import streamlit as st
import tempfile
import os
import logging
from pathlib import Path
from PIL import Image
import io
import numpy as np
import sys
import subprocess
import json
from pygments import highlight
from pygments.lexers import PythonLexer, CppLexer
from pygments.formatters import HtmlFormatter
import base64
from transformers import pipeline
import re
import shutil
import time
from datetime import datetime, timedelta
import streamlit.components.v1 as components
import uuid
import platform
import pandas as pd
import plotly.express as px
import markdown
import zipfile
import contextlib
import threading
import traceback
from io import StringIO, BytesIO

# Set up enhanced logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

# Model configuration mapping for different API requirements and limits
MODEL_CONFIGS = {
    "DeepSeek-V3-0324": {"max_tokens": 4000, "param_name": "max_tokens", "api_version": None, "category": "DeepSeek", "warning": None},
    "DeepSeek-R1": {"max_tokens": 4000, "param_name": "max_tokens", "api_version": None, "category": "DeepSeek", "warning": None},
    "gpt-4o": {"max_tokens": 16000, "param_name": "max_tokens", "api_version": None, "category": "OpenAI", "warning": None},
    "gpt-4.1": {"max_tokens": 32768, "param_name": "max_tokens", "api_version": None, "category": "OpenAI", "warning": None},
    "gpt-4.1-mini": {"max_tokens": 32768, "param_name": "max_tokens", "api_version": None, "category": "OpenAI", "warning": None},
    "gpt-4.1-nano": {"max_tokens": 32768, "param_name": "max_tokens", "api_version": None, "category": "OpenAI", "warning": None},
    "o3": {"max_tokens": 100000, "param_name": "max_tokens", "api_version": None, "category": "OpenAI", "warning": None},
    "o4-mini": {"max_tokens": 100000, "param_name": "max_tokens", "api_version": None, "category": "OpenAI", "warning": None},
    # Default configuration for other models
    "default": {"max_tokens": 4000, "param_name": "max_tokens", "api_version": None, "category": "Other", "warning": None}
}

# Try to import Streamlit Ace
try:
    from streamlit_ace import st_ace
    ACE_EDITOR_AVAILABLE = True
except ImportError:
    ACE_EDITOR_AVAILABLE = False
    logger.warning("streamlit-ace not available, falling back to standard text editor")

def prepare_api_params(messages, model_name):
    """Create appropriate API parameters based on model configuration"""
    # Get model configuration
    config = MODEL_CONFIGS.get(model_name, MODEL_CONFIGS["default"])
    
    # Base parameters common to all models
    api_params = {
        "messages": messages,
        "model": model_name
    }
    
    # Add the appropriate token parameter based on model's parameter name
    token_param = config["param_name"]
    token_value = config[token_param]  # Get the actual value from the config
    
    # Add the parameter to the API params
    api_params[token_param] = token_value
    
    return api_params, config

# New functions for accessing secrets and password verification
def get_secret(github_token_api):
    """Retrieve a secret from HuggingFace Spaces environment variables"""
    secret_value = os.environ.get(github_token_api)
    if not secret_value:
        logger.warning(f"Secret '{github_token_api}' not found")
        return None
    return secret_value

def check_password():
    """Returns True if the user entered the correct password"""
    # Get the password from secrets
    correct_password = get_secret("password")
    if not correct_password:
        st.error("Admin password not configured in HuggingFace Spaces secrets")
        return False
        
    # Password input
    if "password_entered" not in st.session_state:
        st.session_state.password_entered = False
        
    if not st.session_state.password_entered:
        password = st.text_input("Enter password to access AI features", type="password")
        if password:
            if password == correct_password:
                st.session_state.password_entered = True
                return True
            else:
                st.error("Incorrect password")
                return False
        return False
    return True

def ensure_packages():
    required_packages = {
        'manim': '0.17.3',
        'Pillow': '9.0.0',
        'numpy': '1.22.0',
        'transformers': '4.30.0',
        'torch': '2.0.0',
        'pygments': '2.15.1',
        'streamlit-ace': '0.1.1',
        'pydub': '0.25.1',  # For audio processing
        'plotly': '5.14.0',  # For timeline editor
        'pandas': '2.0.0',   # For data manipulation
        'python-pptx': '0.6.21',  # For PowerPoint export
        'markdown': '3.4.3',  # For markdown processing
        'fpdf': '1.7.2',     # For PDF generation
        'matplotlib': '3.5.0',  # For Python script runner
        'seaborn': '0.11.2',  # For enhanced visualizations
        'scipy': '1.7.3',    # For scientific computations
        'huggingface_hub': '0.16.0',  # For Hugging Face API
    }
    
    with st.spinner("Checking required packages..."):
        # First, quickly check if packages are already installed
        missing_packages = {}
        for package, version in required_packages.items():
            try:
                # Try to import the package to check if it's available
                if package == 'manim':
                    import manim
                elif package == 'Pillow':
                    import PIL
                elif package == 'numpy':
                    import numpy
                elif package == 'transformers':
                    import transformers
                elif package == 'torch':
                    import torch
                elif package == 'pygments':
                    import pygments
                elif package == 'streamlit-ace':
                    # This one is trickier, we already handle it with ACE_EDITOR_AVAILABLE flag
                    pass
                elif package == 'pydub':
                    import pydub
                elif package == 'plotly':
                    import plotly
                elif package == 'pandas':
                    import pandas
                elif package == 'python-pptx':
                    import pptx
                elif package == 'markdown':
                    import markdown
                elif package == 'fpdf':
                    import fpdf
                elif package == 'matplotlib':
                    import matplotlib
                elif package == 'seaborn':
                    import seaborn
                elif package == 'scipy':
                    import scipy
                elif package == 'huggingface_hub':
                    import huggingface_hub
            except ImportError:
                missing_packages[package] = version
        
        # If no packages are missing, return success immediately
        if not missing_packages:
            logger.info("All required packages already installed.")
            return True
        
        # If there are missing packages, install them with progress reporting
        progress_bar = st.progress(0)
        status_text = st.empty()
        
        for i, (package, version) in enumerate(missing_packages.items()):
            try:
                progress = (i / len(missing_packages))
                progress_bar.progress(progress)
                status_text.text(f"Installing {package}...")
                
                result = subprocess.run(
                    [sys.executable, "-m", "pip", "install", f"{package}>={version}"], 
                    capture_output=True, 
                    text=True
                )
                
                if result.returncode != 0:
                    st.error(f"Failed to install {package}: {result.stderr}")
                    logger.error(f"Package installation failed: {package}")
                    return False
                    
            except Exception as e:
                st.error(f"Error installing {package}: {str(e)}")
                logger.error(f"Package installation error: {str(e)}")
                return False
        
        progress_bar.progress(1.0)
        status_text.text("All packages installed successfully!")
        time.sleep(0.5)
        progress_bar.empty()
        status_text.empty()
        return True

def install_custom_packages(package_list):
    """Install custom packages specified by the user without page refresh"""
    if not package_list.strip():
        return True, "No packages specified"
    
    # Split and clean package list
    packages = [pkg.strip() for pkg in package_list.split(',') if pkg.strip()]
    
    if not packages:
        return True, "No valid packages specified"
    
    status_placeholder = st.sidebar.empty()
    progress_bar = st.sidebar.progress(0)
    
    results = []
    success = True
    
    for i, package in enumerate(packages):
        try:
            progress = (i / len(packages))
            progress_bar.progress(progress)
            status_placeholder.text(f"Installing {package}...")
            
            result = subprocess.run(
                [sys.executable, "-m", "pip", "install", package], 
                capture_output=True, 
                text=True
            )
            
            if result.returncode != 0:
                error_msg = f"Failed to install {package}: {result.stderr}"
                results.append(error_msg)
                logger.error(error_msg)
                success = False
            else:
                results.append(f"Successfully installed {package}")
                logger.info(f"Successfully installed custom package: {package}")
                
        except Exception as e:
            error_msg = f"Error installing {package}: {str(e)}"
            results.append(error_msg)
            logger.error(error_msg)
            success = False
    
    progress_bar.progress(1.0)
    status_placeholder.text("Installation complete!")
    time.sleep(0.5)
    progress_bar.empty()
    status_placeholder.empty()
    
    return success, "\n".join(results)

@st.cache_resource(ttl=3600)
def init_ai_models_direct():
    """Direct implementation using the exact pattern from the example code"""
    try:
        # Get token from secrets
        token = get_secret("github_token_api")
        if not token:
            st.error("GitHub token not found in secrets. Please add 'github_token_api' to your HuggingFace Spaces secrets.")
            return None
        
        # Log what we're doing - for debugging
        logger.info(f"Initializing AI model with token: {token[:5]}...")
        
        # Use exact imports as in your example
        import os
        from azure.ai.inference import ChatCompletionsClient
        from azure.ai.inference.models import SystemMessage, UserMessage
        from azure.core.credentials import AzureKeyCredential
        
        # Use exact endpoint as in your example
        endpoint = "https://models.inference.ai.azure.com"
        
        # Use default model
        model_name = "gpt-4o"
        
        # Create client exactly as in your example
        client = ChatCompletionsClient(
            endpoint=endpoint,
            credential=AzureKeyCredential(token),
        )
        
        # Return the necessary information
        return {
            "client": client,
            "model_name": model_name,
            "endpoint": endpoint
        }
    except ImportError as ie:
        st.error(f"Import error: {str(ie)}. Please make sure azure-ai-inference is installed.")
        logger.error(f"Import error: {str(ie)}")
        return None
    except Exception as e:
        st.error(f"Error initializing AI model: {str(e)}")
        logger.error(f"Initialization error: {str(e)}")
        return None
        
def suggest_code_completion(code_snippet, models):
    """Generate code completion using the AI model"""
    if not models:
        st.error("AI models not properly initialized.")
        return None
    
    try:
        # Create the prompt
        prompt = f"""Write a complete Manim animation scene based on this code or idea:
{code_snippet}
The code should be a complete, working Manim animation that includes:
- Proper Scene class definition
- Constructor with animations
- Proper use of self.play() for animations
- Proper wait times between animations
Here's the complete Manim code:
"""
        
        with st.spinner("AI is generating your animation code..."):
            # Get the current model name and base URL
            model_name = models["model_name"]
            
            # Convert message to the appropriate format based on model category
            config = MODEL_CONFIGS.get(model_name, MODEL_CONFIGS["default"])
            category = config.get("category", "Other")
            
            if category == "OpenAI":
                # Import OpenAI client
                from openai import OpenAI
                
                # Get token
                token = get_secret("github_token_api")
                
                # Create or get client
                if "openai_client" not in models:
                    client = OpenAI(
                        base_url="https://models.github.ai/inference",
                        api_key=token
                    )
                    models["openai_client"] = client
                else:
                    client = models["openai_client"]
                
                # For OpenAI models, we need role-based messages
                messages = [
                    {"role": "system", "content": "You are an expert in Manim animations."},
                    {"role": "user", "content": prompt}
                ]
                
                # Create params
                params = {
                    "messages": messages,
                    "model": model_name
                }
                
                # Add token parameter
                token_param = config["param_name"]
                params[token_param] = config[token_param]
                
                # Make API call
                response = client.chat.completions.create(**params)
                completed_code = response.choices[0].message.content
                
            else:
                # Use Azure client
                from azure.ai.inference.models import UserMessage
                
                # Convert message format for Azure
                messages = [UserMessage(prompt)]
                api_params, _ = prepare_api_params(messages, model_name)
                
                # Make API call with Azure client
                response = models["client"].complete(**api_params)
                completed_code = response.choices[0].message.content
            
            # Process the code
            if "```python" in completed_code:
                completed_code = completed_code.split("```python")[1].split("```")[0]
            elif "```" in completed_code:
                completed_code = completed_code.split("```")[1].split("```")[0]
            
            # Add Scene class if missing
            if "Scene" not in completed_code:
                completed_code = f"""from manim import *
class MyScene(Scene):
    def construct(self):
        {completed_code}"""
            
            return completed_code
            
    except Exception as e:
        st.error(f"Error generating code: {str(e)}")
        st.code(traceback.format_exc())
        return None

def check_model_freshness():
    """Check if models need to be reloaded based on TTL"""
    if 'ai_models' not in st.session_state or st.session_state.ai_models is None:
        return False
    
    if 'last_loaded' not in st.session_state.ai_models:
        return False
    
    last_loaded = datetime.fromisoformat(st.session_state.ai_models['last_loaded'])
    ttl_hours = 1  # 1 hour TTL
    
    return datetime.now() - last_loaded < timedelta(hours=ttl_hours)

def extract_scene_class_name(python_code):
    """Extract the scene class name from Python code."""
    import re
    scene_classes = re.findall(r'class\s+(\w+)\s*\([^)]*Scene[^)]*\)', python_code)
    
    if scene_classes:
        # Return the first scene class found
        return scene_classes[0]
    else:
        # If no scene class is found, use a default name
        return "MyScene"

def suggest_code_completion(code_snippet, models):
    if not models or "code_model" not in models:
        st.error("AI models not properly initialized")
        return None
        
