topic.strip()) < 3:
        return "❌ Error: Topic must be at least 3 characters long", None, gr.update(visible=False)
    
    if len(description.strip()) < 10:
        return "❌ Error: Description must be at least 10 characters long", None, gr.update(visible=False)
    
    try:
        # Generate job ID
        job_id = str(uuid.uuid4())
        
        # Initialize job status
        job_status[job_id] = {
            'id': job_id,
            'status': 'pending',
            'topic': topic,
            'description': description,
            'model': model,
            'start_time': datetime.now().isoformat(),
            'progress': 0,
            'message': 'Job submitted, waiting to start...'
        }
        
        # Prepare parameters
        params = {
            'topic': topic,
            'description': description,
            'model': model,
            'helper_model': helper_model,
            'max_retries': max_retries,
            'use_rag': use_rag,
            'use_visual_fix_code': use_visual_fix_code,
            'temperature': temperature,
            'use_context_learning': use_context_learning,
            'verbose': verbose,
            'max_scene_concurrency': max_scene_concurrency,
            'output_dir': Config.OUTPUT_DIR,
        }
        
        # Start job asynchronously
        start_async_job(job_id, params)
        
        return f"✅ Job submitted successfully. Job ID: {job_id}", job_id, gr.update(visible=True)
    
    except Exception as e:
        logger.error(f"Error submitting job: {str(e)}")
        return f"❌ Error: {str(e)}", None, gr.update(visible=False)

def check_job_status(job_id):
    """Check the status of a job."""
    if not job_id or job_id not in job_status:
        return {"status": "not_found", "message": "Job not found"}
    
    return job_status[job_id]

def get_video_details(job_id):
    """Get details of a completed video job."""
    if not job_id or job_id not in job_status:
        return None, None, None, [], "Job not found"
    
    job = job_status[job_id]
    
    if job['status'] != 'completed':
        return None, None, None, [], f"Video not ready. Current status: {job['status']}"
    
    # Get video path, processing time, thumbnail and scene snapshots
    video_path = job.get('output_file')
    processing_time = job.get('processing_time', 0)
    thumbnail = job.get('thumbnail')
    scene_snapshots = job.get('scene_snapshots', [])
    
    if not video_path or not os.path.exists(video_path):
        return None, None, None, [], "Video file not found"
    
    return video_path, processing_time, thumbnail, scene_snapshots, None

def get_job_list():
    """Get a list of all jobs."""
    job_list = []
    for job_id, job in job_status.items():
        job_list.append({
            'id': job_id,
            'topic': job.get('topic', 'Unknown'),
            'status': job.get('status', 'unknown'),
            'start_time': job.get('start_time', ''),
            'progress': job.get('progress', 0),
            'message': job.get('message', '')
        })
    
    # Sort by start time, most recent first
    job_list.sort(key=lambda x: x.get('start_time', ''), reverse=True)
    return job_list

def format_status_message(job):
    """Format status message for display."""
    if not job:
        return "No job selected"
    
    status = job.get('status', 'unknown')
    progress = job.get('progress', 0)
    message = job.get('message', '')
    
    status_emoji = {
        'pending': '⏳',
        'initializing': '🔄',
        'planning': '🧠',
        'running': '⚙️',
        'completed': '✅',
        'failed': '❌',
        'unknown': '❓'
    }.get(status, '❓')
    
    return f"{status_emoji} Status: {status.title()} ({progress}%)\n{message}"

def update_status_display(job_id):
    """Update the status display for a job."""
    if not job_id:
        return ("No job selected", 
                gr.update(value=None), 
                gr.update(visible=False), 
                gr.update(visible=False), 
                gr.update(value=[]),
                gr.update(visible=False),
                gr.update(visible=False))
    
    job = check_job_status(job_id)
    status_message = format_status_message(job)
    
