app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain_community.llms import HuggingFacePipeline
from langchain.agents import initialize_agent, Tool
from langchain.prompts import PromptTemplate
import json
import subprocess
import logging
import asyncio
from typing import Dict, List, Optional
from dataclasses import dataclass
from enum import Enum
import networkx as nx
from pathlib import Path
from datetime import datetime
from typing import Set, Union, Any
import hashlib
import os
import json
from dataclasses import asdict, field
import shutil
import tempfile
from zipfile import ZipFile

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# Load the LLM and tokenizer
MODEL_NAME = "unit-mesh/autodev-coder-deepseek-6.7b-finetunes"

def load_model():
    try:
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
        # Check available device and memory
        if torch.cuda.is_available():
            device = "cuda"
            # Get available GPU memory
            gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3  # Convert to GB
            if gpu_memory < 8:  # If less than 8GB available
                logger.warning("Limited GPU memory available. Using CPU instead.")
                device = "cpu"
        else:
            device = "cpu"
            logger.info("No GPU detected. Using CPU.")
        
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
            device_map="auto" if device == "cuda" else None,
            low_cpu_mem_usage=True
        )
        return tokenizer, model
    except Exception as e:
        logger.error(f"Failed to load model: {str(e)}")
        raise RuntimeError(f"Model initialization failed: {str(e)}")

# Initialize models lazily
tokenizer = None
model = None
hf_pipeline = None
llm = None

def get_llm():
    global tokenizer, model, hf_pipeline, llm
    if llm is None:
        tokenizer, model = load_model()
        hf_pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            max_length=500,
            temperature=0.7,
        )
        llm = HuggingFacePipeline(pipeline=hf_pipeline)
    return llm

# Lazy initialization of agents
def get_agent(agent_type):
    llm = get_llm()
    return initialize_agent(
        tools=[
            Tool(
                name="Code Formatter",
                func=lambda x: subprocess.run(["black", "-"], input=x.encode(), capture_output=True).stdout.decode(),
                description="Formats code using Black.",
            ),
            Tool(
                name="API Generator",
                func=lambda x: json.dumps({"endpoints": {"example": "POST - Example endpoint."}}),
                description="Generates API details from code.",
            ),
            Tool(
                name="Task Decomposer",
                func=lambda x: json.dumps({"tasks": ["Design UI", "Develop Backend", "Test App", "Deploy App"]}),
                description="Breaks down app requirements into smaller tasks.",
            ),
        ],
        llm=llm,
        agent="zero-shot-react-description",
        verbose=True,
    )

# Enhanced prompt templates with more specific instructions
ui_designer_prompt = PromptTemplate(
    input_variables=["input"],
    template="""You are an expert UI Designer specializing in modern, responsive web applications.
Task: {input}

Focus on:
1. Clean, intuitive user interface
2. Responsive design principles
3. Modern UI components
4. Accessibility standards
5. Cross-browser compatibility

Generate code using:
- HTML5 semantic elements
- Modern CSS (Flexbox/Grid)
- React/Vue.js best practices
- Material UI or Tailwind CSS

Provide detailed component structure and styling."""
)

backend_developer_prompt = PromptTemplate(
    input_variables=["input"],
    template="""You are an expert Backend Developer specializing in scalable applications.
Task: {input}

Focus on:
1. RESTful API design
2. Database schema optimization
3. Security best practices
4. Error handling
5. Performance optimization

Include:
- API endpoint definitions
- Database models
- Authentication/Authorization
- Input validation
- Error handling middleware
- Rate limiting
- Logging

Use modern backend frameworks (FastAPI/Django/Express)."""
)

qa_engineer_prompt = PromptTemplate(
    input_variables=["input"],
    template="""You are an expert QA Engineer focusing on comprehensive testing.
Task: {input}

Implement:
1. Unit tests
2. Integration tests
3. API endpoint tests
4. UI component tests
5. Performance tests

Include:
- Test cases for edge cases
- Input validation tests
- Error handling tests
- Load testing scenarios
- Security testing checks"""
)

devops_engineer_prompt = PromptTemplate(
    input_variables=["input"],
    template="""You are an expert DevOps Engineer specializing in modern deployment practices.
Task: {input}

Provide:
1. Dockerfile configuration
2. Docker Compose setup
3. CI/CD pipeline configuration
4. Environment configuration
5. Monitoring setup