    try:
        prompt = f"""Write a complete Manim animation scene based on this code or idea:
{code_snippet}
The code should be a complete, working Manim animation that includes:
- Proper Scene class definition
- Constructor with animations
- Proper use of self.play() for animations
- Proper wait times between animations
Here's the complete Manim code:
```python
"""
        with st.spinner("AI is generating your animation code..."):
            response = models["code_model"](
                prompt, 
                max_length=1024,
                do_sample=True,
                temperature=0.2,
                top_p=0.95,
                top_k=50,
                num_return_sequences=1,
                truncation=True,
                pad_token_id=50256
            )
        
        if not response or not response[0].get('generated_text'):
            st.error("No valid completion generated")
            return None
            
        completed_code = response[0]['generated_text']
        if "```python" in completed_code:
            completed_code = completed_code.split("```python")[1].split("```")[0]
        
        if "Scene" not in completed_code:
            completed_code = f"""from manim import *
class MyScene(Scene):
    def construct(self):
        {completed_code}"""
        
        return completed_code
    except Exception as e:
        st.error(f"Error suggesting code: {str(e)}")
        logger.error(f"Code suggestion error: {str(e)}")
        return None

# Quality presets
QUALITY_PRESETS = {
    "480p": {"resolution": "480p", "fps": "30"},
    "720p": {"resolution": "720p", "fps": "30"},
    "1080p": {"resolution": "1080p", "fps": "60"},
    "4K": {"resolution": "2160p", "fps": "60"},
    "8K": {"resolution": "4320p", "fps": "60"}  # Added 8K option
}

# Animation speeds
ANIMATION_SPEEDS = {
    "Slow": 0.5,
    "Normal": 1.0,
    "Fast": 2.0,
    "Very Fast": 3.0
}

# Export formats
EXPORT_FORMATS = {
    "MP4 Video": "mp4",
    "GIF Animation": "gif",
    "WebM Video": "webm",
    "PNG Image Sequence": "png_sequence",
    "SVG Image": "svg"
}

# FPS options
FPS_OPTIONS = [15, 24, 30, 60, 120]

def highlight_code(code):
    formatter = HtmlFormatter(style='monokai')
    highlighted = highlight(code, PythonLexer(), formatter)
    return highlighted, formatter.get_style_defs()

def generate_manim_preview(python_code):
    """Generate a lightweight preview of the Manim animation"""
    try:
        # Extract scene components for preview
        scene_objects = []
        if "Circle" in python_code:
            scene_objects.append("circle")
        if "Square" in python_code:
            scene_objects.append("square")
        if "MathTex" in python_code or "Tex" in python_code:
            scene_objects.append("equation")
        if "Text" in python_code:
            scene_objects.append("text")
        if "Axes" in python_code:
            scene_objects.append("graph")
        if "ThreeDScene" in python_code or "ThreeDAxes" in python_code:
            scene_objects.append("3D scene")
        if "Sphere" in python_code:
            scene_objects.append("sphere")
        if "Cube" in python_code:
            scene_objects.append("cube")
            
        # Generate a more detailed visual preview based on extracted objects
        object_icons = {
            "circle": "⭕",
            "square": "🔲",
            "equation": "📊",
            "text": "📝",
            "graph": "📈",
            "3D scene": "🧊",
            "sphere": "🌐",
            "cube": "🧊"
        }
        
        icon_html = ""
        for obj in scene_objects:
            if obj in object_icons:
                icon_html += f'<span style="font-size:2rem; margin:0.3rem;">{object_icons[obj]}</span>'
        
        preview_html = f"""
        <div style="background-color:#000000; width:100%; height:220px; border-radius:10px; display:flex; flex-direction:column; align-items:center; justify-content:center; color:white; text-align:center;">
            <h3 style="margin-bottom:10px;">Animation Preview</h3>
            <div style="margin-bottom:15px;">
                {icon_html if icon_html else '<span style="font-size:2rem;">🎬</span>'}
            </div>
            <p>Scene contains: {', '.join(scene_objects) if scene_objects else 'No detected objects'}</p>
            <div style="margin-top:10px; font-size:0.8rem; opacity:0.8;">Full rendering required for accurate preview</div>
        </div>
        """
        return preview_html
    except Exception as e:
        logger.error(f"Preview generation error: {str(e)}")
        return f"""
        <div style="background-color:#FF0000; width:100%; height:200px; border-radius:10px; display:flex; align-items:center; justify-content:center; color:white; text-align:center;">
            <div>
                <h3>Preview Error</h3>
                <p>{str(e)}</p>
            </div>
        </div>
        """

def prepare_audio_for_manim(audio_file, target_dir):
    """Process audio file and return path for use in Manim"""
    try:
        # Create audio directory if it doesn't exist
        audio_dir = os.path.join(target_dir, "audio")
        os.makedirs(audio_dir, exist_ok=True)
        
        # Generate a unique filename
        filename = f"audio_{int(time.time())}.mp3"
        output_path = os.path.join(audio_dir, filename)
        
        # Save audio file
        with open(output_path, "wb") as f:
            f.write(audio_file.getvalue())
        
        return output_path
    except Exception as e:
        logger.error(f"Audio processing error: {str(e)}")
        return None

def mp4_to_gif(mp4_path, output_path, fps=15):
    """Convert MP4 to GIF using ffmpeg as a backup when Manim fails"""
    try:
        # Use ffmpeg for conversion with optimized settings
        command = [
            "ffmpeg",
            "-i", mp4_path,
            "-vf", f"fps={fps},scale=640:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse",
            "-loop", "0",
            output_path
        ]
        
        # Run the conversion
        result = subprocess.run(command, capture_output=True, text=True)
        
        if result.returncode != 0:
            logger.error(f"FFmpeg conversion error: {result.stderr}")
            return None
            
        return output_path
        
    except Exception as e:
        logger.error(f"GIF conversion error: {str(e)}")
        return None

def generate_manim_video(python_code, format_type, quality_preset, animation_speed=1.0, audio_path=None, fps=None):
    temp_dir = None
    progress_placeholder = st.empty()
    status_placeholder = st.empty()
    log_placeholder = st.empty()
    video_data = None  # Initialize video data variable
    
    try:
        if not python_code or not format_type:
            raise ValueError("Missing required parameters")
            
        # Create temporary directory
        temp_dir = tempfile.mkdtemp(prefix="manim_render_")
        
        # Extract the scene class name from the code
        scene_class = extract_scene_class_name(python_code)
        logger.info(f"Detected scene class: {scene_class}")
        
        # If audio is provided, we need to modify the code to include it
        if audio_path:
            # Check if the code already has a with_sound decorator
            if "with_sound" not in python_code:
                # Add the necessary import
                if "from manim.scene.scene_file_writer import SceneFileWriter" not in python_code:
                    python_code = "from manim.scene.scene_file_writer import SceneFileWriter\n" + python_code
                
                # Add sound to the scene
                scene_def_pattern = f"class {scene_class}\\(.*?\\):"
                scene_def_match = re.search(scene_def_pattern, python_code)
                
                if scene_def_match:
                    scene_def = scene_def_match.group(0)
                    scene_def_with_sound = f"@with_sound(\"{audio_path}\")\n{scene_def}"
                    python_code = python_code.replace(scene_def, scene_def_with_sound)
                else:
                    logger.warning("Could not find scene definition to add audio")
        
        # Write the code to a file
        scene_file = os.path.join(temp_dir, "scene.py")
        with open(scene_file, "w", encoding="utf-8") as f:
            f.write(python_code)
        
        # Map quality preset to Manim quality flag
        quality_map = {
            "480p": "-ql",  # Low quality
            "720p": "-qm",  # Medium quality
            "1080p": "-qh",  # High quality
            "4K": "-qk",     # 4K quality
            "8K": "-qp"      # 8K quality (production quality)
        }
        quality_flag = quality_map.get(quality_preset, "-qm")
        
        # Handle special formats
        if format_type == "png_sequence":
            # For PNG sequence, we need additional flags
            format_arg = "--format=png"
            extra_args = ["--save_pngs"]
        elif format_type == "svg":
            # For SVG, we need a different format
            format_arg = "--format=svg"
            extra_args = []
        else:
            # Standard video formats
            format_arg = f"--format={format_type}"
            extra_args = []
        
        # Add custom FPS if specified
        if fps is not None:
            extra_args.append(f"--fps={fps}")
        
        # Show status and create progress bar
        status_placeholder.info(f"Rendering {scene_class} with {quality_preset} quality...")
        progress_bar = progress_placeholder.progress(0)
        
        # Build command
        command = [
            "manim",
            scene_file,
            scene_class,
            quality_flag,
            format_arg
        ]
        command.extend(extra_args)
        
        logger.info(f"Running command: {' '.join(command)}")
        
        # Execute the command
        process = subprocess.Popen(
            command,
            stdout=subprocess.PIPE,
            stderr=subprocess.STDOUT,
            text=True
        )
        
        # Track output
        full_output = []
        output_file_path = None
        mp4_output_path = None  # Track MP4 output for GIF fallback
        
        # Animation tracking variables
        total_animations = None
        current_animation = 0
        total_frames = None
        current_frame = 0
        
        while True:
            line = process.stdout.readline()
            if not line and process.poll() is not None:
                break
            
            full_output.append(line)
            log_placeholder.code("".join(full_output[-10:]))
            
            # Try to detect total animations
            if "Rendering animation number" in line or "Processing animation" in line:
                try:
                    # Extract current animation number
                    anim_match = re.search(r"(?:Rendering animation number|Processing animation) (\d+) (?:out of|/) (\d+)", line)
                    if anim_match:
                        current_animation = int(anim_match.group(1))
                        total_animations = int(anim_match.group(2))
                        logger.info(f"Animation progress: {current_animation}/{total_animations}")
                        
                        # Calculate progress based on animations
                        animation_progress = current_animation / total_animations
                        progress_bar.progress(animation_progress)
                        status_placeholder.info(f"Rendering {scene_class}: Animation {current_animation}/{total_animations} ({int(animation_progress*100)}%)")
                except Exception as e:
                    logger.error(f"Error parsing animation progress: {str(e)}")
            
            # Try to extract total frames information as fallback
            elif "Render animations with total frames:" in line and not total_animations:
                try:
                    total_frames = int(line.split("Render animations with total frames:")[1].strip().split()[0])
                    logger.info(f"Total frames to render: {total_frames}")
                except Exception as e:
                    logger.error(f"Error parsing total frames: {str(e)}")
            
            # Update progress bar based on frame information if animation count not available
            elif "Rendering frame" in line and total_frames and not total_animations:
                try:
                    # Extract current frame number
                    frame_match = re.search(r"Rendering frame (\d+)", line)
                    if frame_match:
                        current_frame = int(frame_match.group(1))
                        # Calculate progress as current frame / total frames
                        frame_progress = min(0.99, current_frame / total_frames)
                        progress_bar.progress(frame_progress)
                        # Update status with frame information
                        status_placeholder.info(f"Rendering {scene_class}: Frame {current_frame}/{total_frames} ({int(frame_progress*100)}%)")
                except Exception as e:
                    logger.error(f"Error parsing frame progress: {str(e)}")
            elif "%" in line and not total_animations and not total_frames:
                try:
                    # Fallback to percentage if available
                    percent = float(line.split("%")[0].strip().split()[-1])
                    progress_bar.progress(min(0.99, percent / 100))
                except:
                    pass
                    
            # Try to capture the output file path from Manim's output
            if "File ready at" in line:
                try:
                    # Combine next few lines to get the full path
                    path_parts = []
                    path_parts.append(line.split("File ready at")[-1].strip())
                    
                    # Read up to 5 more lines to get the complete path
                    for _ in range(5):
                        additional_line = process.stdout.readline()
                        if additional_line:
                            full_output.append(additional_line)
                            path_parts.append(additional_line.strip())
                            if additional_line.strip().endswith(('.mp4', '.gif', '.webm', '.svg')):
                                break
                    
                    # Join all parts and clean up
                    potential_path = ''.join(path_parts).replace("'", "").strip()
                    # Look for path pattern surrounded by quotes
                    path_match = re.search(r'([\'"]?)((?:/|[a-zA-Z]:\\).*?\.(?:mp4|gif|webm|svg))(\1)', potential_path)
                    if path_match:
                        output_file_path = path_match.group(2)
                        logger.info(f"Found output path in logs: {output_file_path}")
                        
                        # Track MP4 file for potential GIF fallback
                        if output_file_path.endswith('.mp4'):
                            mp4_output_path = output_file_path
                except Exception as e:
                    logger.error(f"Error parsing output path: {str(e)}")
        
        # Wait for the process to complete
        process.wait()
        progress_bar.progress(1.0)
        
        # IMPORTANT: Wait a moment for file system to catch up
        time.sleep(3)
        
        # Rest of the function remains the same
        
        # Special handling for GIF format - if Manim failed to generate a GIF but we have an MP4
        if format_type == "gif" and (not output_file_path or not os.path.exists(output_file_path)) and mp4_output_path and os.path.exists(mp4_output_path):
            status_placeholder.info("GIF generation via Manim failed. Trying FFmpeg conversion...")
            
            # Generate a GIF using FFmpeg
            gif_output_path = os.path.join(temp_dir, f"{scene_class}_converted.gif")
            gif_path = mp4_to_gif(mp4_output_path, gif_output_path, fps=fps if fps else 15)
            
            if gif_path and os.path.exists(gif_path):
                output_file_path = gif_path
                logger.info(f"Successfully converted MP4 to GIF using FFmpeg: {gif_path}")
        
        # For PNG sequence, we need to collect the PNGs
        if format_type == "png_sequence":
            # Find the PNG directory
            png_dirs = []
            search_dirs = [
                os.path.join(os.getcwd(), "media", "images", scene_class, "Animations"),
                os.path.join(temp_dir, "media", "images", scene_class, "Animations"),
                "/tmp/media/images", 
            ]
            
            for search_dir in search_dirs:
                if os.path.exists(search_dir):
                    for root, dirs, _ in os.walk(search_dir):
                        for d in dirs:
                            if os.path.exists(os.path.join(root, d)):
                                png_dirs.append(os.path.join(root, d))
            
            if png_dirs:
                # Get the newest directory
                newest_dir = max(png_dirs, key=os.path.getctime)
                
                # Create a zip file with all PNGs
                png_files = [f for f in os.listdir(newest_dir) if f.endswith('.png')]
                if png_files:
                    zip_path = os.path.join(temp_dir, f"{scene_class}_pngs.zip")
                    
                    with zipfile.ZipFile(zip_path, 'w') as zipf:
                        for png in png_files:
                            png_path = os.path.join(newest_dir, png)
                            zipf.write(png_path, os.path.basename(png_path))
                    
                    with open(zip_path, 'rb') as f:
                        video_data = f.read()
                    
                    logger.info(f"Created PNG sequence zip: {zip_path}")
                else:
                    logger.error("No PNG files found in directory")
            else:
                logger.error("No PNG directories found")
        elif output_file_path and os.path.exists(output_file_path):
            # For other formats, read the output file directly
            with open(output_file_path, 'rb') as f:
                video_data = f.read()
            logger.info(f"Read output file from path: {output_file_path}")
        else:
            # If we didn't find the output path, search for files
            search_paths = [
                os.path.join(os.getcwd(), "media", "videos"),
                os.path.join(os.getcwd(), "media", "videos", "scene"),
                os.path.join(os.getcwd(), "media", "videos", scene_class),
                "/tmp/media/videos",
                temp_dir,
                os.path.join(temp_dir, "media", "videos"),
            ]
            
            # Add quality-specific paths
            for quality in ["480p30", "720p30", "1080p60", "2160p60", "4320p60"]:
                search_paths.append(os.path.join(os.getcwd(), "media", "videos", "scene", quality))
                search_paths.append(os.path.join(os.getcwd(), "media", "videos", scene_class, quality))
            
            # For SVG format
            if format_type == "svg":
                search_paths.extend([
                    os.path.join(os.getcwd(), "media", "designs"),
                    os.path.join(os.getcwd(), "media", "designs", scene_class),
                ])
            
            # Find all output files in the search paths
            output_files = []
            for search_path in search_paths:
                if os.path.exists(search_path):
                    for root, _, files in os.walk(search_path):
                        for file in files:
                            if file.endswith(f".{format_type}") and "partial" not in file:
                                file_path = os.path.join(root, file)
                                if os.path.exists(file_path):
                                    output_files.append(file_path)
                                    logger.info(f"Found output file: {file_path}")
            
            if output_files:
                # Get the newest file
                latest_file = max(output_files, key=os.path.getctime)
                with open(latest_file, 'rb') as f:
                    video_data = f.read()
                logger.info(f"Read output from file search: {latest_file}")
                
                # If the format is GIF but we got an MP4, try to convert it
                if format_type == "gif" and latest_file.endswith('.mp4'):
                    gif_output_path = os.path.join(temp_dir, f"{scene_class}_converted.gif")
                    gif_path = mp4_to_gif(latest_file, gif_output_path, fps=fps if fps else 15)
                    
                    if gif_path and os.path.exists(gif_path):
                        with open(gif_path, 'rb') as f:
                            video_data = f.read()
                        logger.info(f"Successfully converted MP4 to GIF using FFmpeg: {gif_path}")
        
        # If we got output data, return it
        if video_data:
            file_size_mb = len(video_data) / (1024 * 1024)
            
            # Clear placeholders
            progress_placeholder.empty()
            status_placeholder.empty()
            log_placeholder.empty()
            
            return video_data, f"✅ Animation generated successfully! ({file_size_mb:.1f} MB)"
        else:
            output_str = ''.join(full_output)
            logger.error(f"No output files found. Full output: {output_str}")
            
            # Check if we have an MP4 but need a GIF (special handling for GIF issues)
            if format_type == "gif":
                # Try one more aggressive search for any MP4 file
                mp4_files = []
                for search_path in [os.getcwd(), temp_dir, "/tmp"]:
                    for root, _, files in os.walk(search_path):
                        for file in files:
                            if file.endswith('.mp4') and scene_class.lower() in file.lower():
                                mp4_path = os.path.join(root, file)
                                if os.path.exists(mp4_path) and os.path.getsize(mp4_path) > 0:
                                    mp4_files.append(mp4_path)
                
                if mp4_files:
                    newest_mp4 = max(mp4_files, key=os.path.getctime)
                    logger.info(f"Found MP4 for GIF conversion: {newest_mp4}")
                    
                    # Convert to GIF
                    gif_output_path = os.path.join(temp_dir, f"{scene_class}_converted.gif")
                    gif_path = mp4_to_gif(newest_mp4, gif_output_path, fps=fps if fps else 15)
                    
                    if gif_path and os.path.exists(gif_path):
                        with open(gif_path, 'rb') as f:
                            video_data = f.read()
                        
                        # Clear placeholders
                        progress_placeholder.empty()
                        status_placeholder.empty()
                        log_placeholder.empty()
                        
                        file_size_mb = len(video_data) / (1024 * 1024)
                        return video_data, f"✅ Animation converted to GIF successfully! ({file_size_mb:.1f} MB)"
            
            return None, f"❌ Error: No output files were generated.\n\nMakim output:\n{output_str[:500]}..."
    
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        import traceback
        logger.error(traceback.format_exc())
        
        if progress_placeholder:
            progress_placeholder.empty()
        if status_placeholder:
            status_placeholder.error(f"Rendering Error: {str(e)}")
        if log_placeholder:
            log_placeholder.empty()
        
        return None, f"❌ Error: {str(e)}"
    
    finally:
        # CRITICAL: Only cleanup after we've captured the output data
        if temp_dir and os.path.exists(temp_dir) and video_data is not None:
            try:
                shutil.rmtree(temp_dir)
                logger.info(f"Cleaned up temp dir: {temp_dir}")
            except Exception as e:
                logger.error(f"Failed to clean temp dir: {str(e)}")

# ENHANCED PYTHON RUNNER FUNCTIONS
def detect_input_calls(code):
    """Detect input() calls in Python code to prepare for handling"""
    input_calls = []
    lines = code.split('\n')
    for i, line in enumerate(lines):
        if 'input(' in line and not line.strip().startswith('#'):
            # Try to extract the prompt if available
            prompt_match = re.search(r'input\([\'"](.+?)[\'"]\)', line)
            prompt = prompt_match.group(1) if prompt_match else f"Input for line {i+1}"
            input_calls.append({"line": i+1, "prompt": prompt})
    return input_calls

def run_python_script_enhanced(code, inputs=None, timeout=60, enable_debug=False, enable_profile=False, 
                              additional_libs=None, project_files=None, realtime_viz=False):
    """Enhanced version of run_python_script with debugging, profiling, etc."""
    result = {
        "stdout": "",
        "stderr": "",
        "exception": None,
        "plots": [],
        "dataframes": [],
        "execution_time": 0,
        "profile_data": None,
        "debug_steps": [],
        "realtime_data": []
    }
    
    # Create a tempdir for script execution
    with tempfile.TemporaryDirectory() as temp_dir:
        # Path for saving plots
        plot_dir = os.path.join(temp_dir, 'plots')
        os.makedirs(plot_dir, exist_ok=True)
        
        # Handle multi-file project if provided
        if project_files:
            for filename, file_content in project_files.items():
                with open(os.path.join(temp_dir, filename), 'w', encoding='utf-8') as f:
                    f.write(file_content)
            
            # Set the main script path
            main_script = os.path.join(temp_dir, "main.py")
        else:
            # Write the single code file
            main_script = os.path.join(temp_dir, "script.py")
            with open(main_script, 'w', encoding='utf-8') as f:
                f.write(code)
        
        # Add library imports if specified
        if additional_libs:
            lib_imports = "\n".join([f"import {lib}" for lib in additional_libs if lib != "numpy" and lib != "matplotlib"])
            if lib_imports:
                with open(main_script, 'r+', encoding='utf-8') as f:
                    content = f.read()
                    f.seek(0, 0)
                    f.write(lib_imports + "\n\n" + content)
        
        # Add debugging setup if enabled
        if enable_debug:
            debug_setup = """
import pdb
import sys
import traceback

class StringIODebugger:
    def __init__(self):
        self.steps = []
        