    # Check if the job is completed to show the video
    if job.get('status') == 'completed' and job.get('output_file') and os.path.exists(job.get('output_file')):
        video_path = job.get('output_file')
        video_vis = True
        thumbnail = job.get('thumbnail')
        scene_snapshots = job.get('scene_snapshots', [])
        processing_time = job.get('processing_time', 0)
        
        return (status_message, 
                gr.update(value=video_path), 
                gr.update(visible=video_vis), 
                gr.update(visible=thumbnail is not None, value=thumbnail), 
                gr.update(value=scene_snapshots),
                gr.update(visible=True, value=f"⏱️ Processing Time: {processing_time:.2f} seconds"),
                gr.update(visible=job.get('status') in ['pending', 'initializing', 'planning', 'running']))
    
    return (status_message, 
            gr.update(value=None), 
            gr.update(visible=False), 
            gr.update(visible=False), 
            gr.update(value=[]),
            gr.update(visible=False),
            gr.update(visible=job.get('status') in ['pending', 'initializing', 'planning', 'running']))

# Create Gradio interface
with gr.Blocks(
    title="Theory2Manim Video Generator", 
    theme=gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="slate",
        neutral_hue="slate",
        font=gr.themes.GoogleFont("Inter")
    ),
    css="""
    .main-header {
        text-align: center;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 2rem;
        border-radius: 1rem;
        margin-bottom: 2rem;
    }
    .status-card {
        border: 1px solid #e1e5e9;
        border-radius: 0.5rem;
        padding: 1rem;
        background: #f8f9fa;
    }
    .metric-card {
        border: 1px solid #e1e5e9;
        border-radius: 0.5rem;
        padding: 1rem;
        text-align: center;
        background: white;
    }
    .job-actions {
        gap: 0.5rem;
    }
    """
) as app:
    
    # Header
    with gr.Row():
        with gr.Column():
            gr.HTML("""
                <div class="main-header">
                    <h1>🎬 Theory2Manim Video Generator</h1>
                    <p>Transform mathematical and scientific concepts into engaging educational videos</p>
                </div>
            """)
    
    # Statistics Dashboard
    with gr.Row():
        stats_total = gr.Textbox(label="📊 Total Jobs", interactive=False, scale=1)
        stats_completed = gr.Textbox(label="✅ Completed", interactive=False, scale=1)
        stats_running = gr.Textbox(label="⚙️ Running", interactive=False, scale=1)
        stats_failed = gr.Textbox(label="❌ Failed", interactive=False, scale=1)
    
    with gr.Tab("🎥 Generate Video"):
        with gr.Row():
            with gr.Column(scale=2):
                with gr.Group():
                    gr.Markdown("### 📝 Content Configuration")
                    topic_input = gr.Textbox(
                        label="📚 Topic", 
                        placeholder="e.g., Fourier Transform, Calculus Derivatives, Quantum Mechanics",
                        info="Enter the main topic for your educational video"
                    )
                    description_input = gr.Textbox(
                        label="📋 Detailed Description", 
                        placeholder="Provide a comprehensive description of what you want the video to cover, including specific concepts, examples, and target audience level...",
                        lines=6,
                        info="The more detailed your description, the better the AI can generate relevant content"
                    )
            
            with gr.Column(scale=1):
                with gr.Group():
                    gr.Markdown("### ⚙️ AI Model Settings")
                    model_input = gr.Dropdown(
                        label="🤖 Primary AI Model", 
                        choices=list(MODEL_DESCRIPTIONS.keys()),
                        value="gemini/gemini-2.5-flash-preview-04-17",
                        info="Choose the AI model for content generation"
                    )
                    model_description = gr.Markdown(MODEL_DESCRIPTIONS["gemini/gemini-2.5-flash-preview-04-17"])
                    
                    helper_model_input = gr.Dropdown(
                        label="🔧 Helper Model", 
                        choices=list(MODEL_DESCRIPTIONS.keys()),
                        value="gemini/gemini-2.5-flash-preview-04-17",
                        info="Model for auxiliary tasks"
                    )
                    
                    temperature_input = gr.Slider(
                        label="🌡️ Creativity (Temperature)", 
                        minimum=0.0, 
                        maximum=1.0, 
                        value=0.7, 
                        step=0.1,
                        info="Lower = more focused, Higher = more creative"
                    )
        