Include:
- Development/Production configs
- Environment variables
- Health checks
- Logging setup
- Monitoring integration
- Backup strategies"""
)

def generate_project_structure(app_name, features):
    """Generate a complete project structure based on features."""
    return f"""
 {app_name}/
├──  frontend/
│   ├──  src/
│   │   ├──  components/
│   │   ├──  pages/
│   │   ├──  hooks/
│   │   ├──  utils/
│   │   └──  styles/
│   ├── package.json
│   └── README.md
├──  backend/
│   ├──  src/
│   │   ├──  routes/
│   │   ├──  controllers/
│   │   ├──  models/
│   │   ├──  middleware/
│   │   └──  utils/
│   ├── requirements.txt
│   └── README.md
├──  tests/
│   ├──  unit/
│   ├──  integration/
│   └──  e2e/
├──  docs/
│   ├── API.md
│   ├── SETUP.md
│   └── DEPLOYMENT.md
├── docker-compose.yml
├── .env.example
└── README.md
"""

def generate_documentation(app_name, features, api_details):
    """Generate comprehensive documentation."""
    return f"""
# {app_name}

## Overview
A modern web application with the following features:
{features}

## Quick Start
```bash
# Clone the repository
git clone <repository-url>

# Install dependencies
cd {app_name}
# Frontend
cd frontend && npm install
# Backend
cd ../backend && pip install -r requirements.txt

# Run the application
docker-compose up
```

## API Documentation
{api_details}

## Development
- Frontend: React.js with TypeScript
- Backend: Python with FastAPI
- Database: PostgreSQL
- Cache: Redis
- Testing: Jest, Pytest

## Deployment
Includes Docker configuration for easy deployment:
- Frontend container
- Backend container
- Database container
- Redis container

## Testing
```bash
# Run frontend tests
cd frontend && npm test

# Run backend tests
cd backend && pytest
```

## Contributing
Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

## License
This project is licensed under the MIT License - see the LICENSE.md file for details
"""

# AI Flow States and Types
class FlowState(Enum):
    PENDING = "pending"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"

class AgentRole(Enum):
    ARCHITECT = "architect"
    UI_DESIGNER = "ui_designer"
    BACKEND_DEVELOPER = "backend_developer"
    DATABASE_ENGINEER = "database_engineer"
    SECURITY_EXPERT = "security_expert"
    QA_ENGINEER = "qa_engineer"
    DEVOPS_ENGINEER = "devops_engineer"
    DOCUMENTATION_WRITER = "documentation_writer"

@dataclass
class AgentContext:
    """Context information for each agent in the flow."""
    role: AgentRole
    state: FlowState
    artifacts: Dict[str, str]
    dependencies: List[AgentRole]
    feedback: List[str]

class AIFlow:
    """Manages the flow of work between different AI agents."""
    
    def __init__(self):
        self.flow_graph = nx.DiGraph()
        self.contexts: Dict[AgentRole, AgentContext] = {}
        self.global_context = {}
        
    def initialize_flow(self):
        """Initialize the AI Flow with agent relationships and dependencies."""
        # Define agent relationships
        flow_structure = {
            AgentRole.ARCHITECT: [AgentRole.UI_DESIGNER, AgentRole.BACKEND_DEVELOPER, AgentRole.DATABASE_ENGINEER],
            AgentRole.UI_DESIGNER: [AgentRole.QA_ENGINEER],
            AgentRole.BACKEND_DEVELOPER: [AgentRole.SECURITY_EXPERT, AgentRole.QA_ENGINEER],
            AgentRole.DATABASE_ENGINEER: [AgentRole.SECURITY_EXPERT],
            AgentRole.SECURITY_EXPERT: [AgentRole.QA_ENGINEER],
            AgentRole.QA_ENGINEER: [AgentRole.DEVOPS_ENGINEER],
            AgentRole.DEVOPS_ENGINEER: [AgentRole.DOCUMENTATION_WRITER],
            AgentRole.DOCUMENTATION_WRITER: []
        }
        
        # Build the flow graph
        for role, dependencies in flow_structure.items():
            self.flow_graph.add_node(role)
            for dep in dependencies:
                self.flow_graph.add_edge(role, dep)
            
            # Initialize context for each agent
            self.contexts[role] = AgentContext(
                role=role,
                state=FlowState.PENDING,
                artifacts={},
                dependencies=dependencies,
                feedback=[]
            )

    async def execute_flow(self, requirements: str):
        """Execute the AI Flow with parallel processing where possible."""
        try:
            self.initialize_flow()
            self.global_context["requirements"] = requirements
            
            # Get all paths through the flow graph
            paths = list(nx.all_simple_paths(
                self.flow_graph,
                AgentRole.ARCHITECT,
                AgentRole.DOCUMENTATION_WRITER
            ))
            