    def add_step(self, frame, event, arg):
        if event == 'line':
            self.steps.append({
                'file': frame.f_code.co_filename,
                'line': frame.f_lineno,
                'function': frame.f_code.co_name,
                'locals': {k: str(v) for k, v in frame.f_locals.items() if not k.startswith('__')}
            })
        return self

debug_steps = []
def trace_calls(frame, event, arg):
    if event != 'call':
        return
    co = frame.f_code
    func_name = co.co_name
    if func_name == 'write':
        return
    line_no = frame.f_lineno
    filename = co.co_filename
    if 'debugger' in filename or func_name.startswith('__'):
        return
    debug_steps.append(f"Calling {func_name} in {filename} at line {line_no}")
    return trace_calls

sys.settrace(trace_calls)
"""
            with open(main_script, 'r+', encoding='utf-8') as f:
                content = f.read()
                f.seek(0, 0)
                f.write(debug_setup + "\n" + content)
        
        # Add profiling if enabled
        if enable_profile:
            profile_setup = """
import cProfile
import pstats
import io

# Set up profiler
profiler = cProfile.Profile()
profiler.enable()
"""
            profile_teardown = """
# Finish profiling
profiler.disable()
s = io.StringIO()
ps = pstats.Stats(profiler, stream=s).sort_stats('cumulative')
ps.print_stats()
with open('profile_results.txt', 'w') as f:
    f.write(s.getvalue())
"""
            with open(main_script, 'r+', encoding='utf-8') as f:
                content = f.read()
                f.seek(0, 0)
                f.write(profile_setup + "\n" + content + "\n" + profile_teardown)
        
        # Add real-time visualization if enabled
        if realtime_viz:
            realtime_viz_setup = """
# Setup for real-time visualization
import threading
import json
import time

class RealTimeData:
    def __init__(self):
        self.data = []
        
    def add_data(self, label, value):
        self.data.append({'label': label, 'value': value, 'time': time.time()})
        # Write to file for real-time monitoring
        with open('realtime_data.json', 'w') as f:
            json.dump(self.data, f)

rt_data = RealTimeData()

# Example usage: rt_data.add_data("iteration", i)
"""
            with open(main_script, 'r+', encoding='utf-8') as f:
                content = f.read()
                f.seek(0, 0)
                f.write(realtime_viz_setup + "\n" + content)
        
        # Add input handling code
        if inputs and len(inputs) > 0:
            # Modify the code to use predefined inputs instead of waiting for user input
            input_handling = """
# Input values provided by the user
__INPUT_VALUES = {}
__INPUT_INDEX = 0
# Override the built-in input function
def input(prompt=''):
    global __INPUT_INDEX
    print(prompt, end='')
    if __INPUT_INDEX < len(__INPUT_VALUES):
        value = __INPUT_VALUES[__INPUT_INDEX]
        __INPUT_INDEX += 1
        print(value)  # Echo the input
        return value
    else:
        print("\\n[WARNING] No more predefined inputs available, using empty string")
        return ""
""".format(inputs)
            
            with open(main_script, 'r+', encoding='utf-8') as f:
                content = f.read()
                f.seek(0, 0)
                f.write(input_handling + "\n" + content)
        
        # Add matplotlib and pandas handling
        data_handling = """
# Add plot saving code if matplotlib is used
import os

# For matplotlib plots
if 'matplotlib' in globals() or 'matplotlib.pyplot' in globals() or 'plt' in globals():
    import matplotlib
    matplotlib.use('Agg')  # Use non-interactive backend
    import matplotlib.pyplot as plt
    
    # Hook to save all figures
    original_show = plt.show
    def custom_show(*args, **kwargs):
        for i, fig in enumerate(map(plt.figure, plt.get_fignums())):
            fig.savefig(os.path.join('{}', f'plot_{{i}}.png'))
        return original_show(*args, **kwargs)
    plt.show = custom_show

# For pandas DataFrames
if 'pandas' in globals() or 'pd' in globals():
    import pandas as pd
    import json
    
    # Save DataFrames
    original_df_repr_html = pd.DataFrame._repr_html_
    def custom_df_repr_html(self):
        try:
            df_info = {{
                "name": str(id(self)),
                "shape": self.shape,
                "columns": list(map(str, self.columns)),
                "preview_html": self.head().to_html()
            }}
            with open(f'df_{{id(self)}}.json', 'w') as f:
                json.dump(df_info, f)
        except:
            pass
        return original_df_repr_html(self)
    pd.DataFrame._repr_html_ = custom_df_repr_html
""".format(plot_dir.replace('\\', '\\\\'))
        
        with open(main_script, 'r+', encoding='utf-8') as f:
            content = f.read()
            f.seek(0, 0)
            f.write(data_handling + "\n" + content)
        
        # Files for capturing stdout and stderr
        stdout_file = os.path.join(temp_dir, 'stdout.txt')
        stderr_file = os.path.join(temp_dir, 'stderr.txt')
        
        # Execute with timeout
        start_time = time.time()
        try:
            # Run the script with stdout and stderr redirection
            with open(stdout_file, 'w') as stdout_f, open(stderr_file, 'w') as stderr_f:
                process = subprocess.Popen(
                    [sys.executable, main_script],
                    stdout=stdout_f,
                    stderr=stderr_f,
                    cwd=temp_dir
                )
                
                # Real-time monitoring for real-time visualization
                if realtime_viz:
                    realtime_data_file = os.path.join(temp_dir, 'realtime_data.json')
                    while process.poll() is None:
                        if os.path.exists(realtime_data_file):
                            try:
                                with open(realtime_data_file, 'r') as f:
                                    result["realtime_data"] = json.load(f)
                            except:
                                pass
                        time.sleep(0.1)
                        
                        # Check for timeout
                        if time.time() - start_time > timeout:
                            process.kill()
                            result["stderr"] += f"\nScript execution timed out after {timeout} seconds."
                            result["exception"] = "TimeoutError"
                            break
                else:
                    try:
                        process.wait(timeout=timeout)
                    except subprocess.TimeoutExpired:
                        process.kill()
                        result["stderr"] += f"\nScript execution timed out after {timeout} seconds."
                        result["exception"] = "TimeoutError"
            
            # Read the output
            with open(stdout_file, 'r') as f:
                result["stdout"] = f.read()
            
            with open(stderr_file, 'r') as f:
                result["stderr"] = f.read()
            
            # Collect plots
            if os.path.exists(plot_dir):
                plot_files = sorted([f for f in os.listdir(plot_dir) if f.endswith('.png')])
                for plot_file in plot_files:
                    with open(os.path.join(plot_dir, plot_file), 'rb') as f:
                        result["plots"].append(f.read())
            
            # Collect dataframes
            df_files = [f for f in os.listdir(temp_dir) if f.startswith('df_') and f.endswith('.json')]
            for df_file in df_files:
                with open(os.path.join(temp_dir, df_file), 'r') as f:
                    result["dataframes"].append(json.load(f))
            
            # Collect profiling data if enabled
            if enable_profile and os.path.exists(os.path.join(temp_dir, 'profile_results.txt')):
                with open(os.path.join(temp_dir, 'profile_results.txt'), 'r') as f:
                    result["profile_data"] = f.read()
            
            # Collect debug data if enabled
            if enable_debug and 'debug_steps' in globals():
                result["debug_steps"] = debug_steps
            
            # Calculate execution time
            result["execution_time"] = time.time() - start_time
            
        except Exception as e:
            result["exception"] = str(e)
            result["stderr"] += f"\nError executing script: {str(e)}"
            
    return result

def display_python_script_results_enhanced(result):
    """Display the enhanced results from the Python script execution"""
    if not result:
        st.error("No results to display.")
        return
    
    # Display execution time
    st.info(f"Execution completed in {result['execution_time']:.2f} seconds")
    
    # Display any errors
    if result["exception"]:
        st.error(f"Exception occurred: {result['exception']}")
    
    if result["stderr"]:
        st.error("Errors:")
        st.code(result["stderr"], language="bash")
    
    # Display profiling data if available
    if result.get("profile_data"):
        with st.expander("Profiling Results"):
            st.code(result["profile_data"], language="bash")
    
    # Display debugging steps if available
    if result.get("debug_steps"):
        with st.expander("Debugging Steps"):
            for i, step in enumerate(result["debug_steps"]):
                st.markdown(f"**Step {i+1}**: {step}")
    
    # Display plots if any
    if result["plots"]:
        st.markdown("### Plots")
        cols = st.columns(min(3, len(result["plots"])))
        for i, plot_data in enumerate(result["plots"]):
            cols[i % len(cols)].image(plot_data, use_column_width=True)
            
            # Add button to use this plot in Manim
            if cols[i % len(cols)].button(f"Use in Manim", key=f"use_plot_{i}"):
                # Create a temporary file
                with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
                    tmp.write(plot_data)
                    plot_path = tmp.name
                
                # Generate Manim code
                plot_code = f"""
# Import the plot image
plot_image = ImageMobject(r"{plot_path}")
plot_image.scale(2)  # Adjust size as needed
self.play(FadeIn(plot_image))
self.wait(1)
"""
                if st.session_state.code:
                    st.session_state.code += "\n" + plot_code
                else:
                    st.session_state.code = f"""from manim import *
class PlotScene(Scene):
    def construct(self):
        {plot_code}
"""
                st.session_state.temp_code = st.session_state.code
                st.success(f"Added plot to your Manim code!")
                # Set pending tab switch to editor tab
                st.session_state.pending_tab_switch = 0
                st.rerun()
    
    # Display dataframes if any
    if result["dataframes"]:
        st.markdown("### DataFrames")
        for df_info in result["dataframes"]:
            with st.expander(f"{df_info.get('name', 'DataFrame')} - {df_info['shape'][0]} rows × {df_info['shape'][1]} columns"):
                st.markdown(df_info["preview_html"], unsafe_allow_html=True)
                
                # Add button to visualize this dataframe in Manim
                if st.button(f"Visualize in Manim", key=f"viz_df_{df_info.get('name', 'df')}"):
                    # Generate Manim code for dataframe visualization
                    df_viz_code = f"""
# Create a simple table visualization
columns = {df_info['columns']}
table = Table(
    col_labels=[Text(col, font_size=24) for col in columns]
)

# Add data rows (showing first 5 rows)
for i in range(min(5, {df_info['shape'][0]})):
    # This is a placeholder - in a real implementation, you'd extract actual data
    table.add_row(*[Text(f"Row {{i}}, Col {{j}}", font_size=20) for j in range(len(columns))])

self.play(Create(table))
self.wait(1)
"""
                    if st.session_state.code:
                        st.session_state.code += "\n" + df_viz_code
                    else:
                        st.session_state.code = f"""from manim import *
class DataFrameScene(Scene):
    def construct(self):
        {df_viz_code}
"""
                    st.session_state.temp_code = st.session_state.code
                    st.success(f"Added DataFrame visualization to your Manim code!")
                    # Set pending tab switch to editor tab
                    st.session_state.pending_tab_switch = 0
                    st.rerun()
    
    # Display standard output
    if result["stdout"]:
        st.markdown("### Standard Output")
        st.code(result["stdout"], language="bash")
    
    # Display real-time data if available
    if result.get("realtime_data"):
        st.markdown("### Real-time Data")
        
        # Convert to DataFrame for easier visualization
        import pandas as pd
        rt_df = pd.DataFrame(result["realtime_data"])
        
        # Create a plotly chart
        import plotly.express as px
        if not rt_df.empty and "time" in rt_df.columns and "value" in rt_df.columns:
            fig = px.line(rt_df, x="time", y="value", color="label" if "label" in rt_df.columns else None,
                        title="Real-time Data Visualization")
            st.plotly_chart(fig, use_container_width=True)
            
            # Add button to create Manim animation from this data
            if st.button("Create Manim Animation from Data", key="create_manim_from_rt"):
                # Extract data points
                data_points = []
                for _, row in rt_df.iterrows():
                    if "value" in row:
                        data_points.append(float(row["value"]))
                
                # Generate Manim code
                rt_viz_code = f"""
# Visualize real-time data
data = {data_points}
axes = Axes(
    x_range=[0, {len(data_points)}, 1],
    y_range=[{min(data_points) if data_points else 0}, {max(data_points) if data_points else 10}, {(max(data_points)-min(data_points))/10 if data_points and max(data_points) > min(data_points) else 1}],
    axis_config={{"color": BLUE}}
)
points = [axes.coords_to_point(i, v) for i, v in enumerate(data)]
graph = VMobject(color=RED)
graph.set_points_as_corners(points)

self.play(Create(axes))
self.play(Create(graph), run_time=2)
self.wait(1)
"""
                if st.session_state.code:
                    st.session_state.code += "\n" + rt_viz_code
                else:
                    st.session_state.code = f"""from manim import *
class DataVisualizationScene(Scene):
    def construct(self):
        {rt_viz_code}
"""
                st.session_state.temp_code = st.session_state.code
                st.success(f"Added real-time data visualization to your Manim code!")
                # Set pending tab switch to editor tab
                st.session_state.pending_tab_switch = 0
                st.rerun()

# C/C++ RUNNER FUNCTIONS
def compile_cpp_code_enhanced(code, settings, project_files=None, enable_debug=False, breakpoints=None, watch_vars=None):
    """Enhanced function to compile C++ code with advanced options."""
    try:
        # Create a temporary directory for compilation
        temp_dir = tempfile.mkdtemp(prefix="cpp_runner_")
        
        # Write the project files
        if project_files:
            for filename, content in project_files.items():
                file_path = os.path.join(temp_dir, filename)
                with open(file_path, "w") as f:
                    f.write(content)
            # Set main file for single file mode
            cpp_file = os.path.join(temp_dir, "main.cpp")
        else:
            # Write the single code file
            cpp_file = os.path.join(temp_dir, "main.cpp")
            with open(cpp_file, "w") as f:
                f.write(code)
        
        # Output executable path
        exe_file = os.path.join(temp_dir, "program.exe" if platform.system() == "Windows" else "program")
        
        # Build the compilation command
        compiler = settings.get("compiler", "g++")
        std_version = settings.get("std", "c++17")
        optimization = settings.get("optimization", "-O2")
        
        compile_cmd = [
            compiler,
            "-std=" + std_version,
            optimization
        ]
        
        # Add debug flag if debugging is enabled
        if enable_debug:
            compile_cmd.append("-g")
        
        # Add preprocessor definitions
        for definition in settings.get("definitions", []):
            if "=" in definition:
                name, value = definition.split("=", 1)
                compile_cmd.append(f"-D{name}={value}")
            else:
                compile_cmd.append(f"-D{definition}")
        
        # Add include paths
        for path in settings.get("include_paths", []):
            compile_cmd.append(f"-I{path}")
        
        # Add library paths
        for path in settings.get("library_paths", []):
            compile_cmd.append(f"-L{path}")
        
        # Add files to compile
        if project_files:
            source_files = [os.path.join(temp_dir, f) for f in project_files.keys() if f.endswith((".cpp", ".c", ".cc"))]
            compile_cmd.extend(source_files)
        else:
            compile_cmd.append(cpp_file)
        
        # Output file
        compile_cmd.extend(["-o", exe_file])
        
        # Add libraries
        for lib in settings.get("libraries", []):
            if lib == "Eigen":
                # Eigen is header-only, nothing to link
                pass
            elif lib == "OpenCV":
                # Add OpenCV libraries
                try:
                    # Get OpenCV libraries using pkg-config
                    pkg_config = subprocess.run(
                        ["pkg-config", "--libs", "opencv4"],
                        capture_output=True,
                        text=True,
                        check=False
                    )
                    if pkg_config.returncode == 0:
                        compile_cmd.extend(pkg_config.stdout.strip().split())
                    else:
                        # Try opencv instead of opencv4
                        pkg_config = subprocess.run(
                            ["pkg-config", "--libs", "opencv"],
                            capture_output=True,
                            text=True,
                            check=False
                        )
                        if pkg_config.returncode == 0:
                            compile_cmd.extend(pkg_config.stdout.strip().split())
                        else:
                            # Fallback to common OpenCV libraries
                            compile_cmd.extend(["-lopencv_core", "-lopencv_imgproc", "-lopencv_highgui"])
                except:
                    # Fallback to common OpenCV libraries
                    compile_cmd.extend(["-lopencv_core", "-lopencv_imgproc", "-lopencv_highgui"])
            elif lib == "Boost":
                # Add common Boost libraries
                compile_cmd.extend(["-lboost_system", "-lboost_filesystem"])
            elif lib == "FFTW":
                compile_cmd.append("-lfftw3")
            elif lib == "SDL2":
                compile_cmd.append("-lSDL2")
            elif lib == "SFML":
                compile_cmd.extend(["-lsfml-graphics", "-lsfml-window", "-lsfml-system"])
            elif lib == "OpenGL":
                compile_cmd.extend(["-lGL", "-lGLU", "-lglut"])
        
        # Add additional libraries
        for lib in settings.get("additional_libs", []):
            compile_cmd.append(f"-l{lib}")
        
        # Add advanced flags
        if settings.get("advanced_flags"):
            compile_cmd.extend(settings["advanced_flags"].split())
        
        # Run the compilation process
        logger.info(f"Compiling with command: {' '.join(compile_cmd)}")
        result = subprocess.run(
            compile_cmd,
            capture_output=True,
            text=True,
            check=False,
            cwd=temp_dir
        )
        
        if result.returncode != 0:
            return None, result.stderr, temp_dir
        
        return exe_file, None, temp_dir
    except Exception as e:
        return None, str(e), None

def run_cpp_executable_enhanced(exe_path, temp_dir, inputs=None, timeout=30, enable_debug=False, breakpoints=None, watch_vars=None):
    """Enhanced function to run C++ executable with debugging support."""
    result = {
        "stdout": "",
        "stderr": "",
        "execution_time": 0,
        "images": [],
        "exception": None,
        "debug_output": None,
        "memory_usage": None
    }
    
    try:
        # Prepare input data if provided
        input_data = "\n".join(inputs) if inputs else None
        
        # Start timing
        start_time = time.time()
        
        if enable_debug and breakpoints:
            # Run with GDB for debugging
            gdb_commands = ["set pagination off"]
            
            # Add breakpoints
            for bp in breakpoints:
                gdb_commands.append(f"break {bp}")
            
            # Add watchpoints for variables
            if watch_vars:
                for var in watch_vars:
                    gdb_commands.append(f"watch {var}")
            
            # Run the program
            gdb_commands.append("run")
            
            # Continue to end
            gdb_commands.append("continue")
            
            # Quit GDB
            gdb_commands.append("quit")
            
            # Create GDB command file
            gdb_cmd_file = os.path.join(temp_dir, "gdb_commands.txt")
            with open(gdb_cmd_file, "w") as f:
                f.write("\n".join(gdb_commands))
            
            # Run with GDB
            process = subprocess.run(
                ["gdb", "-x", gdb_cmd_file, "-batch", exe_path],
                input=input_data,
                text=True,
                capture_output=True,
                timeout=timeout,
                cwd=temp_dir
            )
            
            # Capture outputs
            result["stdout"] = process.stdout
            result["stderr"] = process.stderr
            result["debug_output"] = process.stdout
        else:
            # Run normally
            process = subprocess.run(
                [exe_path],
                input=input_data,
                text=True,
                capture_output=True,
                timeout=timeout,
                cwd=temp_dir
            )
            
            # Capture outputs
            result["stdout"] = process.stdout
            result["stderr"] = process.stderr
        
        # Calculate execution time
        result["execution_time"] = time.time() - start_time
        
        # Look for generated images in the executable directory
        for ext in [".png", ".jpg", ".jpeg", ".bmp", ".ppm"]:
            image_files = [f for f in os.listdir(temp_dir) if f.endswith(ext)]
            for img_file in image_files:
                try:
                    img_path = os.path.join(temp_dir, img_file)
                    
                    # For PPM files, convert to PNG for easier display
                    if img_file.endswith(".ppm"):
                        # Create output path
                        png_path = os.path.join(temp_dir, img_file.replace(".ppm", ".png"))
                        
                        # Convert using PIL
                        from PIL import Image
                        Image.open(img_path).save(png_path)
                        img_path = png_path
                        img_file = img_file.replace(".ppm", ".png")
                    
                    with open(img_path, "rb") as f:
                        result["images"].append({
                            "name": img_file,
                            "data": f.read()
                        })
                except Exception as e:
                    logger.error(f"Error processing image {img_file}: {str(e)}")
        
        # Estimate memory usage
        try:
            if platform.system() != "Windows":
                # Use ps command to get memory usage
                ps_output = subprocess.run(
                    ["ps", "-p", str(process.pid), "-o", "rss="],
                    capture_output=True,
                    text=True,
                    check=False
                )
                if ps_output.returncode == 0:
                    mem_kb = int(ps_output.stdout.strip())
                    result["memory_usage"] = mem_kb / 1024  # Convert to MB
        except:
            pass
        
        return result
    except subprocess.TimeoutExpired:
        result["stderr"] += f"\nProgram execution timed out after {timeout} seconds."
        result["exception"] = "TimeoutError"
        return result
    except Exception as e:
        result["stderr"] += f"\nError executing program: {str(e)}"
        result["exception"] = str(e)
        return result

def parse_animation_steps(python_code):
    """Parse Manim code to extract animation steps for timeline editor"""
    animation_steps = []
    
    # Look for self.play calls in the code
    play_calls = re.findall(r'self\.play\((.*?)\)', python_code, re.DOTALL)
    wait_calls = re.findall(r'self\.wait\((.*?)\)', python_code, re.DOTALL)
    
    # Extract animation objects from play calls
    for i, play_call in enumerate(play_calls):
        # Parse the arguments to self.play()
        animations = [arg.strip() for arg in play_call.split(',')]
        
        # Get wait time after this animation if available
        wait_time = 1.0  # Default wait time
        if i < len(wait_calls):
            wait_match = re.search(r'(\d+\.?\d*)', wait_calls[i])
            if wait_match:
                wait_time = float(wait_match.group(1))
        
        # Add to animation steps
        animation_steps.append({
            "id": i+1,
            "type": "play",
            "animations": animations,
            "duration": wait_time,
            "start_time": sum([step.get("duration", 1.0) for step in animation_steps]),
            "code": f"self.play({play_call})"
        })
    
    return animation_steps

def generate_code_from_timeline(animation_steps, original_code):
    """Generate Manim code from the timeline data"""
    # Extract the class definition and setup
    class_match = re.search(r'(class\s+\w+\s*\([^)]*\)\s*:.*?def\s+construct\s*\(\s*self\s*\)\s*:)', original_code, re.DOTALL)
    
    if not class_match:
        return original_code  # Can't find proper structure to modify
        
    setup_code = class_match.group(1)
    
    # Build the new construct method
    new_code = [setup_code]
    indent = "        "  # Standard Manim indentation
    
    # Add each animation step in order
    for step in sorted(animation_steps, key=lambda x: x["id"]):