        with gr.Row():
            with gr.Column():
                with gr.Group():
                    gr.Markdown("### 🔧 Advanced Settings")
                    with gr.Row():
                        max_retries_input = gr.Slider(
                            label="🔄 Max Retries", 
                            minimum=1, 
                            maximum=10, 
                            value=3, 
                            step=1,
                            info="Number of retry attempts for failed operations"
                        )
                        max_scene_concurrency_input = gr.Slider(
                            label="⚡ Scene Concurrency", 
                            minimum=1, 
                            maximum=5, 
                            value=1, 
                            step=1,
                            info="Number of scenes to process simultaneously"
                        )
                    
                    with gr.Row():
                        use_rag_input = gr.Checkbox(
                            label="📚 Use RAG (Retrieval Augmented Generation)", 
                            value=False,
                            info="Enhance generation with relevant knowledge retrieval"
                        )
                        use_visual_fix_code_input = gr.Checkbox(
                            label="🎨 Use Visual Code Fixing", 
                            value=True,
                            info="Automatically fix visual rendering issues"
                        )
                        use_context_learning_input = gr.Checkbox(
                            label="🧠 Use Context Learning", 
                            value=False,
                            info="Learn from previous successful videos"
                        )
                        verbose_input = gr.Checkbox(
                            label="📝 Verbose Logging", 
                            value=True,
                            info="Enable detailed logging for debugging"
                        )
        
        with gr.Row():
            with gr.Column(scale=3):
                submit_btn = gr.Button("🚀 Generate Video", variant="primary", size="lg")
            with gr.Column(scale=1):
                clear_form_btn = gr.Button("🧹 Clear Form", variant="secondary")
        
        result_text = gr.Textbox(label="📋 Status", interactive=False)
        job_id_output = gr.Textbox(label="Job ID", visible=False)
        
        with gr.Column(visible=False) as status_container:
            with gr.Group():
                gr.Markdown("### 📊 Job Progress")
                with gr.Row():
                    with gr.Column(scale=3):
                        status_text = gr.Textbox(label="Current Status", interactive=False, elem_classes=["status-card"])
                        processing_time_text = gr.Textbox(label="Processing Information", visible=False, interactive=False)
                    with gr.Column(scale=1):
                        with gr.Group():
                            refresh_btn = gr.Button("🔄 Refresh Status", variant="secondary")
                            cancel_btn = gr.Button("⏹️ Cancel Job", variant="stop", visible=False)
                
                with gr.Row():
                    with gr.Column(scale=2):
                        video_output = gr.Video(
                            label="🎬 Generated Video", 
                            interactive=False, 
                            visible=False,
                            show_download_button=True
                        )
                        thumbnail_preview = gr.Image(
                            label="🖼️ Video Thumbnail", 
                            visible=False,
                            height=200
                        )
                    
                    with gr.Column(scale=1):
                        scene_gallery = gr.Gallery(
                            label="🎨 Scene Previews", 
                            columns=2, 
                            object_fit="contain", 
                            height=400,
                            show_download_button=True
                        )
    
    with gr.Tab("📂 Job History & Management"):
        with gr.Row():
            with gr.Column(scale=3):
                refresh_jobs_btn = gr.Button("🔄 Refresh Job List", variant="secondary")
            with gr.Column(scale=1):
                clear_completed_btn = gr.Button("🧹 Clear Completed Jobs", variant="secondary")
                clear_all_btn = gr.Button("🗑️ Clear All Jobs", variant="stop")
        
        jobs_table = gr.Dataframe(
            headers=["ID", "Topic", "Status", "Progress (%)", "Start Time", "Message"],
            datatype=["str", "str", "str", "number", "str", "str"],
            interactive=False,
            label="📋 Job History",
            wrap=True
        )
        
        with gr.Row():
            with gr.Column():
                select_job_btn = gr.Button("👁️ View Selected Job", variant="primary")
                selected_job_id = gr.Textbox(label="Selected Job ID", visible=False)
            with gr.Column():
                delete_job_btn = gr.Button("🗑️ Delete Selected Job", variant="stop")
                download_job_btn = gr.Button("💾 Download Job Results", variant="secondary")
    
    with gr.Tab("ℹ️ Help & Documentation"):
        gr.Markdown("""
        ## 🎯 How to Use Theory2Manim
        