        new_code.append(f"{indent}{step['code']}")
        if "duration" in step and step["duration"] > 0:
            new_code.append(f"{indent}self.wait({step['duration']})")
    
    # Add any code that might come after animations
    end_match = re.search(r'(#\s*End\s+of\s+animations.*?$)', original_code, re.DOTALL)
    if end_match:
        new_code.append(end_match.group(1))
        
    # Combine the code parts with proper indentation
    return "\n".join(new_code)

def create_timeline_editor(code):
    """Create an interactive timeline editor for animation sequences"""
    st.markdown("### 🎞️ Animation Timeline Editor")
    
    if not code:
        st.warning("Add animation code first to use the timeline editor.")
        return code
    
    # Parse animation steps from the code
    animation_steps = parse_animation_steps(code)
    
    if not animation_steps:
        st.warning("No animation steps detected in your code.")
        return code
    
    # Convert to DataFrame for easier manipulation
    df = pd.DataFrame(animation_steps)
    
    # Create an interactive Gantt chart with plotly
    st.markdown("#### Animation Timeline")
    st.markdown("Drag timeline elements to reorder or resize to change duration")
    
    # Create the Gantt chart
    fig = px.timeline(
        df, 
        x_start="start_time", 
        x_end=df["start_time"] + df["duration"],
        y="id",
        color="type",
        hover_name="animations",
        labels={"id": "Step", "start_time": "Time (seconds)"}
    )
    
    # Make it interactive
    fig.update_layout(
        height=400,
        xaxis=dict(
            title="Time (seconds)",
            rangeslider_visible=True
        )
    )
    
    # Add buttons and interactivity
    timeline_chart = st.plotly_chart(fig, use_container_width=True)
    
    # Control panel
    st.markdown("#### Timeline Controls")
    controls_col1, controls_col2, controls_col3 = st.columns(3)
    
    with controls_col1:
        selected_step = st.selectbox(
            "Select Step to Edit:",
            options=list(range(1, len(animation_steps) + 1)),
            format_func=lambda x: f"Step {x}"
        )
    
    with controls_col2:
        new_duration = st.number_input(
            "Duration (seconds):",
            min_value=0.1,
            max_value=10.0,
            value=float(df[df["id"] == selected_step]["duration"].values[0]),
            step=0.1
        )
    
    with controls_col3:
        step_action = st.selectbox(
            "Action:",
            options=["Update Duration", "Move Up", "Move Down", "Delete Step"]
        )
    
    apply_btn = st.button("Apply Change", key="apply_timeline_change")
    
    # Handle timeline modifications
    if apply_btn:
        modified = False
        
        if step_action == "Update Duration":
            # Update the duration of the selected step
            idx = df[df["id"] == selected_step].index[0]
            df.at[idx, "duration"] = new_duration
            modified = True
            
        elif step_action == "Move Up" and selected_step > 1:
            # Swap with the step above
            idx1 = df[df["id"] == selected_step].index[0]
            idx2 = df[df["id"] == selected_step - 1].index[0]
            
            # Swap IDs to maintain order
            df.at[idx1, "id"], df.at[idx2, "id"] = selected_step - 1, selected_step
            modified = True
            
        elif step_action == "Move Down" and selected_step < len(animation_steps):
            # Swap with the step below
            idx1 = df[df["id"] == selected_step].index[0]
            idx2 = df[df["id"] == selected_step + 1].index[0]
            
            # Swap IDs to maintain order
            df.at[idx1, "id"], df.at[idx2, "id"] = selected_step + 1, selected_step
            modified = True
            
        elif step_action == "Delete Step":
            # Remove the selected step
            df = df[df["id"] != selected_step]
            # Reindex remaining steps
            new_ids = list(range(1, len(df) + 1))
            df["id"] = new_ids
            modified = True
        
        if modified:
            # Recalculate start times
            df = df.sort_values("id")
            cumulative_time = 0
            for idx, row in df.iterrows():
                df.at[idx, "start_time"] = cumulative_time
                cumulative_time += row["duration"]
            
            # Regenerate animation code
            animation_steps = df.to_dict('records')
            new_code = generate_code_from_timeline(animation_steps, code)
            
            st.success("Timeline updated! Code has been regenerated.")
            return new_code
    
    # Visual keyframe editor
    st.markdown("#### Visual Keyframe Editor")
    st.markdown("Add keyframes for smooth property transitions")
    
    keyframe_obj = st.selectbox(
        "Select object to animate:",
        options=[f"Object {i+1}" for i in range(5)]  # Placeholder for actual objects
    )
    
    keyframe_prop = st.selectbox(
        "Select property:",
        options=["position", "scale", "rotation", "opacity", "color"]
    )
    
    # Keyframe timeline visualization
    keyframe_times = [0, 1, 2, 3, 4]  # Placeholder
    keyframe_values = [0, 0.5, 0.8, 0.2, 1.0]  # Placeholder
    
    keyframe_df = pd.DataFrame({
        "time": keyframe_times,
        "value": keyframe_values
    })
    
    keyframe_fig = px.line(
        keyframe_df, 
        x="time", 
        y="value",
        markers=True,
        title=f"{keyframe_prop.capitalize()} Keyframes"
    )
    
    keyframe_fig.update_layout(
        xaxis_title="Time (seconds)",
        yaxis_title="Value",
        height=250
    )
    
    st.plotly_chart(keyframe_fig, use_container_width=True)
    
    keyframe_col1, keyframe_col2, keyframe_col3 = st.columns(3)
    with keyframe_col1:
        keyframe_time = st.number_input("Time (s)", min_value=0.0, max_value=10.0, value=0.0, step=0.1)
    with keyframe_col2:
        keyframe_value = st.number_input("Value", min_value=0.0, max_value=1.0, value=0.0, step=0.1)
    with keyframe_col3:
        add_keyframe = st.button("Add Keyframe")
    
    # Return the original code or modified code
    return code

def export_to_educational_format(video_data, format_type, animation_title, explanation_text, temp_dir):
    """Export animation to various educational formats"""
    try:
        if format_type == "powerpoint":
            # Make sure python-pptx is installed
            try:
                import pptx
                from pptx.util import Inches
            except ImportError:
                logger.error("python-pptx not installed")
                subprocess.run([sys.executable, "-m", "pip", "install", "python-pptx"], check=True)
                import pptx
                from pptx.util import Inches
            
            # Create PowerPoint presentation
            prs = pptx.Presentation()
            
            # Title slide
            title_slide = prs.slides.add_slide(prs.slide_layouts[0])
            title_slide.shapes.title.text = animation_title
            title_slide.placeholders[1].text = "Created with Manim Animation Studio"
            
            # Video slide
            video_slide = prs.slides.add_slide(prs.slide_layouts[5])
            video_slide.shapes.title.text = "Animation"
            
            # Save video to temp file
            video_path = os.path.join(temp_dir, "animation.mp4")
            with open(video_path, "wb") as f:
                f.write(video_data)
            
            # Add video to slide
            try:
                left = Inches(1)
                top = Inches(1.5)
                width = Inches(8)
                height = Inches(4.5)
                video_slide.shapes.add_movie(video_path, left, top, width, height)
            except Exception as e:
                logger.error(f"Error adding video to PowerPoint: {str(e)}")
                # Fallback to adding a picture with link
                img_path = os.path.join(temp_dir, "thumbnail.png")
                # Generate thumbnail with ffmpeg
                subprocess.run([
                    "ffmpeg", "-i", video_path, "-ss", "00:00:01.000", 
                    "-vframes", "1", img_path
                ], check=True)
                
                if os.path.exists(img_path):
                    pic = video_slide.shapes.add_picture(img_path, left, top, width, height)
                    video_slide.shapes.add_textbox(left, top + height + Inches(0.5), width, Inches(0.5)).text_frame.text = "Click to play video (exported separately)"
            
            # Explanation slide
            if explanation_text:
                text_slide = prs.slides.add_slide(prs.slide_layouts[1])
                text_slide.shapes.title.text = "Explanation"
                text_slide.placeholders[1].text = explanation_text
            
            # Save presentation
            output_path = os.path.join(temp_dir, f"{animation_title.replace(' ', '_')}.pptx")
            prs.save(output_path)
            
            # Read the file to return it
            with open(output_path, "rb") as f:
                return f.read(), "powerpoint"
                
        elif format_type == "html":
            # Create interactive HTML animation
            html_template = """
            <!DOCTYPE html>
            <html>
            <head>
                <title>{title}</title>
                <style>
                    body {{ font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }}
                    .animation-container {{ margin: 20px 0; }}
                    .controls {{ display: flex; margin: 10px 0; }}
                    .controls button {{ margin-right: 10px; padding: 5px 10px; }}
                    .explanation {{ margin-top: 20px; padding: 15px; background: #f5f5f5; border-radius: 5px; }}
                </style>
                <script>
                    document.addEventListener('DOMContentLoaded', function() {{
                        const video = document.getElementById('animation');
                        const playBtn = document.getElementById('play');
                        const pauseBtn = document.getElementById('pause');
                        const restartBtn = document.getElementById('restart');
                        const slowBtn = document.getElementById('slow');
                        const normalBtn = document.getElementById('normal');
                        const fastBtn = document.getElementById('fast');
                        
                        playBtn.addEventListener('click', function() {{ video.play(); }});
                        pauseBtn.addEventListener('click', function() {{ video.pause(); }});
                        restartBtn.addEventListener('click', function() {{ video.currentTime = 0; video.play(); }});
                        slowBtn.addEventListener('click', function() {{ video.playbackRate = 0.5; }});
                        normalBtn.addEventListener('click', function() {{ video.playbackRate = 1.0; }});
                        fastBtn.addEventListener('click', function() {{ video.playbackRate = 2.0; }});
                    }});
                </script>
            </head>
            <body>
                <h1>{title}</h1>
                
                <div class="animation-container">
                    <video id="animation" width="100%" controls>
                        <source src="data:video/mp4;base64,{video_base64}" type="video/mp4">
                        Your browser does not support the video tag.
                    </video>
                    
                    <div class="controls">
                        <button id="play">Play</button>
                        <button id="pause">Pause</button>
                        <button id="restart">Restart</button>
                        <button id="slow">0.5x Speed</button>
                        <button id="normal">1x Speed</button>
                        <button id="fast">2x Speed</button>
                    </div>
                </div>
                
                <div class="explanation">
                    <h2>Explanation</h2>
                    {explanation_html}
                </div>
                
                <footer>
                    <p>Created with Manim Animation Studio</p>
                </footer>
            </body>
            </html>
            """
            
            # Convert video data to base64
            video_base64 = base64.b64encode(video_data).decode('utf-8')
            
            # Convert markdown explanation to HTML
            explanation_html = markdown.markdown(explanation_text) if explanation_text else "<p>No explanation provided.</p>"
            
            # Format the HTML template
            html_content = html_template.format(
                title=animation_title,
                video_base64=video_base64,
                explanation_html=explanation_html
            )
            
            # Save to file
            output_path = os.path.join(temp_dir, f"{animation_title.replace(' ', '_')}.html")
            with open(output_path, "w", encoding="utf-8") as f:
                f.write(html_content)
                
            # Read the file to return it
            with open(output_path, "rb") as f:
                return f.read(), "html"
                
        elif format_type == "sequence":
            # Generate animation sequence with explanatory text
            # Make sure FPDF is installed
            try:
                from fpdf import FPDF
            except ImportError:
                logger.error("fpdf not installed")
                subprocess.run([sys.executable, "-m", "pip", "install", "fpdf"], check=True)
                from fpdf import FPDF
            
            # Save video temporarily
            temp_video_path = os.path.join(temp_dir, "temp_video.mp4")
            with open(temp_video_path, "wb") as f:
                f.write(video_data)
            
            # Create frames directory
            frames_dir = os.path.join(temp_dir, "frames")
            os.makedirs(frames_dir, exist_ok=True)
            
            # Extract frames using ffmpeg (assuming it's installed)
            frame_count = 5  # Number of key frames to extract
            try:
                subprocess.run([
                    "ffmpeg", 
                    "-i", temp_video_path, 
                    "-vf", f"select=eq(n\\,0)+eq(n\\,{frame_count//4})+eq(n\\,{frame_count//2})+eq(n\\,{frame_count*3//4})+eq(n\\,{frame_count-1})",
                    "-vsync", "0", 
                    os.path.join(frames_dir, "frame_%03d.png")
                ], check=True)
            except Exception as e:
                logger.error(f"Error extracting frames: {str(e)}")
                # Try a simpler approach
                subprocess.run([
                    "ffmpeg",
                    "-i", temp_video_path,
                    "-r", "1",  # 1 frame per second
                    os.path.join(frames_dir, "frame_%03d.png")
                ], check=True)
            
            # Parse explanation text into segments (assuming sections divided by ##)
            explanation_segments = explanation_text.split("##") if explanation_text else ["No explanation provided."]
            
            # Create a PDF with frames and explanations
            pdf = FPDF()
            pdf.set_auto_page_break(auto=True, margin=15)
            
            # Title page
            pdf.add_page()
            pdf.set_font("Arial", "B", 20)
            pdf.cell(190, 10, animation_title, ln=True, align="C")
            pdf.ln(10)
            pdf.set_font("Arial", "", 12)
            pdf.cell(190, 10, "Animation Sequence with Explanations", ln=True, align="C")
            
            # Add each frame with explanation
            frame_files = sorted([f for f in os.listdir(frames_dir) if f.endswith('.png')])
            
            for i, frame_file in enumerate(frame_files):
                pdf.add_page()
                
                # Add frame image
                frame_path = os.path.join(frames_dir, frame_file)
                pdf.image(frame_path, x=10, y=10, w=190)
                
                # Add explanation text
                pdf.ln(140)  # Move below the image
                pdf.set_font("Arial", "B", 12)
                pdf.cell(190, 10, f"Step {i+1}", ln=True)
                pdf.set_font("Arial", "", 10)
                
                # Use the corresponding explanation segment if available
                explanation = explanation_segments[min(i, len(explanation_segments)-1)]
                pdf.multi_cell(190, 5, explanation.strip())
            
            # Save PDF
            output_path = os.path.join(temp_dir, f"{animation_title.replace(' ', '_')}_sequence.pdf")
            pdf.output(output_path)
            
            # Read the file to return it
            with open(output_path, "rb") as f:
                return f.read(), "pdf"
                
        return None, None
                
    except Exception as e:
        logger.error(f"Educational export error: {str(e)}")
        import traceback
        logger.error(traceback.format_exc())
        return None, None

def main():
    # Initialize session state variables if they don't exist
    if 'init' not in st.session_state:
        st.session_state.init = True
        st.session_state.video_data = None
        st.session_state.status = None
        st.session_state.ai_models = None
        st.session_state.generated_code = ""
        st.session_state.code = ""
        st.session_state.temp_code = ""
        st.session_state.editor_key = str(uuid.uuid4())
        st.session_state.packages_checked = False  # Track if packages were already checked
        st.session_state.audio_path = None
        st.session_state.image_paths = []
        st.session_state.custom_library_result = ""
        st.session_state.python_script = "import matplotlib.pyplot as plt\nimport numpy as np\n\n# Example: Create a simple plot\nx = np.linspace(0, 10, 100)\ny = np.sin(x)\n\nplt.figure(figsize=(10, 6))\nplt.plot(x, y, 'b-', label='sin(x)')\nplt.title('Sine Wave')\nplt.xlabel('x')\nplt.ylabel('sin(x)')\nplt.grid(True)\nplt.legend()\n"
        st.session_state.python_result = None
        st.session_state.active_tab = 0  # Track currently active tab
        st.session_state.settings = {
            "quality": "720p",
            "format_type": "mp4",
            "animation_speed": "Normal",
            "fps": 30  # Default FPS
        }
        st.session_state.password_entered = False  # Track password authentication
        st.session_state.custom_model = "gpt-4o"  # Default model
        st.session_state.first_load_complete = False  # Prevent refreshes on first load
        st.session_state.pending_tab_switch = None  # Track pending tab switches
        # C++ runner state
        st.session_state.cpp_code = """#include <iostream>
#include <vector>
#include <algorithm>

int main() {
    std::cout << "Hello, Manim Animation Studio!" << std::endl;
    
    // Create a vector of numbers
    std::vector<int> numbers = {5, 2, 8, 1, 9, 3, 7, 4, 6};
    
    // Sort the vector
    std::sort(numbers.begin(), numbers.end());
    
    // Print the sorted numbers
    std::cout << "Sorted numbers: ";
    for (int num : numbers) {
        std::cout << num << " ";
    }
    std::cout << std::endl;
    
    return 0;
}"""
        st.session_state.cpp_result = None
        st.session_state.cpp_project_files = {"main.cpp": st.session_state.cpp_code}
        st.session_state.cpp_settings = {
            "compiler": "g++",
            "std": "c++17",
            "optimization": "-O2",
            "include_paths": [],
            "library_paths": [],
            "libraries": []
        }

    # Page configuration with improved layout
    st.set_page_config(
        page_title="Manim Animation Studio",
        page_icon="🎬",
        layout="wide",
        initial_sidebar_state="expanded"
    )

    # Custom CSS for improved UI
    st.markdown("""
    <style>
    .main-header {
        font-size: 2.5rem;
        font-weight: 700;
        background: linear-gradient(90deg, #4F46E5, #818CF8);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        margin-bottom: 1rem;
        text-align: center;
    }
    /* Improved Cards */
    .card {
        background-color: #ffffff;
        border-radius: 12px;
        padding: 1.8rem;
        box-shadow: 0 6px 12px rgba(0, 0, 0, 0.08);
        margin-bottom: 1.8rem;
        border-left: 5px solid #4F46E5;
        transition: all 0.3s ease;
    }
    .card:hover {
        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.12);
        transform: translateY(-2px);
    }
    /* Tab styling */
    .stTabs [data-baseweb="tab-list"] {
        gap: 2px;
    }
    .stTabs [data-baseweb="tab"] {
        height: 45px;
        white-space: pre-wrap;
        border-radius: 4px 4px 0 0;
        font-weight: 500;
    }
    .stTabs [aria-selected="true"] {
        background-color: #f0f4fd;
        border-bottom: 2px solid #4F46E5;
    }
    /* Buttons */
    .stButton button {
        border-radius: 6px;
        font-weight: 500;
        transition: all 0.2s ease;
    }
    .stButton button:hover {
        transform: translateY(-1px);
        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    }
    /* Model selection */
    .model-group {
        margin-bottom: 1.5rem;
        padding: 15px;
        border-radius: 8px;
        background-color: #f8f9fa;
    }
    .model-card {
        background-color: #f8f9fa;
        border-radius: 10px;
        padding: 15px;
        margin-bottom: 10px;
        border-left: 4px solid #4F46E5;
        transition: all 0.3s ease;
    }
    .model-card:hover {
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
        transform: translateY(-2px);
    }
    .model-category {
        font-size: 1.2rem;
        font-weight: 600;
        padding: 10px 5px;
        margin-top: 15px;
        border-bottom: 2px solid #e9ecef;
        color: #333;
    }
    .model-details {
        font-size: 0.8rem;
        color: #666;
        margin-top: 5px;
    }
    .selected-model {
        background-color: #e8f4fe;
        border-left: 4px solid #0d6efd;
    }
    .preview-container {
        border: 1px solid #e0e0e0;
        border-radius: 10px;
        padding: 1rem;
        margin-bottom: 1rem;
        min-height: 200px;
    }
    .small-text {
        font-size: 0.8rem;
        color: #6c757d;
    }
    .asset-card {
        background-color: #f0f2f5;
        border-radius: 8px;
        padding: 1rem;
        margin-bottom: 1rem;
        border-left: 4px solid #4F46E5;
    }
    .timeline-container {
        background-color: #f8f9fa;
        border-radius: 10px;
        padding: 1.5rem;
        margin-bottom: 1.5rem;
    }
    .keyframe {
        width: 12px;
        height: 12px;
        border-radius: 50%;
        background-color: #4F46E5;
        position: absolute;
        transform: translate(-50%, -50%);
        cursor: pointer;
    }
    .educational-export-container {
        background-color: #f0f7ff;
        border-radius: 10px;
        padding: 1.5rem;
        margin-bottom: 1.5rem;
        border: 1px solid #c2e0ff;
    }
    .code-output {
        background-color: #f8f9fa;
        border-radius: 8px;
        padding: 1rem;
        margin-top: 1rem;
        border-left: 4px solid #10b981;
        max-height: 400px;
        overflow-y: auto;
    }
    .error-output {
        background-color: #fef2f2;
        border-radius: 8px;
        padding: 1rem;
        margin-top: 1rem;
        border-left: 4px solid #ef4444;
    }
    </style>
    """, unsafe_allow_html=True)

    # Header
    st.markdown("""
    <div class="main-header">
        🎬 Manim Animation Studio
    </div>
    <p style="text-align: center; margin-bottom: 2rem;">Create mathematical animations with Manim</p>
    """, unsafe_allow_html=True)

    # Check for packages ONLY ONCE per session
    if not st.session_state.packages_checked:
        if ensure_packages():
            st.session_state.packages_checked = True
        else:
            st.error("Failed to install required packages. Please try again.")
            st.stop()
    
    # Create main tabs
    tab_names = ["✨ Editor", "🤖 AI Assistant", "🎨 Assets", "🎞️ Timeline", "🎓 Educational Export", "🐍 Python Runner", "🔧 C/C++ Runner"]
    tabs = st.tabs(tab_names)
    
    # Sidebar for rendering settings and custom libraries
    with st.sidebar:
        # Rendering settings section
        st.markdown("## ⚙️ Rendering Settings")
        
        col1, col2 = st.columns(2)
        with col1:
            quality = st.selectbox(
                "🎯 Quality",
                options=list(QUALITY_PRESETS.keys()),
                index=list(QUALITY_PRESETS.keys()).index(st.session_state.settings["quality"]),
                key="quality_select"
            )
        
        with col2:
            format_type_display = st.selectbox(
                "📦 Format",
                options=list(EXPORT_FORMATS.keys()),
                index=list(EXPORT_FORMATS.values()).index(st.session_state.settings["format_type"]) 
                      if st.session_state.settings["format_type"] in EXPORT_FORMATS.values() else 0,
                key="format_select_display"
            )
            # Convert display name to actual format value
            format_type = EXPORT_FORMATS[format_type_display]
        
        # Add FPS control
        fps = st.selectbox(
            "🎞️ FPS",
            options=FPS_OPTIONS,
            index=FPS_OPTIONS.index(st.session_state.settings["fps"]) if st.session_state.settings["fps"] in FPS_OPTIONS else 2,  # Default to 30 FPS (index 2)
            key="fps_select"
        )
        
        animation_speed = st.selectbox(
            "⚡ Speed",
            options=list(ANIMATION_SPEEDS.keys()),
            index=list(ANIMATION_SPEEDS.keys()).index(st.session_state.settings["animation_speed"]),
            key="speed_select"
        )
        
        # Apply the settings without requiring a button
        st.session_state.settings = {
            "quality": quality,
            "format_type": format_type,
            "animation_speed": animation_speed,
            "fps": fps
        }
        
        # Custom libraries section
        st.markdown("## 📚 Custom Libraries")
        st.markdown("Enter additional Python packages needed for your animations (comma-separated):")
        
        custom_libraries = st.text_area(
            "Libraries to install",
            placeholder="e.g., scipy, networkx, matplotlib",
            key="custom_libraries"
        )
        
        if st.button("Install Libraries", key="install_libraries_btn"):
            success, result = install_custom_packages(custom_libraries)
            st.session_state.custom_library_result = result
            
            if success:
                st.success("Installation complete!")
            else:
                st.error("Installation failed for some packages.")
        
        if st.session_state.custom_library_result:
            with st.expander("Installation Results"):
                st.code(st.session_state.custom_library_result)
                
        # C/C++ Library Options
        with st.sidebar.expander("C/C++ Library Options"):
            st.markdown("### Advanced C/C++ Settings")
            
            cpp_libs = st.multiselect(
                "Include Libraries",
                options=["Eigen", "Boost", "OpenCV", "FFTW", "Matplotlib-cpp"],
                default=st.session_state.cpp_settings.get("libraries", [])
            )
            
            st.session_state.cpp_settings["libraries"] = cpp_libs
            
            custom_include = st.text_input("Custom Include Path:")
            custom_lib = st.text_input("Custom Library Path:")
            
            if custom_include and custom_include not in st.session_state.cpp_settings.get("include_paths", []):
                if "include_paths" not in st.session_state.cpp_settings:
                    st.session_state.cpp_settings["include_paths"] = []
                st.session_state.cpp_settings["include_paths"].append(custom_include)
                
            if custom_lib and custom_lib not in st.session_state.cpp_settings.get("library_paths", []):
                if "library_paths" not in st.session_state.cpp_settings:
                    st.session_state.cpp_settings["library_paths"] = []
                st.session_state.cpp_settings["library_paths"].append(custom_lib)
            
            if st.button("Update Library Settings"):
                st.success("Library settings updated!")

    # EDITOR TAB
    with tabs[0]:
        col1, col2 = st.columns([3, 2])
        
        with col1:
            st.markdown("### 📝 Animation Editor")
            
            # Toggle between upload and type
            editor_mode = st.radio(
                "Choose how to input your code:",
                ["Type Code", "Upload File"],
                key="editor_mode"
            )
            
            if editor_mode == "Upload File":
                uploaded_file = st.file_uploader("Upload Manim Python File", type=["py"], key="code_uploader")
                if uploaded_file:
                    code_content = uploaded_file.getvalue().decode("utf-8")
                    if code_content.strip():  # Only update if file has content
                        st.session_state.code = code_content
                        st.session_state.temp_code = code_content
            
            # Code editor
            if ACE_EDITOR_AVAILABLE:
                current_code = st.session_state.code if hasattr(st.session_state, 'code') and st.session_state.code else ""
                st.session_state.temp_code = st_ace(
                    value=current_code,
                    language="python",
                    theme="monokai",
                    min_lines=20,
                    key=f"ace_editor_{st.session_state.editor_key}"
                )
            else:
                current_code = st.session_state.code if hasattr(st.session_state, 'code') and st.session_state.code else ""
                st.session_state.temp_code = st.text_area(
                    "Manim Python Code",
                    value=current_code,
                    height=400,
                    key=f"code_textarea_{st.session_state.editor_key}"
                )
            
            # Update code in session state if it changed
            if st.session_state.temp_code != st.session_state.code:
                st.session_state.code = st.session_state.temp_code
            
            # Generate button (use a form to prevent page reloads)
            generate_btn = st.button("🚀 Generate Animation", use_container_width=True, key="generate_btn")
            if generate_btn:
                if not st.session_state.code:
                    st.error("Please enter some code before generating animation")
                else:
                    # Extract scene class name
                    scene_class = extract_scene_class_name(st.session_state.code)
                    
                    # If no valid scene class found, add a basic one
                    if scene_class == "MyScene" and "class MyScene" not in st.session_state.code:
                        default_scene = """
class MyScene(Scene):
    def construct(self):
        text = Text("Default Scene")
        self.play(Write(text))
        self.wait(2)
"""
                        st.session_state.code += default_scene
                        st.session_state.temp_code = st.session_state.code
                        st.warning("No scene class found. Added a default scene.")
                        
                    with st.spinner("Generating animation..."):
                        video_data, status = generate_manim_video(
                            st.session_state.code,
                            st.session_state.settings["format_type"],
                            st.session_state.settings["quality"],
                            ANIMATION_SPEEDS[st.session_state.settings["animation_speed"]],
                            st.session_state.audio_path,
                            st.session_state.settings["fps"]
                        )
                        st.session_state.video_data = video_data
                        st.session_state.status = status
        
        with col2:
            st.markdown("### 🖥️ Preview & Output")
            
            # Preview container
            if st.session_state.code:
                with st.container():
                    st.markdown("<div class='preview-container'>", unsafe_allow_html=True)
                    preview_html = generate_manim_preview(st.session_state.code)
                    components.html(preview_html, height=250)
                    st.markdown("</div>", unsafe_allow_html=True)
            
            # Generated output display
            if st.session_state.video_data:
                # Different handling based on format type
                format_type = st.session_state.settings["format_type"]
                
                if format_type == "png_sequence":
                    st.info("PNG sequence generated successfully. Use the download button to get the ZIP file.")
                    
                    # Add download button for ZIP
                    st.download_button(
                        label="⬇️ Download PNG Sequence (ZIP)",
                        data=st.session_state.video_data,
                        file_name=f"manim_pngs_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip",
                        mime="application/zip",
                        use_container_width=True
                    )
                elif format_type == "svg":
                    # Display SVG preview
                    try:
                        svg_data = st.session_state.video_data.decode('utf-8')
                        components.html(svg_data, height=400)
                    except Exception as e:
                        st.error(f"Error displaying SVG: {str(e)}")
                    
                    # Download button for SVG
                    st.download_button(
                        label="⬇️ Download SVG",
                        data=st.session_state.video_data,
                        file_name=f"manim_animation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.svg",
                        mime="image/svg+xml",
                        use_container_width=True
                    )
                else:
                    # Standard video display for MP4, GIF, WebM
                    try:
                        st.video(st.session_state.video_data, format=format_type)
                    except Exception as e:
                        st.error(f"Error displaying video: {str(e)}")
                        # Fallback for GIF if st.video fails
                        if format_type == "gif":
                            st.markdown("GIF preview:")
                            gif_b64 = base64.b64encode(st.session_state.video_data).decode()
                            st.markdown(f'<img src="data:image/gif;base64,{gif_b64}" alt="animation" style="width:100%">', unsafe_allow_html=True)
                    
                    # Add download button
                    st.download_button(
                        label=f"⬇️ Download {format_type.upper()}",
                        data=st.session_state.video_data,
                        file_name=f"manim_animation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.{format_type}",
                        mime=f"{'image' if format_type == 'gif' else 'video'}/{format_type}",
                        use_container_width=True
                    )
            
            if st.session_state.status:
                if "Error" in st.session_state.status:
                    st.error(st.session_state.status)
                    
                    # Show troubleshooting tips
                    with st.expander("🔍 Troubleshooting Tips"):
                        st.markdown("""
                        ### Common Issues:
                        1. **Syntax Errors**: Check your Python code for any syntax issues
                        2. **Missing Scene Class**: Ensure your code contains a scene class that extends Scene
                        3. **High Resolution Issues**: Try a lower quality preset for complex animations
                        4. **Memory Issues**: For 4K animations, reduce complexity or try again
                        5. **Format Issues**: Some formats require specific Manim configurations
                        6. **GIF Generation**: If GIF doesn't work, try MP4 and we'll convert it automatically
                        
                        ### Example Code:
                        ```python
                        from manim import *
                        
                        class MyScene(Scene):
                            def construct(self):
                                circle = Circle(color=RED)
                                self.play(Create(circle))
                                self.wait(1)
                        ```
                        """)
                else:
                    st.success(st.session_state.status)

    # AI ASSISTANT TAB
    with tabs[1]:
        st.markdown("### 🤖 AI Animation Assistant")
        
        # Check password before allowing access
        if check_password():
            # Debug section
            with st.expander("🔧 Debug Connection"):
                st.markdown("Test the AI model connection directly")
                
                if st.button("Test API Connection", key="test_api_btn"):
                    with st.spinner("Testing API connection..."):
                        try:
                            # Get token from secrets
                            token = get_secret("github_token_api")
                            if not token:
                                st.error("GitHub token not found in secrets")
                                st.stop()
                            
                            # Get model details
                            model_name = st.session_state.custom_model
                            config = MODEL_CONFIGS.get(model_name, MODEL_CONFIGS["default"])
                            category = config.get("category", "Other")
                            
                            if category == "OpenAI":
                                # Use OpenAI client for GitHub AI models
                                try:
                                    from openai import OpenAI
                                except ImportError:
                                    st.error("OpenAI package not installed. Please run 'pip install openai'")
                                    st.stop()
                                
                                # Create OpenAI client with GitHub AI endpoint
                                client = OpenAI(
                                    base_url="https://models.github.ai/inference",
                                    api_key=token,
                                )
                                
                                # For GitHub AI models, ensure the model_name includes the publisher
                                # If it doesn't have a publisher prefix, add "openai/"
                                if "/" not in model_name:
                                    full_model_name = f"openai/{model_name}"
                                    st.info(f"Using full model name: {full_model_name}")
                                else:
                                    full_model_name = model_name
                                
                                # Prepare parameters based on model configuration
                                params = {
                                    "messages": [
                                        {"role": "system", "content": "You are a helpful assistant."},
                                        {"role": "user", "content": "Hello, this is a connection test."}
                                    ],
                                    "model": full_model_name
                                }
                                
                                # Add appropriate token parameter
                                token_param = config["param_name"]
                                params[token_param] = config[token_param]
                                
                                # Make API call
                                response = client.chat.completions.create(**params)
                                
                                # Check if response is valid
                                if response and response.choices and len(response.choices) > 0:
                                    test_response = response.choices[0].message.content
                                    st.success(f"✅ Connection successful! Response: {test_response[:50]}...")
                                    
                                    # Save working connection to session state
                                    st.session_state.ai_models = {
                                        "openai_client": client,
                                        "model_name": full_model_name,  # Store the full model name
                                        "endpoint": "https://models.github.ai/inference",
                                        "last_loaded": datetime.now().isoformat(),
                                        "category": category
                                    }
                                else:
                                    st.error("❌ API returned an empty response")
                            
                            elif category == "Azure" or category in ["DeepSeek", "Meta", "Microsoft", "Mistral", "Other"]:
                                # Use Azure client for Azure API models
                                try:
                                    from azure.ai.inference import ChatCompletionsClient
                                    from azure.ai.inference.models import SystemMessage, UserMessage
                                    from azure.core.credentials import AzureKeyCredential
                                except ImportError:
                                    st.error("Azure AI packages not installed. Please run 'pip install azure-ai-inference azure-core'")
                                    st.stop()
                                
                                # Define endpoint
                                endpoint = "https://models.inference.ai.azure.com"
                                
                                # Prepare API parameters
                                messages = [UserMessage("Hello, this is a connection test.")]
                                api_params, config = prepare_api_params(messages, model_name)
                                
                                # Create client with appropriate API version
                                api_version = config.get("api_version")
                                if api_version:
                                    client = ChatCompletionsClient(
                                        endpoint=endpoint,
                                        credential=AzureKeyCredential(token),
                                        api_version=api_version
                                    )
                                else:
                                    client = ChatCompletionsClient(
                                        endpoint=endpoint,
                                        credential=AzureKeyCredential(token),
                                    )
                                
                                # Test with the prepared parameters
                                response = client.complete(**api_params)
                                
                                # Check if response is valid
                                if response and response.choices and len(response.choices) > 0:
                                    test_response = response.choices[0].message.content
                                    st.success(f"✅ Connection successful! Response: {test_response[:50]}...")
                                    
                                    # Save working connection to session state
                                    st.session_state.ai_models = {
                                        "client": client,
                                        "model_name": model_name,
                                        "endpoint": endpoint,
                                        "last_loaded": datetime.now().isoformat(),
                                        "category": category,
                                        "api_version": api_version
                                    }
                                else:
                                    st.error("❌ API returned an empty response")
                            
                            else:
                                st.error(f"Unsupported model category: {category}")
                        
                        except ImportError as ie:
                            st.error(f"Module import error: {str(ie)}")
                            st.info("Try installing required packages: openai, azure-ai-inference and azure-core")
                        except Exception as e:
                            st.error(f"❌ API test failed: {str(e)}")
                            import traceback
                            st.code(traceback.format_exc())
                            
            # Model selection with enhanced UI
            st.markdown("### 🤖 Model Selection")
            st.markdown("Select an AI model for generating animation code:")

            # Group models by category for better organization
            model_categories = {}
            for model_name in MODEL_CONFIGS:
                if model_name != "default":
                    category = MODEL_CONFIGS[model_name].get("category", "Other")
                    if category not in model_categories:
                        model_categories[category] = []
                    model_categories[category].append(model_name)

            # Create tabbed interface for model categories
            category_tabs = st.tabs(sorted(model_categories.keys()))

            for i, category in enumerate(sorted(model_categories.keys())):
                with category_tabs[i]:
                    for model_name in sorted(model_categories[category]):
                        config = MODEL_CONFIGS[model_name]
                        is_selected = model_name == st.session_state.custom_model
                        warning = config.get("warning")
                        
                        # Create styled card for each model
                        warning_html = f'<p style="color: #ff9800; font-size: 0.8rem; margin-top: 5px;">⚠️ {warning}</p>' if warning else ""
                        
                        st.markdown(f"""
                        <div class="model-card {'selected-model' if is_selected else ''}">
                            <h4>{model_name}</h4>
                            <div class="model-details">
                                <p>Max Tokens: {config.get(config['param_name'], 'Unknown')}</p>
                                <p>Category: {config['category']}</p>
                                <p>API Version: {config['api_version'] if config['api_version'] else 'Default'}</p>
                                {warning_html}
                            </div>
                        </div>
                    """, unsafe_allow_html=True)
                        
                        # Button to select this model
                        button_label = "Selected ✓" if is_selected else "Select Model"
                        if st.button(button_label, key=f"model_{model_name}", disabled=is_selected):
                            st.session_state.custom_model = model_name
                            if st.session_state.ai_models and 'model_name' in st.session_state.ai_models:
                                st.session_state.ai_models['model_name'] = model_name
                            st.rerun()

            # Display current model selection
            st.info(f"🤖 **Currently using: {st.session_state.custom_model}**")

            # Add a refresh button to update model connection
            if st.button("🔄 Refresh Model Connection", key="refresh_model_connection"):
                if st.session_state.ai_models and 'client' in st.session_state.ai_models:
                    try:
                        # Test connection with minimal prompt
                        from azure.ai.inference.models import UserMessage
                        model_name = st.session_state.custom_model
                        
                        # Prepare parameters
                        messages = [UserMessage("Hello")]
                        api_params, config = prepare_api_params(messages, model_name)
                        
                        # Check if we need a new client with specific API version
                        if config["api_version"] and config["api_version"] != st.session_state.ai_models.get("api_version"):
                            # Create version-specific client if needed
                            token = get_secret("github_token_api")
                            from azure.ai.inference import ChatCompletionsClient
                            from azure.core.credentials import AzureKeyCredential
                            
                            client = ChatCompletionsClient(
                                endpoint=st.session_state.ai_models["endpoint"],
                                credential=AzureKeyCredential(token),
                                api_version=config["api_version"]
                            )
                            response = client.complete(**api_params)
                            
                            # Update session state with the new client
                            st.session_state.ai_models["client"] = client
                            st.session_state.ai_models["api_version"] = config["api_version"]
                        else:
                            response = st.session_state.ai_models["client"].complete(**api_params)
                            
                        st.success(f"✅ Connection to {model_name} successful!")
                        st.session_state.ai_models["model_name"] = model_name
                        
                    except Exception as e:
                        st.error(f"❌ Connection error: {str(e)}")
                        st.info("Please try the Debug Connection section to re-initialize the API connection.")
            
            # AI code generation
            if st.session_state.ai_models and "client" in st.session_state.ai_models:
                st.markdown("<div class='card'>", unsafe_allow_html=True)
                st.markdown("#### Generate Animation from Description")
                st.write("Describe the animation you want to create, or provide partial code to complete.")
                
                # Predefined animation ideas dropdown
                animation_ideas = [
                    "Select an idea...",
                    "Create a 3D animation showing a sphere morphing into a torus",
                    "Show a visual proof of the Pythagorean theorem",
                    "Visualize a Fourier transform converting a signal from time domain to frequency domain",
                    "Create an animation explaining neural network forward propagation",
                    "Illustrate the concept of integration with area under a curve"
                ]
                
                selected_idea = st.selectbox(
                    "Try one of these ideas", 
                    options=animation_ideas
                )
                
                prompt_value = selected_idea if selected_idea != "Select an idea..." else ""
                
                code_input = st.text_area(
                    "Your Prompt or Code",
                    value=prompt_value,
                    placeholder="Example: Create an animation that shows a circle morphing into a square while changing color from red to blue",
                    height=150
                )
                
                if st.button("Generate Animation Code", key="gen_ai_code"):
                    if code_input:
                        with st.spinner("AI is generating your animation code..."):
                            try:
                                # Get the client and model name
                                client = st.session_state.ai_models["client"]
                                model_name = st.session_state.ai_models["model_name"]
                                
                                # Create the prompt
                                prompt = f"""Write a complete Manim animation scene based on this code or idea:
                {code_input}
                
                The code should be a complete, working Manim animation that includes:
                - Proper Scene class definition
                - Constructor with animations
                - Proper use of self.play() for animations
                - Proper wait times between animations
                
                Here's the complete Manim code:
                """
                                
                                # Prepare API parameters
                                from azure.ai.inference.models import UserMessage
                                messages = [UserMessage(prompt)]
                                api_params, config = prepare_api_params(messages, model_name)
                                
                                # Make the API call with proper parameters
                                response = client.complete(**api_params)
                                
                                # Process the response
                                if response and response.choices and len(response.choices) > 0:
                                    completed_code = response.choices[0].message.content
                                    
                                    # Extract code from markdown if present
                                    if "```python" in completed_code:
                                        completed_code = completed_code.split("```python")[1].split("```")[0]
                                    elif "```" in completed_code:
                                        completed_code = completed_code.split("```")[1].split("```")[0]
                                    
                                    # Add Scene class if missing
                                    if "Scene" not in completed_code:
                                        completed_code = f"""from manim import *
                
class MyScene(Scene):
    def construct(self):
                        {completed_code}"""
                                    
                                    # Store the generated code
                                    st.session_state.generated_code = completed_code
                                else:
                                    st.error("Failed to generate code. API returned an empty response.")
                            except Exception as e:
                                st.error(f"Error generating code: {str(e)}")
                                import traceback
                                st.code(traceback.format_exc())
                    else:
                        st.warning("Please enter a description or prompt first")
                        
                
                # AI generated code display and actions
                if "generated_code" in st.session_state and st.session_state.generated_code:
                    st.markdown("<div class='card'>", unsafe_allow_html=True)
                    st.markdown("#### Generated Animation Code")
                    st.code(st.session_state.generated_code, language="python")
                    
                    col_ai1, col_ai2 = st.columns(2)
                    with col_ai1:
                        if st.button("Use This Code", key="use_gen_code"):
                            st.session_state.code = st.session_state.generated_code
                            st.session_state.temp_code = st.session_state.generated_code
                            # Set pending tab switch to editor tab
                            st.session_state.pending_tab_switch = 0
                            st.rerun()
                    
                    with col_ai2:
                        if st.button("Render Preview", key="render_preview"):
                            with st.spinner("Rendering preview..."):
                                video_data, status = generate_manim_video(
                                    st.session_state.generated_code, 
                                    "mp4", 
                                    "480p",  # Use lowest quality for preview
                                    ANIMATION_SPEEDS["Normal"],
                                    fps=st.session_state.settings["fps"]
                                )
                                
                                if video_data:
                                    st.video(video_data)
                                    st.download_button(
                                        label="Download Preview",
                                        data=video_data,
                                        file_name=f"manim_preview_{int(time.time())}.mp4",
                                        mime="video/mp4"
                                    )
                                else:
                                    st.error(f"Failed to generate preview: {status}")
                    st.markdown("</div>", unsafe_allow_html=True)
            else:
                st.warning("AI models not initialized. Please use the Debug Connection section to test API connectivity.")
        else:
            st.info("Please enter the correct password to access AI features")

    # ASSETS TAB
    with tabs[2]:
        st.markdown("### 🎨 Asset Management")
        
        asset_col1, asset_col2 = st.columns([1, 1])
        
        with asset_col1:
            # Image uploader section
            st.markdown("#### 📸 Image Assets")
            st.markdown("Upload images to use in your animations:")
            
            # Allow multiple image uploads
            uploaded_images = st.file_uploader(
                "Upload Images", 
                type=["jpg", "png", "jpeg", "svg"], 
                accept_multiple_files=True,
                key="image_uploader_tab"
            )
            
            if uploaded_images:
                # Create a unique image directory if it doesn't exist
                image_dir = os.path.join(os.getcwd(), "manim_assets", "images")
                os.makedirs(image_dir, exist_ok=True)
                
                # Process each uploaded image
                for uploaded_image in uploaded_images:
                    # Generate a unique filename and save the image
                    file_extension = uploaded_image.name.split(".")[-1]
                    unique_filename = f"image_{int(time.time())}_{uuid.uuid4().hex[:8]}.{file_extension}"
                    image_path = os.path.join(image_dir, unique_filename)
                    
                    with open(image_path, "wb") as f:
                        f.write(uploaded_image.getvalue())
                    
                    # Store the path in session state
                    if "image_paths" not in st.session_state:
                        st.session_state.image_paths = []
                    
                    # Check if this image was already added
                    image_already_added = False
                    for img in st.session_state.image_paths:
                        if img["name"] == uploaded_image.name:
                            image_already_added = True
                            break
                    
                    if not image_already_added:
                        st.session_state.image_paths.append({
                            "name": uploaded_image.name, 
                            "path": image_path
                        })
                
                # Display uploaded images in a grid
                st.markdown("##### Uploaded Images:")
                image_cols = st.columns(3)
                
                for i, img_info in enumerate(st.session_state.image_paths[-len(uploaded_images):]):
                    with image_cols[i % 3]:
                        try:
                            img = Image.open(img_info["path"])
                            st.image(img, caption=img_info["name"], width=150)
                            
                            # Show code snippet for this specific image
                            if st.button(f"Use {img_info['name']}", key=f"use_img_{i}"):
                                image_code = f"""
# Load and display image
image = ImageMobject(r"{img_info['path']}")
image.scale(2)  # Adjust size as needed
self.play(FadeIn(image))
self.wait(1)
"""
                                if not st.session_state.code:
                                    base_code = """from manim import *
class ImageScene(Scene):
    def construct(self):
"""
                                    st.session_state.code = base_code + "\n        " + image_code.replace("\n", "\n        ")
                                else:
                                    st.session_state.code += "\n" + image_code
                                
                                st.session_state.temp_code = st.session_state.code
                                st.success(f"Added {img_info['name']} to your code!")
                                
                                # Set pending tab switch to editor tab
                                st.session_state.pending_tab_switch = 0
                                st.rerun()
                        except Exception as e:
                            st.error(f"Error loading image {img_info['name']}: {e}")
            
            # Display previously uploaded images
            if st.session_state.image_paths:
                with st.expander("Previously Uploaded Images"):
                    # Group images by 3 in each row
                    for i in range(0, len(st.session_state.image_paths), 3):
                        prev_cols = st.columns(3)
                        for j in range(3):
                            if i+j < len(st.session_state.image_paths):
                                img_info = st.session_state.image_paths[i+j]
                                with prev_cols[j]:
                                    try:
                                        img = Image.open(img_info["path"])
                                        st.image(img, caption=img_info["name"], width=100)
                                        st.markdown(f"<div class='small-text'>Path: {img_info['path']}</div>", unsafe_allow_html=True)
                                    except:
                                        st.markdown(f"**{img_info['name']}**")
                                        st.markdown(f"<div class='small-text'>Path: {img_info['path']}</div>", unsafe_allow_html=True)
            
        with asset_col2:
            # Audio uploader section
            st.markdown("#### 🎵 Audio Assets")
            st.markdown("Upload audio files for background or narration:")
            
            uploaded_audio = st.file_uploader("Upload Audio", type=["mp3", "wav", "ogg"], key="audio_uploader")
            
            if uploaded_audio:
                # Create a unique audio directory if it doesn't exist
                audio_dir = os.path.join(os.getcwd(), "manim_assets", "audio")
                os.makedirs(audio_dir, exist_ok=True)
                
                # Generate a unique filename and save the audio
                file_extension = uploaded_audio.name.split(".")[-1]
                unique_filename = f"audio_{int(time.time())}.{file_extension}"
                audio_path = os.path.join(audio_dir, unique_filename)
                
                with open(audio_path, "wb") as f:
                    f.write(uploaded_audio.getvalue())
                
                # Store the path in session state
                st.session_state.audio_path = audio_path
                
                # Display audio player
                st.audio(uploaded_audio)
                
                st.markdown(f"""
                <div class="asset-card">
                    <p><strong>Audio: {uploaded_audio.name}</strong></p>
                    <p class="small-text">Path: {audio_path}</p>
                </div>
                """, unsafe_allow_html=True)
                
                # Two options for audio usage
                st.markdown("#### Add Audio to Your Animation")
                
                option = st.radio(
                    "Choose how to use audio:",
                    ["Background Audio", "Generate Audio from Text"]
                )
                
                if option == "Background Audio":
                    st.markdown("##### Code to add background audio:")
                    
                    # For with_sound decorator
                    audio_code1 = f"""
# Add this import at the top of your file
from manim.scene.scene_file_writer import SceneFileWriter
# Add this decorator before your scene class
@with_sound("{audio_path}")
class YourScene(Scene):
    def construct(self):
        # Your animation code here
"""
                    st.code(audio_code1, language="python")
                    
                    if st.button("Use This Audio in Animation", key="use_audio_btn"):
                        st.success("Audio set for next render!")
                
                elif option == "Generate Audio from Text":
                    # Text-to-speech input
                    tts_text = st.text_area(
                        "Enter text for narration",
                        placeholder="Type the narration text here...",
                        height=100
                    )
                    
                    if st.button("Create Narration", key="create_narration_btn"):
                        try:
                            # Use basic TTS (placeholder for actual implementation)
                            st.warning("Text-to-speech feature requires additional setup. Using uploaded audio instead.")
                            st.session_state.audio_path = audio_path
                            st.success("Audio set for next render!")
                        except Exception as e:
                            st.error(f"Error creating narration: {str(e)}")

    # TIMELINE EDITOR TAB
    with tabs[3]:
        # New code for reordering animation steps
        updated_code = create_timeline_editor(st.session_state.code)
        
        # If code was modified by the timeline editor, update the session state
        if updated_code != st.session_state.code:
            st.session_state.code = updated_code
            st.session_state.temp_code = updated_code

    # EDUCATIONAL EXPORT TAB
    with tabs[4]:
        st.markdown("### 🎓 Educational Export Options")
        
        # Check if we have an animation to export
        if not st.session_state.video_data:
            st.warning("Generate an animation first before using educational export features.")
        else:
            st.markdown("Create various educational assets from your animation:")
            
            # Animation title and explanation
            animation_title = st.text_input("Animation Title", value="Manim Animation", key="edu_title")
            
            st.markdown("#### Explanation Text")
            st.markdown("Add explanatory text to accompany your animation. Use markdown formatting.")
            st.markdown("Use ## to separate explanation sections for step-by-step sequence export.")
            
            explanation_text = st.text_area(
                "Explanation (markdown supported)", 
                height=150,
                placeholder="Explain your animation here...\n\n## Step 1\nIntroduction to the concept...\n\n## Step 2\nNext, we demonstrate..."
            )
            
            # Export format selection
            edu_format = st.selectbox(
                "Export Format",
                options=["PowerPoint Presentation", "Interactive HTML", "Explanation Sequence PDF"]
            )
            
            # Format-specific options
            if edu_format == "PowerPoint Presentation":
                st.info("Creates a PowerPoint file with your animation and explanation text.")
                
            elif edu_format == "Interactive HTML":
                st.info("Creates an interactive HTML webpage with playback controls and explanation.")
                include_controls = st.checkbox("Include interactive controls", value=True)
                
            elif edu_format == "Explanation Sequence PDF":
                st.info("Creates a PDF with key frames and step-by-step explanations.")
                frame_count = st.slider("Number of key frames", min_value=3, max_value=10, value=5)
            
            # Export button
            if st.button("Export Educational Material", key="export_edu_btn"):
                with st.spinner(f"Creating {edu_format}..."):
                    # Map selected format to internal format type
                    format_map = {
                        "PowerPoint Presentation": "powerpoint",
                        "Interactive HTML": "html",
                        "Explanation Sequence PDF": "sequence"
                    }
                    
                    # Create a temporary directory for export
                    temp_export_dir = tempfile.mkdtemp(prefix="manim_edu_export_")
                    
                    # Process the export
                    exported_data, file_type = export_to_educational_format(
                        st.session_state.video_data,
                        format_map[edu_format],
                        animation_title,
                        explanation_text,
                        temp_export_dir
                    )
                    
                    if exported_data:
                        # File extension mapping
                        ext_map = {
                            "powerpoint": "pptx",
                            "html": "html",
                            "pdf": "pdf"
                        }
                        
                        # Download button
                        ext = ext_map.get(file_type, "zip")
                        filename = f"{animation_title.replace(' ', '_')}.{ext}"
                        
                        st.success(f"{edu_format} created successfully!")
                        st.download_button(
                            label=f"⬇️ Download {edu_format}",
                            data=exported_data,
                            file_name=filename,
                            mime=f"application/{ext}",
                            use_container_width=True
                        )
                        
                        # For HTML, also offer to open in browser
                        if file_type == "html":
                            html_path = os.path.join(temp_export_dir, filename)
                            st.markdown(f"[🌐 Open in browser](file://{html_path})", unsafe_allow_html=True)
                    else:
                        st.error(f"Failed to create {edu_format}. Check logs for details.")
                        
            # Show usage examples and tips
            with st.expander("Usage Tips"):
                st.markdown("""
                ### Educational Export Tips
                
                **PowerPoint Presentations**
                - Great for lectures and classroom presentations
                - Animation will autoplay when clicked
                - Add detailed explanations in notes section
                
                **Interactive HTML**
                - Perfect for websites and online learning platforms
                - Students can control playback speed and navigation
                - Mobile-friendly for learning on any device
                
                **Explanation Sequence**
                - Ideal for printed materials and study guides
                - Use ## headers to mark different explanation sections
                - Each section will be paired with a key frame
                """)

    # PYTHON RUNNER TAB
    with tabs[5]:
        st.markdown("### 🐍 Python Script Runner")
        st.markdown("Execute Python scripts and visualize the results directly.")
        
        # New UI elements for advanced features
        with st.expander("🔧 Advanced Python Features"):
            py_feature_col1, py_feature_col2 = st.columns(2)
            
            with py_feature_col1:
                enable_debugging = st.checkbox("Enable Debugging", value=False, key="py_debug_enable")
                enable_profiling = st.checkbox("Enable Profiling", value=False, key="py_profile_enable")
                
            with py_feature_col2:
                py_libs = st.multiselect(
                    "Additional Libraries",
                    options=["numpy", "scipy", "pandas", "matplotlib", "seaborn", "plotly", "scikit-learn", "tensorflow", "pytorch", "sympy"],
                    default=["numpy", "matplotlib"],
                    key="py_additional_libs"
                )

        # Multi-file project support
        with st.expander("📁 Multi-file Project"):
            st.markdown("Add multiple Python files to your project")
            
            # File manager
            if "py_project_files" not in st.session_state:
                st.session_state.py_project_files = {"main.py": st.session_state.python_script}
            
            # File selector
            current_file = st.selectbox(
                "Select File",
                options=list(st.session_state.py_project_files.keys()),
                key="py_current_file"
            )
            
            # New file creation
            new_file_col1, new_file_col2 = st.columns([3, 1])
            with new_file_col1:
                new_filename = st.text_input("New File Name", value="", key="py_new_filename")
            with new_file_col2:
                if st.button("Add File", key="py_add_file_btn"):
                    if new_filename and new_filename not in st.session_state.py_project_files:
                        if not new_filename.endswith(".py"):
                            new_filename += ".py"
                        st.session_state.py_project_files[new_filename] = "# New Python file\n\n"
                        st.session_state.py_current_file = new_filename
                        st.experimental_rerun()
            
            # Update the current file content in session state
            if current_file in st.session_state.py_project_files:
                st.session_state.py_project_files[current_file] = st.session_state.python_script
                # Update main script if we're editing the main file
                if current_file == "main.py":
                    st.session_state.python_script = st.session_state.python_script

        # Real-time visualization toggle
        real_time_viz = st.checkbox("Enable Real-time Visualization", value=False, key="py_realtime_viz")
        
        # Predefined example scripts
        example_scripts = {
            "Select an example...": "",
            "Basic Matplotlib Plot": """import matplotlib.pyplot as plt
import numpy as np
# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)
# Create plot
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', label='sin(x)')
plt.title('Sine Wave')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)
plt.legend()
""",
            "User Input Example": """# This example demonstrates how to handle user input
name = input("Enter your name: ")
age = int(input("Enter your age: "))
print(f"Hello, {name}! In 10 years, you'll be {age + 10} years old.")
# Let's get some numbers and calculate the average
num_count = int(input("How many numbers would you like to average? "))
total = 0
for i in range(num_count):
    num = float(input(f"Enter number {i+1}: "))
    total += num
average = total / num_count
print(f"The average of your {num_count} numbers is: {average}")
""",
            "Pandas DataFrame": """import pandas as pd
import numpy as np
# Create a sample dataframe
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'],
    'Age': [25, 30, 35, 40, 45],
    'Salary': [50000, 60000, 70000, 80000, 90000],
    'Department': ['HR', 'IT', 'Finance', 'Marketing', 'Engineering']
}
df = pd.DataFrame(data)
# Display the dataframe
print("Sample DataFrame:")
print(df)
# Basic statistics
print("\\nSummary Statistics:")
print(df.describe())
# Filtering
print("\\nEmployees older than 30:")
print(df[df['Age'] > 30])
""",
            "Seaborn Visualization": """import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
# Set the style
sns.set_style("whitegrid")
# Create sample data
np.random.seed(42)
data = np.random.randn(100, 3)
df = pd.DataFrame(data, columns=['A', 'B', 'C'])
df['category'] = pd.Categorical(['Group 1'] * 50 + ['Group 2'] * 50)
# Create a paired plot
sns.pairplot(df, hue='category', palette='viridis')
# Create another plot
plt.figure(figsize=(10, 6))
sns.violinplot(x='category', y='A', data=df, palette='magma')
plt.title('Distribution of A by Category')
"""
        }
        
        # Select example script
        selected_example = st.selectbox("Select an example script:", options=list(example_scripts.keys()))
        
        # Python code editor
        if selected_example != "Select an example..." and selected_example in example_scripts:
            python_code = example_scripts[selected_example]
        else:
            python_code = st.session_state.python_script
        
        if ACE_EDITOR_AVAILABLE:
            python_code = st_ace(
                value=python_code,
                language="python",
                theme="monokai",
                min_lines=15,
                key=f"python_editor_{st.session_state.editor_key}"
            )
        else:
            python_code = st.text_area(
                "Python Code",
                value=python_code,
                height=400,
                key=f"python_textarea_{st.session_state.editor_key}"
            )
        
        # Store script in session state (without clearing existing code)
        st.session_state.python_script = python_code
        
        # Check for input() calls
        input_calls = detect_input_calls(python_code)
        user_inputs = []
        
        if input_calls:
            st.markdown("### Input Values")
            st.info(f"This script contains {len(input_calls)} input() calls. Please provide values below:")
            
            for i, input_call in enumerate(input_calls):
                user_input = st.text_input(
                    f"{input_call['prompt']} (Line {input_call['line']})",
                    key=f"input_{i}"
                )
                user_inputs.append(user_input)
        
        # Options and execution
        col1, col2 = st.columns([2, 1])
        
        with col1:
            timeout_seconds = st.slider("Execution Timeout (seconds)", 5, 3600, 30)
        
        with col2:
            run_btn = st.button("▶️ Run Script", use_container_width=True)
        
        if run_btn:
            with st.spinner("Executing Python script..."):
                # Use the enhanced function
                result = run_python_script_enhanced(
                    python_code, 
                    inputs=user_inputs, 
                    timeout=timeout_seconds,
                    enable_debug=enable_debugging,
                    enable_profile=enable_profiling,
                    additional_libs=py_libs,
                    project_files=st.session_state.py_project_files if "py_project_files" in st.session_state else None,
                    realtime_viz=real_time_viz
                )
                st.session_state.python_result = result
        
        # Display results
        if st.session_state.python_result:
            display_python_script_results_enhanced(st.session_state.python_result)
            