        ### 📝 Step 1: Content Planning
        - **Topic**: Enter a clear, specific topic (e.g., "Linear Algebra: Matrix Multiplication")
        - **Description**: Provide detailed context about what you want covered:
          - Target audience level (beginner, intermediate, advanced)
          - Specific concepts to include
          - Examples or applications to demonstrate
          - Preferred video length or depth
        
        ### 🤖 Step 2: Model Selection
        - **Gemini 1.5 Pro**: Best for complex mathematical reasoning
        - **Gemini 2.0 Flash**: Fastest processing, good for simple topics
        - **GPT-4**: Reliable and consistent output
        - **Claude**: Excellent for detailed explanations
        
        ### ⚙️ Step 3: Advanced Settings
        - **Temperature**: 0.3-0.5 for factual content, 0.7-0.9 for creative explanations
        - **RAG**: Enable for topics requiring external knowledge
        - **Visual Code Fixing**: Recommended for better video quality
        - **Context Learning**: Use previous successful videos as examples
        
        ### 📊 Step 4: Monitor Progress
        - Check the **Job History** tab to monitor all your video generation tasks
        - Use **Refresh Status** to get real-time updates
        - **Cancel** jobs if needed during processing
        
        ### 🎬 Step 5: Review Results
        - Preview generated videos directly in the interface
        - View scene breakdowns and thumbnails
        - Download videos for offline use
        
        ## 💡 Tips for Best Results
        1. **Be Specific**: Detailed descriptions lead to better videos
        2. **Start Simple**: Try basic topics first to understand the system
        3. **Use Examples**: Mention specific examples you want included
        4. **Set Context**: Specify the educational level and background needed
        5. **Review Settings**: Adjust temperature and models based on your content type
        
        ## 🔧 Troubleshooting
        - **Job Stuck**: Try canceling and resubmitting with different settings
        - **Poor Quality**: Use higher temperature or enable Visual Code Fixing
        - **Missing Content**: Provide more detailed descriptions
        - **Errors**: Check the verbose logs in the status messages
        """)
    
    # Event handlers with improved functionality
    def clear_form():
        return ("", "", 0.7, False, True, False, True, 1, 1, "Form cleared! Ready for new input.")
    
    def update_model_description(model):
        return MODEL_DESCRIPTIONS.get(model, "No description available")
    
    def update_stats():
        stats = get_job_statistics()
        return (f"{stats['total']}", 
                f"{stats['completed']}", 
                f"{stats['running']}", 
                f"{stats['failed']}")
    
    def clear_completed_jobs():
        completed_jobs = [job_id for job_id, job in job_status.items() 
                         if job.get('status') == 'completed']
        for job_id in completed_jobs:
            delete_job(job_id)
        return f"Cleared {len(completed_jobs)} completed jobs"
    
    def clear_all_jobs():
        count = len(job_status)
        job_status.clear()
        return f"Cleared all {count} jobs"
    
    # Connect event handlers
    model_input.change(
        fn=update_model_description,
        inputs=[model_input],
        outputs=[model_description]
    )
    
    clear_form_btn.click(
        fn=clear_form,
        outputs=[topic_input, description_input, temperature_input, 
                use_rag_input, use_visual_fix_code_input, use_context_learning_input, 
                verbose_input, max_retries_input, max_scene_concurrency_input, result_text]
    )
    