            # Provide option to save the script
            if st.button("📄 Save This Script", key="save_script_btn"):
                # Generate a unique filename
                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                script_filename = f"script_{timestamp}.py"
                
                # Offer download button for the script
                st.download_button(
                    label="⬇️ Download Script",
                    data=python_code,
                    file_name=script_filename,
                    mime="text/plain"
                )
        
        # Show advanced examples and tips
        with st.expander("Python Script Runner Tips"):
            st.markdown("""
            ### Python Script Runner Tips
            
            **What can I run?**
            - Any Python code that doesn't require direct UI interaction
            - Libraries like Matplotlib, NumPy, Pandas, SciPy, etc.
            - Data processing and visualization code
            - Scripts that ask for user input (now supported!)
            
            **What can't I run?**
            - Streamlit, Gradio, Dash, or other web UIs
            - Long-running operations (timeout will occur)
            - Code that requires file access outside the temporary environment
            
            **Working with visualizations:**
            - All Matplotlib/Seaborn plots will be automatically captured
            - Pandas DataFrames are detected and displayed as tables
            - Use `print()` to show text output
            
            **Handling user input:**
            - The app detects input() calls and automatically creates text fields
            - Input values you provide will be passed to the script when it runs
            - Type conversion (like int(), float()) is preserved
            
            **Adding to animations:**
            - Charts and plots can be directly added to your Manim animations
            - Generated images will be properly scaled for your animation
            - Perfect for educational content combining data and animations
            """)

    # C/C++ RUNNER TAB
    with tabs[6]:  # Assuming this is the 7th tab (index 6)
        st.markdown("### 🔧 C/C++ Runner")
        st.markdown("Write, compile, and run C/C++ code with advanced features.")
        
        # Create a tabbed interface for different C++ features
        cpp_tabs = st.tabs(["Code Editor", "Project Files", "Libraries", "Build Settings", "Debugger"])
        
        with cpp_tabs[0]:  # Code Editor tab
            # Compiler options
            cpp_col1, cpp_col2, cpp_col3 = st.columns(3)
            
            with cpp_col1:
                compiler = st.selectbox(
                    "Compiler",
                    options=["g++", "clang++", "gcc", "msvc"],
                    index=["g++", "clang++", "gcc", "msvc"].index(st.session_state.cpp_settings["compiler"]),
                    key="cpp_compiler"
                )
                st.session_state.cpp_settings["compiler"] = compiler
            
            with cpp_col2:
                std_version = st.selectbox(
                    "Standard",
                    options=["c++11", "c++14", "c++17", "c++20"],
                    index=["c++11", "c++14", "c++17", "c++20"].index(st.session_state.cpp_settings["std"]),
                    key="cpp_std"
                )
                st.session_state.cpp_settings["std"] = std_version
            
            with cpp_col3:
                optimization = st.selectbox(
                    "Optimization",
                    options=["-O0", "-O1", "-O2", "-O3"],
                    index=["-O0", "-O1", "-O2", "-O3"].index(st.session_state.cpp_settings["optimization"]),
                    key="cpp_opt"
                )
                st.session_state.cpp_settings["optimization"] = optimization
            
            # Example code templates
            cpp_examples = {
                "Select an example...": "",
                "Hello World": """#include <iostream>

int main() {
    std::cout << "Hello, World!" << std::endl;
    return 0;
}""",
                "Calculate Prime Numbers": """#include <iostream>
#include <vector>
#include <chrono>

bool isPrime(int n) {
    if (n <= 1) return false;
    if (n <= 3) return true;
    if (n % 2 == 0 || n % 3 == 0) return false;
    
    for (int i = 5; i * i <= n; i += 6) {
        if (n % i == 0 || n % (i + 2) == 0)
            return false;
    }
    return true;
}

int main() {
    int limit = 10000;
    std::vector<int> primes;
    
    auto start = std::chrono::high_resolution_clock::now();
    
    for (int i = 2; i <= limit; i++) {
        if (isPrime(i)) {
            primes.push_back(i);
        }
    }
    
    auto end = std::chrono::high_resolution_clock::now();
    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
    
    std::cout << "Found " << primes.size() << " prime numbers up to " << limit << std::endl;
    std::cout << "First 10 primes: ";
    for (int i = 0; i < std::min(10, (int)primes.size()); i++) {
        std::cout << primes[i] << " ";
    }
    std::cout << std::endl;
    std::cout << "Computation time: " << duration.count() << " ms" << std::endl;
    
    return 0;
}""",
                "Image Generation (PPM)": """#include <iostream>
#include <fstream>
#include <cmath>

// Generate a simple gradient image in PPM format
int main() {
    const int width = 800;
    const int height = 600;
    
    // Create a PPM file (P3 format - ASCII)
    std::ofstream image("output.ppm");
    image << "P3\\n" << width << " " << height << "\\n255\\n";
    
    for (int y = 0; y < height; y++) {
        for (int x = 0; x < width; x++) {
            // Create a gradient based on position
            int r = static_cast<int>(255.0 * x / width);
            int g = static_cast<int>(255.0 * y / height);
            int b = static_cast<int>(255.0 * (x + y) / (width + height));
            
            // Write RGB values
            image << r << " " << g << " " << b << "\\n";
        }
    }
    
    image.close();
    std::cout << "Generated gradient image: output.ppm" << std::endl;
    return 0;
}""",
                "Data Processing with Vectors": """#include <iostream>
#include <vector>
#include <algorithm>
#include <numeric>
#include <random>
#include <iomanip>

int main() {
    const int data_size = 1000;
    
    // Generate random data
    std::vector<double> data(data_size);
    
    std::random_device rd;
    std::mt19937 gen(rd());
    std::normal_distribution<double> dist(100.0, 15.0);
    
    std::cout << "Generating " << data_size << " random values..." << std::endl;
    for (auto& value : data) {
        value = dist(gen);
    }
    
    // Calculate statistics
    double sum = std::accumulate(data.begin(), data.end(), 0.0);
    double mean = sum / data.size();
    
    std::vector<double> deviations(data_size);
    std::transform(data.begin(), data.end(), deviations.begin(),
                  [mean](double x) { return x - mean; });
    
    double sq_sum = std::inner_product(deviations.begin(), deviations.end(),
                                      deviations.begin(), 0.0);
    double stddev = std::sqrt(sq_sum / data.size());
    
    // Sort data
    std::sort(data.begin(), data.end());
    double median = data.size() % 2 == 0 ?
                   (data[data.size()/2 - 1] + data[data.size()/2]) / 2 :
                   data[data.size()/2];
    
    // Output results
    std::cout << std::fixed << std::setprecision(2);
    std::cout << "Data analysis results:" << std::endl;
    std::cout << "Mean:   " << mean << std::endl;
    std::cout << "Median: " << median << std::endl;
    std::cout << "StdDev: " << stddev << std::endl;
    std::cout << "Min:    " << data.front() << std::endl;
    std::cout << "Max:    " << data.back() << std::endl;
    
    return 0;
}""",
                "Interactive User Input": """#include <iostream>
#include <string>
#include <vector>

int main() {
    std::string name;
    int age;
    
    // Get user input
    std::cout << "Enter your name: ";
    std::getline(std::cin, name);
    
    std::cout << "Enter your age: ";
    std::cin >> age;
    std::cin.ignore(); // Clear the newline from the buffer
    
    std::cout << "Hello, " << name << "! ";
    std::cout << "In 10 years, you will be " << age + 10 << " years old." << std::endl;
    
    // Get multiple numbers
    int num_count;
    std::cout << "How many numbers would you like to enter? ";
    std::cin >> num_count;
    
    std::vector<double> numbers;
    double total = 0.0;
    
    for (int i = 0; i < num_count; i++) {
        double num;
        std::cout << "Enter number " << (i+1) << ": ";
        std::cin >> num;
        numbers.push_back(num);
        total += num;
    }
    
    if (!numbers.empty()) {
        double average = total / numbers.size();
        std::cout << "The average of your numbers is: " << average << std::endl;
    }
    
    return 0;
}""",
                "Eigen Matrix Operations": """#include <iostream>
#include <Eigen/Dense>

using Eigen::MatrixXd;
using Eigen::VectorXd;

int main() {
    // Create a 3x3 matrix
    MatrixXd A(3, 3);
    A << 1, 2, 3,
         4, 5, 6,
         7, 8, 9;
    
    // Create a 3D vector
    VectorXd b(3);
    b << 1, 2, 3;
    
    // Perform operations
    std::cout << "Matrix A:\\n" << A << std::endl;
    std::cout << "Vector b:\\n" << b << std::endl;
    std::cout << "A * b:\\n" << A * b << std::endl;
    std::cout << "A transpose:\\n" << A.transpose() << std::endl;
    
    // Solve a linear system Ax = b
    VectorXd x = A.colPivHouseholderQr().solve(b);
    std::cout << "Solution to Ax = b:\\n" << x << std::endl;
    
    // Compute eigenvalues and eigenvectors
    Eigen::EigenSolver<MatrixXd> solver(A);
    std::cout << "Eigenvalues:\\n" << solver.eigenvalues() << std::endl;
    std::cout << "Eigenvectors:\\n" << solver.eigenvectors() << std::endl;
    
    return 0;
}""",
                "OpenCV Image Processing": """#include <iostream>
#include <opencv2/opencv.hpp>

int main() {
    // Load an image (this will create a blank image if no file is found)
    cv::Mat image = cv::Mat::zeros(500, 500, CV_8UC3);
    
    // Draw a circle
    cv::circle(image, cv::Point(250, 250), 100, cv::Scalar(0, 0, 255), 5);
    
    // Draw a rectangle
    cv::rectangle(image, cv::Point(150, 150), cv::Point(350, 350), cv::Scalar(0, 255, 0), 3);
    
    // Add text
    cv::putText(image, "OpenCV Example", cv::Point(100, 50), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(255, 255, 255), 2);
    
    // Save the image
    cv::imwrite("opencv_output.png", image);
    
    std::cout << "Image created and saved as 'opencv_output.png'" << std::endl;
    
    return 0;
}"""
            }
            
            # Example selection
            selected_cpp_example = st.selectbox("Example code:", options=list(cpp_examples.keys()))
            
            # Set initial code from example or session state
            if selected_cpp_example != "Select an example..." and cpp_examples[selected_cpp_example] != "":
                initial_code = cpp_examples[selected_cpp_example]
            else:
                if "cpp_current_file" in st.session_state and st.session_state.cpp_current_file in st.session_state.cpp_project_files:
                    initial_code = st.session_state.cpp_project_files[st.session_state.cpp_current_file]
                else:
                    initial_code = st.session_state.cpp_code
            
            # Code editor for C++
            if ACE_EDITOR_AVAILABLE:
                cpp_code = st_ace(
                    value=initial_code,
                    language="c_cpp",
                    theme="monokai",
                    min_lines=15,
                    key=f"cpp_editor_{st.session_state.editor_key}"
                )
            else:
                cpp_code = st.text_area(
                    "C/C++ Code",
                    value=initial_code,
                    height=400,
                    key=f"cpp_textarea_{st.session_state.editor_key}"
                )
            
            # Save the code to session state
            st.session_state.cpp_code = cpp_code
            
            # Update project files
            if "cpp_current_file" in st.session_state and st.session_state.cpp_current_file in st.session_state.cpp_project_files:
                st.session_state.cpp_project_files[st.session_state.cpp_current_file] = cpp_code
            
            # Check for standard input in the code
            has_cin = "std::cin" in cpp_code or "cin" in cpp_code
            
            # Input values section if needed
            cpp_inputs = []
            if has_cin:
                with st.expander("Input Values"):
                    st.info("This program uses standard input. Please provide input values below:")
                    
                    num_inputs = st.number_input("Number of input lines:", min_value=1, max_value=10, value=1)
                    for i in range(int(num_inputs)):
                        cpp_input = st.text_input(f"Input line {i+1}:", key=f"cpp_input_{i}")
                        cpp_inputs.append(cpp_input)
        
        with cpp_tabs[1]:  # Project Files tab
            st.markdown("### Project Files")
            st.markdown("Manage multiple source files for your C/C++ project")
            
            # File selector
            cpp_current_file = st.selectbox(
                "Current File",
                options=list(st.session_state.cpp_project_files.keys()),
                index=list(st.session_state.cpp_project_files.keys()).index(st.session_state.cpp_current_file) if "cpp_current_file" in st.session_state else 0,
                key="cpp_file_selector"
            )
            
            # Update the current file in session state
            st.session_state.cpp_current_file = cpp_current_file
            
            # Create new file form
            new_file_col1, new_file_col2 = st.columns([3, 1])
            with new_file_col1:
                new_cpp_filename = st.text_input("New File Name", placeholder="e.g., utils.h, helper.cpp", key="new_cpp_file")
            with new_file_col2:
                if st.button("Add File", key="add_cpp_file"):
                    if new_cpp_filename and new_cpp_filename not in st.session_state.cpp_project_files:
                        # Add file extension if missing
                        if not new_cpp_filename.endswith((".cpp", ".h", ".hpp", ".c", ".cc")):
                            new_cpp_filename += ".cpp"
                        
                        # Create a template based on file type
                        if new_cpp_filename.endswith((".h", ".hpp")):
                            template = f"""#ifndef {new_cpp_filename.split('.')[0].upper()}_H
#define {new_cpp_filename.split('.')[0].upper()}_H

// Your header content here

#endif // {new_cpp_filename.split('.')[0].upper()}_H
"""
                        else:
                            template = f"""#include <iostream>

// Your implementation here

"""
                        
                        st.session_state.cpp_project_files[new_cpp_filename] = template
                        st.session_state.cpp_current_file = new_cpp_filename
                        st.experimental_rerun()
            
            # File actions
            file_action_col1, file_action_col2 = st.columns(2)
            with file_action_col1:
                if st.button("Delete Current File", key="delete_cpp_file"):
                    if cpp_current_file != "main.cpp" and cpp_current_file in st.session_state.cpp_project_files:
                        del st.session_state.cpp_project_files[cpp_current_file]
                        st.session_state.cpp_current_file = "main.cpp"
                        st.experimental_rerun()
                    else:
                        st.error("Cannot delete main.cpp")
            
            with file_action_col2:
                if st.button("Download Project Files", key="download_cpp_project"):
                    # Create a zip file with all project files
                    with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp:
                        with zipfile.ZipFile(tmp.name, 'w') as zipf:
                            for filename, content in st.session_state.cpp_project_files.items():
                                # Add file to zip
                                zipf.writestr(filename, content)
                        
                        # Download the zip file
                        with open(tmp.name, "rb") as f:
                            zip_data = f.read()
                            
                        st.download_button(
                            label="Download ZIP",
                            data=zip_data,
                            file_name="cpp_project.zip",
                            mime="application/zip"
                        )
            
            # Project structure visualization
            st.markdown("### Project Structure")
            
            # Group files by type
            headers = []
            sources = []
            others = []
            
            for filename in st.session_state.cpp_project_files:
                if filename.endswith((".h", ".hpp")):
                    headers.append(filename)
                elif filename.endswith((".cpp", ".c", ".cc")):
                    sources.append(filename)
                else:
                    others.append(filename)
            
            # Display structure
            st.markdown("#### Header Files")
            if headers:
                for header in sorted(headers):
                    st.markdown(f"- `{header}`")
            else:
                st.markdown("No header files")
            
            st.markdown("#### Source Files")
            if sources:
                for source in sorted(sources):
                    st.markdown(f"- `{source}`")
            else:
                st.markdown("No source files")
            
            if others:
                st.markdown("#### Other Files")
                for other in sorted(others):
                    st.markdown(f"- `{other}`")
                    
        with cpp_tabs[2]:  # Libraries tab
            st.markdown("### Library Manager")
            st.markdown("Configure libraries and dependencies for your C/C++ project")
            
            # Common library selection
            common_libs = st.multiselect(
                "Common Libraries",
                options=["Eigen", "Boost", "OpenCV", "FFTW", "SDL2", "SFML", "OpenGL", "stb_image", "nlohmann_json", "fmt"],
                default=st.session_state.cpp_settings.get("libraries", []),
                key="cpp_common_libs"
            )
            
            # Update libraries in settings
            st.session_state.cpp_settings["libraries"] = common_libs
            
            # Include paths
            st.markdown("#### Include Paths")
            include_paths = st.text_area(
                "Include Directories (one per line)",
                value="\n".join(st.session_state.cpp_settings.get("include_paths", [])),
                height=100,
                key="cpp_include_paths"
            )
            
            # Update include paths in settings
            st.session_state.cpp_settings["include_paths"] = [path for path in include_paths.split("\n") if path.strip()]
            
            # Library paths
            st.markdown("#### Library Paths")
            library_paths = st.text_area(
                "Library Directories (one per line)",
                value="\n".join(st.session_state.cpp_settings.get("library_paths", [])),
                height=100,
                key="cpp_library_paths"
            )
            
            # Update library paths in settings
            st.session_state.cpp_settings["library_paths"] = [path for path in library_paths.split("\n") if path.strip()]
            
            # Additional libraries
            st.markdown("#### Additional Libraries")
            additional_libs = st.text_area(
                "Additional Libraries (one per line, without -l prefix)",
                value="\n".join(st.session_state.cpp_settings.get("additional_libs", [])),
                height=100,
                key="cpp_additional_libs"
            )
            
            # Update additional libraries in settings
            st.session_state.cpp_settings["additional_libs"] = [lib for lib in additional_libs.split("\n") if lib.strip()]
            
            # Library detection
            if st.button("Detect Installed Libraries", key="detect_libs"):
                with st.spinner("Detecting libraries..."):
                    # This is a placeholder - in a real implementation, you'd scan the system
                    detected_libs = []
                    
                    # Check for Eigen
                    try:
                        result = subprocess.run(
                            ["find", "/usr/include", "-name", "Eigen"],
                            capture_output=True,
                            text=True,
                            timeout=5
                        )
                        if "Eigen" in result.stdout:
                            detected_libs.append("Eigen")
                    except:
                        pass
                    
                    # Check for Boost
                    try:
                        result = subprocess.run(
                            ["find", "/usr/include", "-name", "boost"],
                            capture_output=True,
                            text=True,
                            timeout=5
                        )
                        if "boost" in result.stdout:
                            detected_libs.append("Boost")
                    except:
                        pass
                    
                    # Check for OpenCV
                    try:
                        result = subprocess.run(
                            ["pkg-config", "--exists", "opencv4"],
                            capture_output=True,
                            timeout=5
                        )
                        if result.returncode == 0:
                            detected_libs.append("OpenCV")
                    except:
                        pass
                    
                    # Display detected libraries
                    if detected_libs:
                        st.success(f"Detected libraries: {', '.join(detected_libs)}")
                        # Add to selected libraries if not already present
                        for lib in detected_libs:
                            if lib not in st.session_state.cpp_settings["libraries"]:
                                st.session_state.cpp_settings["libraries"].append(lib)
                    else:
                        st.warning("No common libraries detected")
        
        with cpp_tabs[3]:  # Build Settings tab
            st.markdown("### Build Configuration")
            
            # Build type
            build_type = st.radio(
                "Build Type",
                options=["Debug", "Release", "RelWithDebInfo"],
                index=1,  # Default to Release
                key="cpp_build_type"
            )
            
            # Update build type in settings
            st.session_state.cpp_settings["build_type"] = build_type
            
            # Advanced compiler flags
            st.markdown("#### Advanced Compiler Flags")
            advanced_flags = st.text_area(
                "Additional Compiler Flags",
                value=st.session_state.cpp_settings.get("advanced_flags", ""),
                height=100,
                key="cpp_advanced_flags"
            )
            
            # Update advanced flags in settings
            st.session_state.cpp_settings["advanced_flags"] = advanced_flags
            
            # Preprocessor definitions
            st.markdown("#### Preprocessor Definitions")
            definitions = st.text_area(
                "Preprocessor Definitions (one per line)",
                value="\n".join(st.session_state.cpp_settings.get("definitions", [])),
                height=100,
                placeholder="Example:\nDEBUG\nVERSION=1.0\nUSE_FEATURE_X",
                key="cpp_definitions"
            )
            
            # Update definitions in settings
            st.session_state.cpp_settings["definitions"] = [d for d in definitions.split("\n") if d.strip()]
            
            # Generate CMakeLists.txt
            if st.button("Generate CMakeLists.txt", key="gen_cmake"):
                # Create CMakeLists.txt content
                cmake_content = f"""cmake_minimum_required(VERSION 3.10)
project(ManimCppProject)

set(CMAKE_CXX_STANDARD {st.session_state.cpp_settings["std"].replace("c++", "")})
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)

# Build type
set(CMAKE_BUILD_TYPE {build_type})

# Preprocessor definitions
"""
                
                # Add definitions
                for definition in st.session_state.cpp_settings.get("definitions", []):
                    if "=" in definition:
                        name, value = definition.split("=", 1)
                        cmake_content += f'add_definitions(-D{name}="{value}")\n'
                    else:
                        cmake_content += f"add_definitions(-D{definition})\n"
                
                # Add include paths
                if st.session_state.cpp_settings.get("include_paths", []):
                    cmake_content += "\n# Include directories\n"
                    for path in st.session_state.cpp_settings["include_paths"]:
                        cmake_content += f"include_directories({path})\n"
                
                # Add library paths
                if st.session_state.cpp_settings.get("library_paths", []):
                    cmake_content += "\n# Library directories\n"
                    for path in st.session_state.cpp_settings["library_paths"]:
                        cmake_content += f"link_directories({path})\n"
                
                # Add common libraries
                if "Eigen" in st.session_state.cpp_settings.get("libraries", []):
                    cmake_content += "\n# Eigen\n"
                    cmake_content += "find_package(Eigen3 REQUIRED)\n"
                    cmake_content += "include_directories(${EIGEN3_INCLUDE_DIR})\n"
                
                if "OpenCV" in st.session_state.cpp_settings.get("libraries", []):
                    cmake_content += "\n# OpenCV\n"
                    cmake_content += "find_package(OpenCV REQUIRED)\n"
                    cmake_content += "include_directories(${OpenCV_INCLUDE_DIRS})\n"
                
                if "Boost" in st.session_state.cpp_settings.get("libraries", []):
                    cmake_content += "\n# Boost\n"
                    cmake_content += "find_package(Boost REQUIRED)\n"
                    cmake_content += "include_directories(${Boost_INCLUDE_DIRS})\n"
                
                # Add source files
                cmake_content += "\n# Source files\n"
                source_files = [f for f in st.session_state.cpp_project_files.keys() if f.endswith((".cpp", ".c", ".cc"))]
                cmake_content += "add_executable(main\n"
                for src in source_files:
                    cmake_content += f"    {src}\n"
                cmake_content += ")\n"
                
                # Add libraries to link
                cmake_content += "\n# Link libraries\n"
                cmake_content += "target_link_libraries(main\n"
                
                if "OpenCV" in st.session_state.cpp_settings.get("libraries", []):
                    cmake_content += "    ${OpenCV_LIBS}\n"
                
                if "Boost" in st.session_state.cpp_settings.get("libraries", []):
                    cmake_content += "    ${Boost_LIBRARIES}\n"
                
                # Additional libraries
                for lib in st.session_state.cpp_settings.get("additional_libs", []):
                    cmake_content += f"    {lib}\n"
                
                cmake_content += ")\n"
                
                # Save CMakeLists.txt to project files
                st.session_state.cpp_project_files["CMakeLists.txt"] = cmake_content
                
                # Show the generated file
                st.success("CMakeLists.txt generated!")
                st.code(cmake_content, language="cmake")
                
        with cpp_tabs[4]:  # Debugger tab
            st.markdown("### C++ Debugger")
            st.markdown("Debug your C++ code with breakpoints and variable inspection")
            
            # Enable debugging
            enable_cpp_debug = st.checkbox("Enable Debugging", value=False, key="cpp_debug_enable")
            
            if enable_cpp_debug:
                # Breakpoints
                st.markdown("#### Breakpoints")
                st.markdown("Enter line numbers for breakpoints (one per line)")
                
                breakpoints = st.text_area(
                    "Breakpoints",
                    placeholder="Example:\n10\n15\n20",
                    height=100,
                    key="cpp_breakpoints"
                )
                
                breakpoint_lines = []
                for line in breakpoints.split("\n"):
                    try:
                        line_num = int(line.strip())
                        if line_num > 0:
                            breakpoint_lines.append(line_num)
                    except:
                        pass
                
                # Watch variables
                st.markdown("#### Watch Variables")
                st.markdown("Enter variable names to watch (one per line)")
                
                watch_vars = st.text_area(
                    "Watch Variables",
                    placeholder="Example:\ni\nsum\nresult",
                    height=100,
                    key="cpp_watch_vars"
                )
                
                watch_variables = [var.strip() for var in watch_vars.split("\n") if var.strip()]
        
        # Compilation and execution options
        st.markdown("### Run Configuration")
        
        run_options_col1, run_options_col2 = st.columns(2)
        
        with run_options_col1:
            cpp_timeout = st.slider("Execution Timeout (seconds)", 1, 60, 10)
        
        with run_options_col2:
            compile_btn = st.button("🛠️ Compile and Run", use_container_width=True)
        
        # Compile and run the C++ code
        if compile_btn:
            with st.spinner("Compiling C++ code..."):
                cpp_code_to_compile = st.session_state.cpp_code
                if "cpp_project_files" in st.session_state and st.session_state.cpp_project_files:
                    # Use project files
                    executable_path, compile_error, temp_dir = compile_cpp_code_enhanced(
                        cpp_code_to_compile, 
                        st.session_state.cpp_settings,
                        project_files=st.session_state.cpp_project_files,
                        enable_debug=enable_cpp_debug if "enable_cpp_debug" in locals() else False,
                        breakpoints=breakpoint_lines if "breakpoint_lines" in locals() else None,
                        watch_vars=watch_variables if "watch_variables" in locals() else None
                    )
                else:
                    # Use single file
                    executable_path, compile_error, temp_dir = compile_cpp_code_enhanced(
                        cpp_code_to_compile, 
                        st.session_state.cpp_settings,
                        enable_debug=enable_cpp_debug if "enable_cpp_debug" in locals() else False,
                        breakpoints=breakpoint_lines if "breakpoint_lines" in locals() else None,
                        watch_vars=watch_variables if "watch_variables" in locals() else None
                    )
                
                if compile_error:
                    st.error("Compilation Error:")
                    st.code(compile_error, language="bash")
                else:
                    st.success("Compilation successful!")
                    
                    with st.spinner("Running program..."):
                        result = run_cpp_executable_enhanced(
                            executable_path,
                            temp_dir,
                            inputs=cpp_inputs if "cpp_inputs" in locals() else None,
                            timeout=cpp_timeout,
                            enable_debug=enable_cpp_debug if "enable_cpp_debug" in locals() else False,
                            breakpoints=breakpoint_lines if "breakpoint_lines" in locals() else None,
                            watch_vars=watch_variables if "watch_variables" in locals() else None
                        )
                        
                        st.session_state.cpp_result = result
        
        # Display results
        if "cpp_result" in st.session_state and st.session_state.cpp_result:
            result = st.session_state.cpp_result
            
            st.markdown("### Results")
            
            # Execution information
            info_cols = st.columns(3)
            with info_cols[0]:
                st.info(f"Execution Time: {result['execution_time']:.3f} seconds")
            
            with info_cols[1]:
                if result.get("memory_usage"):
                    st.info(f"Memory Usage: {result['memory_usage']:.2f} MB")
            
            with info_cols[2]:
                if result["exception"]:
                    st.error(f"Exception: {result['exception']}")
            
            # Show debug output if available
            if result.get("debug_output"):
                with st.expander("Debug Output", expanded=True):
                    st.code(result["debug_output"], language="bash")
            
            # Result tabs
            result_tabs = st.tabs(["Output", "Images", "Manim Integration"])
            
            with result_tabs[0]:  # Output tab
                # Show stdout if any
                if result["stdout"]:
                    st.markdown("#### Standard Output")
                    st.code(result["stdout"], language="bash")
                
                # Show stderr if any
                if result["stderr"]:
                    st.markdown("#### Standard Error")
                    st.code(result["stderr"], language="bash")
            
            with result_tabs[1]:  # Images tab
                # Show images if any
                if result["images"]:
                    st.markdown("#### Generated Images")
                    img_cols = st.columns(min(3, len(result["images"])))
                    
                    for i, img in enumerate(result["images"]):
                        with img_cols[i % len(img_cols)]:
                            st.image(img["data"], caption=img["name"])
                else:
                    st.info("No images were generated by the program.")
            
            with result_tabs[2]:  # Manim Integration tab
                st.markdown("#### Integrate C++ Results with Manim")
                
                # Create options for integration
                integration_type = st.radio(
                    "Integration Type",
                    options=["Data Visualization", "Image Import", "Animation Sequence"],
                    key="cpp_integration_type"
                )
                
                if integration_type == "Data Visualization":
                    # Extract numerical data from stdout if possible
                    lines = result["stdout"].strip().split("\n")
                    data_options = []
                    
                    for i, line in enumerate(lines):
                        # Check if line contains numbers
                        numbers = []
                        try:
                            # Try to extract numbers from the line
                            numbers = [float(x) for x in line.split() if x.replace(".", "").isdigit()]
                            if numbers:
                                data_options.append(f"Line {i+1}: {line[:30]}{'...' if len(line) > 30 else ''}")
                        except:
                            pass
                    
                    if data_options:
                        selected_data_line = st.selectbox(
                            "Select Data to Visualize",
                            options=["Select a line..."] + data_options,
                            key="cpp_data_line"
                        )
                        
                        if selected_data_line != "Select a line...":
                            line_idx = int(selected_data_line.split(":")[0].replace("Line ", "")) - 1
                            line = lines[line_idx]
                            
                            # Extract numbers
                            try:
                                numbers = [float(x) for x in line.split() if x.replace(".", "").isdigit()]
                                
                                # Preview the data
                                st.markdown(f"**Extracted Data:** {numbers}")
                                
                                # Create visualization code
                                if st.button("Create Manim Visualization", key="cpp_create_viz"):
                                    viz_code = f"""
# Visualize data from C++ output
values = {numbers}
axes = Axes(
    x_range=[0, {len(numbers)}, 1],
    y_range=[{min(numbers) if numbers else 0}, {max(numbers) if numbers else 10}, {(max(numbers)-min(numbers))/10 if numbers and max(numbers) > min(numbers) else 1}],
    axis_config={{"color": BLUE}}
)
points = [axes.coords_to_point(i, v) for i, v in enumerate(values)]
dots = VGroup(*[Dot(point, color=RED) for point in points])
graph = VMobject(color=YELLOW)
graph.set_points_as_corners(points)

self.play(Create(axes))
self.play(Create(dots), run_time=2)
self.play(Create(graph), run_time=2)
self.wait(1)
"""
                                    if st.session_state.code:
                                        st.session_state.code += "\n" + viz_code
                                    else:
                                        st.session_state.code = f"""from manim import *
class CppDataVisualizationScene(Scene):
    def construct(self):
        {viz_code}
"""
                                    st.session_state.temp_code = st.session_state.code
                                    st.success("Added C++ data visualization to your Manim code!")
                                    # Set pending tab switch to editor tab
                                    st.session_state.pending_tab_switch = 0
                                    st.rerun()
                            except Exception as e:
                                st.error(f"Error extracting numbers: {str(e)}")
                    else:
                        st.warning("No numeric data detected in the output.")
                
                elif integration_type == "Image Import":
                    # Handle image import
                    if result["images"]:
                        st.markdown("#### Select Images to Import")
                        
                        for i, img in enumerate(result["images"]):
                            st.markdown(f"**{img['name']}**")
                            st.image(img["data"], width=300)
                            
                            if st.button(f"Use in Manim", key=f"use_cpp_img_{i}"):
                                # Save image to a temporary file
                                with tempfile.NamedTemporaryFile(delete=False, suffix=f"_{img['name']}") as tmp:
                                    tmp.write(img["data"])
                                    img_path = tmp.name
                                
                                # Generate Manim code
                                image_code = f"""
# Load and display image generated from C++
cpp_image = ImageMobject(r"{img_path}")
cpp_image.scale(2)  # Adjust size as needed
self.play(FadeIn(cpp_image))
self.wait(1)
"""
                                if st.session_state.code:
                                    st.session_state.code += "\n" + image_code
                                else:
                                    st.session_state.code = f"""from manim import *
class CppImageScene(Scene):
    def construct(self):
        {image_code}
"""
                                st.session_state.temp_code = st.session_state.code
                                st.success(f"Added C++ generated image to your Manim code!")
                                st.session_state.pending_tab_switch = 0  # Switch to editor tab
                                st.rerun()
                    else:
                        st.warning("No images were generated by the C++ program.")
                
                elif integration_type == "Animation Sequence":
                    st.markdown("#### Create Animation Sequence")
                    st.info("This will create a Manim animation that visualizes the execution of your C++ program.")
                    
                    # Animation type options
                    animation_style = st.selectbox(
                        "Animation Style",
                        options=["Algorithm Visualization", "Data Flow", "Memory Model"],
                        key="cpp_anim_style"
                    )
                    
                    if st.button("Generate Animation Sequence", key="cpp_gen_anim_seq"):
                        # Create different animations based on selected style
                        if animation_style == "Algorithm Visualization":
                            # Example code for algorithm visualization
                            algo_code = f"""
# C++ Algorithm Visualization
title = Text("C++ Algorithm Visualization")
self.play(Write(title))
self.play(title.animate.to_edge(UP))
self.wait(0.5)

# Create an array representation
values = [5, 2, 8, 1, 9, 3, 7, 4, 6]  # Example values
squares = VGroup(*[Square(side_length=0.7, fill_opacity=0.8, fill_color=BLUE) for _ in values])
squares.arrange(RIGHT, buff=0.1)
labels = VGroup(*[Text(str(v), font_size=24) for v in values])
for label, square in zip(labels, squares):
    label.move_to(square.get_center())

array = VGroup(squares, labels)
array_label = Text("Array", font_size=20).next_to(array, UP)
self.play(FadeIn(array), Write(array_label))
self.wait(1)

# Simulate sorting algorithm
for i in range(len(values)-1):
    # Highlight current element
    self.play(squares[i].animate.set_fill(RED))
    
    for j in range(i+1, len(values)):
        # Highlight comparison element
        self.play(squares[j].animate.set_fill(YELLOW))
        
        # Simulate comparison
        if values[i] > values[j]:
            # Swap animation
            self.play(
                labels[i].animate.move_to(squares[j].get_center()),
                labels[j].animate.move_to(squares[i].get_center())
            )
            
            # Update values and labels
            labels[i], labels[j] = labels[j], labels[i]
            values[i], values[j] = values[j], values[i]
        
        # Reset comparison element
        self.play(squares[j].animate.set_fill(BLUE))
    
    # Mark current element as processed
    self.play(squares[i].animate.set_fill(GREEN))

# Mark the last element as processed
self.play(squares[-1].animate.set_fill(GREEN))

# Show sorted array
sorted_label = Text("Sorted Array", font_size=20).next_to(array, DOWN)
self.play(Write(sorted_label))
self.wait(2)
"""
                            if st.session_state.code:
                                st.session_state.code += "\n" + algo_code
                            else:
                                st.session_state.code = f"""from manim import *
class CppAlgorithmScene(Scene):
    def construct(self):
        {algo_code}
"""
                            st.session_state.temp_code = st.session_state.code
                            st.success("Added C++ algorithm visualization to your Manim code!")
                            st.session_state.pending_tab_switch = 0  # Switch to editor tab
                            st.rerun()
                        
                        elif animation_style == "Data Flow":
                            # Example code for data flow visualization
                            data_flow_code = f"""
# C++ Data Flow Visualization
title = Text("C++ Data Flow")
self.play(Write(title))
self.play(title.animate.to_edge(UP))
self.wait(0.5)

# Create nodes for data flow
input_node = Circle(radius=0.5, fill_opacity=0.8, fill_color=BLUE)
process_node = Square(side_length=1, fill_opacity=0.8, fill_color=GREEN)
output_node = Circle(radius=0.5, fill_opacity=0.8, fill_color=RED)

# Position nodes
input_node.move_to(LEFT*4)
process_node.move_to(ORIGIN)
output_node.move_to(RIGHT*4)

# Add labels
input_label = Text("Input", font_size=20).next_to(input_node, DOWN)
process_label = Text("Process", font_size=20).next_to(process_node, DOWN)
output_label = Text("Output", font_size=20).next_to(output_node, DOWN)

# Create arrows
arrow1 = Arrow(input_node.get_right(), process_node.get_left(), buff=0.2)
arrow2 = Arrow(process_node.get_right(), output_node.get_left(), buff=0.2)

# Display nodes and arrows
self.play(FadeIn(input_node), Write(input_label))
self.wait(0.5)
self.play(FadeIn(process_node), Write(process_label))
self.wait(0.5)
self.play(FadeIn(output_node), Write(output_label))
self.wait(0.5)
self.play(Create(arrow1), Create(arrow2))
self.wait(1)

# Simulate data flow
data = Text("Data", font_size=16).move_to(input_node.get_center())
self.play(FadeIn(data))
self.wait(0.5)

# Move data along the flow
self.play(data.animate.move_to(arrow1.get_center()))
self.wait(0.5)
self.play(data.animate.move_to(process_node.get_center()))
self.wait(0.5)
transformed_data = Text("Processed", font_size=16, color=YELLOW)
transformed_data.move_to(process_node.get_center())
self.play(Transform(data, transformed_data))
self.wait(0.5)
self.play(data.animate.move_to(arrow2.get_center()))
self.wait(0.5)
self.play(data.animate.move_to(output_node.get_center()))
self.wait(1)

result_text = Text("Final Result", font_size=24).to_edge(DOWN)
self.play(Write(result_text))
self.wait(2)
"""
                            if st.session_state.code:
                                st.session_state.code += "\n" + data_flow_code
                            else:
                                st.session_state.code = f"""from manim import *
class CppDataFlowScene(Scene):
    def construct(self):
        {data_flow_code}
"""
                            st.session_state.temp_code = st.session_state.code
                            st.success("Added C++ data flow visualization to your Manim code!")
                            st.session_state.pending_tab_switch = 0  # Switch to editor tab
                            st.rerun()
                        
                        elif animation_style == "Memory Model":
                            # Example code for memory model visualization
                            memory_code = f"""
# C++ Memory Model Visualization
title = Text("C++ Memory Model")
self.play(Write(title))
self.play(title.animate.to_edge(UP))
self.wait(0.5)

# Create memory blocks
stack_rect = Rectangle(height=3, width=4, fill_opacity=0.2, fill_color=BLUE)
stack_rect.move_to(LEFT*3.5)
stack_label = Text("Stack", font_size=20).next_to(stack_rect, UP)

heap_rect = Rectangle(height=3, width=4, fill_opacity=0.2, fill_color=RED)
heap_rect.move_to(RIGHT*3.5)
heap_label = Text("Heap", font_size=20).next_to(heap_rect, UP)

# Display memory areas
self.play(
    Create(stack_rect), Write(stack_label),
    Create(heap_rect), Write(heap_label)
)
self.wait(1)

# Create variables on the stack
int_var = Rectangle(height=0.5, width=1.5, fill_opacity=0.8, fill_color=BLUE_C)
int_var.move_to(stack_rect.get_center() + UP*1)
int_label = Text("int x = 5", font_size=16).next_to(int_var, RIGHT)

pointer_var = Rectangle(height=0.5, width=1.5, fill_opacity=0.8, fill_color=BLUE_D)
pointer_var.move_to(stack_rect.get_center())
pointer_label = Text("int* ptr", font_size=16).next_to(pointer_var, RIGHT)

# Display stack variables
self.play(FadeIn(int_var), Write(int_label))
self.wait(0.5)
self.play(FadeIn(pointer_var), Write(pointer_label))
self.wait(1)

# Create heap allocation
heap_alloc = Rectangle(height=0.8, width=2, fill_opacity=0.8, fill_color=RED_C)
heap_alloc.move_to(heap_rect.get_center() + UP*0.5)
heap_label = Text("new int[4]", font_size=16).next_to(heap_alloc, LEFT)

# Display heap allocation
self.play(FadeIn(heap_alloc), Write(heap_label))
self.wait(1)

# Create arrow from pointer to heap
arrow = Arrow(pointer_var.get_right(), heap_alloc.get_left(), buff=0.2, color=YELLOW)
self.play(Create(arrow))
self.wait(0.5)

# Simulate pointer assignment
assign_text = Text("ptr = new int[4]", font_size=24).to_edge(DOWN)
self.play(Write(assign_text))
self.wait(1)

# Simulate memory deallocation
delete_text = Text("delete[] ptr", font_size=24).to_edge(DOWN)
self.play(Transform(assign_text, delete_text))
self.play(FadeOut(arrow), FadeOut(heap_alloc), FadeOut(heap_label))
self.wait(1)

# Simulate end of scope
end_scope = Text("End of scope", font_size=24).to_edge(DOWN)
self.play(Transform(assign_text, end_scope))
self.play(FadeOut(int_var), FadeOut(int_label), FadeOut(pointer_var), FadeOut(pointer_label))
self.wait(2)
"""
                            if st.session_state.code:
                                st.session_state.code += "\n" + memory_code
                            else:
                                st.session_state.code = f"""from manim import *
class CppMemoryModelScene(Scene):
    def construct(self):
        {memory_code}
"""
                            st.session_state.temp_code = st.session_state.code
                            st.success("Added C++ memory model visualization to your Manim code!")
                            st.session_state.pending_tab_switch = 0  # Switch to editor tab
                            st.rerun()
                                
        # C++ Information and tips
        with st.expander("C/C++ Runner Information"):
            st.markdown("""
            ### C/C++ Runner Tips
            
            **Compilation Options:**
            - Choose the appropriate compiler based on your platform
            - Select the C++ standard version for your code
            - Optimization levels affect performance and debugging
            
            **Library Support:**
            - Common libraries like Eigen, OpenCV, and Boost are supported
            - Add custom include paths and library paths as needed
            - Use the library detection feature to find installed libraries
            
            **Input/Output:**
            - Standard input/output (cin/cout) is fully supported
            - File I/O works within the execution directory
            - For interactive programs, provide input values in advance
            
            **Debugging:**
            - Set breakpoints at specific line numbers
            - Watch variables to track their values
            - Debug with GDB for detailed analysis
            
            **Project Management:**
            - Create multi-file projects with headers and source files
            - Generate CMakeLists.txt for complex projects
            - Download project files as a ZIP archive
            
            **Images and Visualization:**
            - Generate images in PPM, PNG, JPG formats
            - Use OpenCV for more advanced image processing
            - All generated images can be used in Manim animations
            
            **Manim Integration:**
            - Create algorithm visualizations from C++ code
            - Import C++ generated images into Manim scenes
            - Visualize data structures and memory models
            
            **Performance:**
            - Use release mode for best performance
            - Profile your code to identify bottlenecks
            - C++ is ideal for computationally intensive tasks
            """)

    # Help section
    with st.sidebar.expander("ℹ️ Help & Info"):
        st.markdown("""
        ### About Manim Animation Studio
        
        This app allows you to create mathematical animations using Manim, 
        an animation engine for explanatory math videos.
        
        ### Example Code
        
        ```python
        from manim import *
        
        class SimpleExample(Scene):
            def construct(self):
                circle = Circle(color=BLUE)
                self.play(Create(circle))
                
                square = Square(color=RED).next_to(circle, RIGHT)
                self.play(Create(square))
                
                text = Text("Manim Animation").next_to(VGroup(circle, square), DOWN)
                self.play(Write(text))
                
                self.wait(2)
        ```
        """)
    
    # Handle tab switching with session state to prevent refresh loop
    if st.session_state.pending_tab_switch is not None:
        st.session_state.active_tab = st.session_state.pending_tab_switch
        st.session_state.pending_tab_switch = None
        
        # Set tabs active state
        for i, tab in enumerate(tabs):
            if i == st.session_state.active_tab:
                tab.active = True
    
    # Mark first load as complete to prevent unnecessary refreshes
    if not st.session_state.first_load_complete:
        st.session_state.first_load_complete = True

if __name__ == "__main__":
    main()