    submit_btn.click(
        fn=submit_job,
        inputs=[
            topic_input, description_input, model_input, helper_model_input, max_retries_input,
            use_rag_input, use_visual_fix_code_input, temperature_input, use_context_learning_input,
            verbose_input, max_scene_concurrency_input
        ],
        outputs=[result_text, job_id_output, status_container]
    ).then(
        fn=update_status_display,
        inputs=[job_id_output],
        outputs=[status_text, video_output, video_output, thumbnail_preview, scene_gallery, processing_time_text, cancel_btn]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    refresh_btn.click(
        fn=update_status_display,
        inputs=[job_id_output],
        outputs=[status_text, video_output, video_output, thumbnail_preview, scene_gallery, processing_time_text, cancel_btn]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    cancel_btn.click(
        fn=cancel_job,
        inputs=[job_id_output],
        outputs=[result_text]
    ).then(
        fn=update_status_display,
        inputs=[job_id_output],
        outputs=[status_text, video_output, video_output, thumbnail_preview, scene_gallery, processing_time_text, cancel_btn]
    )
    
    # Job history tab functions
    def load_job_list():
        jobs = get_job_list()
        rows = []
        for job in jobs:
            start_time = job.get('start_time', '')
            if start_time:
                try:
                    dt = datetime.fromisoformat(start_time.replace('Z', '+00:00'))
                    formatted_time = dt.strftime('%Y-%m-%d %H:%M:%S')
                except:
                    formatted_time = start_time
            else:
                formatted_time = 'Unknown'
            
            rows.append([
                job['id'][:8] + '...', 
                job['topic'][:50] + ('...' if len(job['topic']) > 50 else ''), 
                job['status'].title(), 
                job['progress'], 
                formatted_time,
                job['message'][:100] + ('...' if len(job['message']) > 100 else '')
            ])
        return rows
    
    def select_job(evt: gr.EventData):
        if not evt:
            return "", "No job selected"
        
        selected_row = evt.index[0] if hasattr(evt, 'index') and evt.index else 0
        jobs = get_job_list()
        if selected_row < len(jobs):
            return jobs[selected_row]['id'], f"Selected job: {jobs[selected_row]['topic']}"
        return "", "No job selected"
    
    def delete_selected_job(job_id):
        if job_id:
            result = delete_job(job_id)
            return result, ""
        return "No job selected", ""
    
    refresh_jobs_btn.click(
        fn=load_job_list,
        outputs=[jobs_table]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    jobs_table.select(
        fn=select_job,
        outputs=[selected_job_id, result_text]
    )
    
    select_job_btn.click(
        fn=lambda x: gr.update(visible=True) if x else gr.update(visible=False),
        inputs=[selected_job_id],
        outputs=[status_container]
    ).then(
        fn=update_status_display,
        inputs=[selected_job_id],
        outputs=[status_text, video_output, video_output, thumbnail_preview, scene_gallery, processing_time_text, cancel_btn]
    )
    
    delete_job_btn.click(
        fn=delete_selected_job,
        inputs=[selected_job_id],
        outputs=[result_text, selected_job_id]
    ).then(
        fn=load_job_list,
        outputs=[jobs_table]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    clear_completed_btn.click(
        fn=clear_completed_jobs,
        outputs=[result_text]
    ).then(
        fn=load_job_list,
        outputs=[jobs_table]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    clear_all_btn.click(
        fn=clear_all_jobs,
        outputs=[result_text]
    ).then(
        fn=load_job_list,
        outputs=[jobs_table]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    # Set up polling for status updates
    app.load(
        fn=load_job_list,
        outputs=[jobs_table]
    ).then(
        fn=update_stats,
        outputs=[stats_total, stats_completed, stats_running, stats_failed]
    )
    
    # Load on app start
    def on_app_start():
        if not os.path.exists("thumbnails"):
            os.makedirs("thumbnails", exist_ok=True)
        return "🎬 Welcome to Theory2Manim Video Generator! Ready to create amazing educational videos."
    
    app.load(
        fn=on_app_start,
        outputs=[result_text]
    )

if __name__ == "__main__":
    import os
    app.queue().launch(
        server_name=os.getenv("GRADIO_SERVER_NAME", "0.0.0.0"),
        server_port=int(os.getenv("GRADIO_SERVER_PORT", 7860)),
        share=False
    )