            # Execute paths in parallel
            await self._execute_paths(paths)
            
            return self._compile_results()
            
        except Exception as e:
            logger.error(f"Flow execution failed: {str(e)}")
            raise

    async def _execute_paths(self, paths: List[List[AgentRole]]):
        """Execute multiple paths through the flow in parallel."""
        path_tasks = [self._execute_path(path) for path in paths]
        await asyncio.gather(*path_tasks)

    async def _execute_path(self, path: List[AgentRole]):
        """Execute a single path through the flow."""
        for role in path:
            context = self.contexts[role]
            if context.state != FlowState.COMPLETED:
                await self._execute_agent(role)

    async def _execute_agent(self, role: AgentRole):
        """Execute a single agent's tasks."""
        context = self.contexts[role]
        context.state = FlowState.RUNNING
        
        try:
            # Get agent-specific prompt
            prompt = self._get_agent_prompt(role)
            
            # Execute agent's task
            if role == AgentRole.ARCHITECT:
                result = await self._execute_architect(prompt)
            elif role == AgentRole.UI_DESIGNER:
                result = await self._execute_ui_designer(prompt)
            elif role == AgentRole.BACKEND_DEVELOPER:
                result = await self._execute_backend_developer(prompt)
            # ... (similar for other roles)
            
            # Store results in context
            context.artifacts["output"] = result
            context.state = FlowState.COMPLETED
            
        except Exception as e:
            context.state = FlowState.FAILED
            context.feedback.append(str(e))
            raise

    def _get_agent_prompt(self, role: AgentRole) -> str:
        """Get the appropriate prompt template for each agent role."""
        prompts = {
            AgentRole.ARCHITECT: """You are a Software Architect designing a scalable application.
Requirements: {requirements}

Focus on:
1. System architecture
2. Component interactions
3. Technology stack selection
4. Scalability considerations
5. Integration patterns

Provide:
- High-level architecture diagram
- Component breakdown
- Technology recommendations
- Integration patterns
- Performance considerations""",
            # ... (other role-specific prompts)
        }
        
        base_prompt = prompts.get(role, "")
        return base_prompt.format(**self.global_context)

    async def _execute_architect(self, prompt: str) -> str:
        """Execute the Architect agent's tasks."""
        agent = get_agent("architect")
        return agent.run(prompt)

    async def _execute_ui_designer(self, prompt: str) -> str:
        """Execute the UI Designer agent's tasks."""
        agent = get_agent("ui_designer")
        return agent.run(prompt)

    # ... (similar methods for other roles)

    def _compile_results(self) -> str:
        """Compile the results from all agents into a final output."""
        results = []
        for role, context in self.contexts.items():
            if context.state == FlowState.COMPLETED:
                results.append(f"## {role.value.replace('_', ' ').title()} Output")
                results.append(context.artifacts.get("output", "No output available"))
                results.append("")
        
        return "\n".join(results)

@dataclass
class FileContext:
    """Context for file operations and tracking."""
    path: Path
    content: str
    last_modified: datetime
    dependencies: Set[Path]
    checksum: str
    
    @classmethod
    def from_path(cls, path: Path):
        content = path.read_text()
        return cls(
            path=path,
            content=content,
            last_modified=datetime.fromtimestamp(path.stat().st_mtime),
            dependencies=set(),
            checksum=hashlib.md5(content.encode()).hexdigest()
        )

@dataclass
class MemoryItem:
    """Represents a single memory item in the system."""
    key: str
    value: Any
    context: dict
    timestamp: datetime
    importance: float = 1.0
    references: Set[str] = field(default_factory=set)

class ContextManager:
    """Manages real-time context awareness across the system."""
    
    def __init__(self):
        self.file_contexts: Dict[Path, FileContext] = {}
        self.global_context: Dict[str, Any] = {}
        self.command_history: List[Dict] = []
        self.memory_store: Dict[str, MemoryItem] = {}
        
    def update_file_context(self, path: Path) -> FileContext:
        """Update context for a specific file."""
        context = FileContext.from_path(path)
        self.file_contexts[path] = context
        return context
    
    def get_related_files(self, path: Path) -> Set[Path]:
        """Find files related to the given file."""
        if path not in self.file_contexts:
            self.update_file_context(path)
        
        context = self.file_contexts[path]
        return context.dependencies
    
    def track_command(self, command: str, args: List[str], result: Any):
        """Track command execution and results."""
        self.command_history.append({
            'command': command,
            'args': args,
            'result': result,
            'timestamp': datetime.now(),
        })
    
    def add_memory(self, key: str, value: Any, context: dict = None):
        """Add an item to the memory store."""
        self.memory_store[key] = MemoryItem(
            key=key,
            value=value,
            context=context or {},
            timestamp=datetime.now()
        )
    
    def get_memory(self, key: str) -> Any:
        """Retrieve an item from memory."""
        item = self.memory_store.get(key)
        return item.value if item else None

class FileOperationManager:
    """Manages multi-file operations and tracking."""
    
    def __init__(self, context_manager: ContextManager):
        self.context_manager = context_manager
        self.pending_changes: Dict[Path, str] = {}
        
    async def edit_files(self, changes: Dict[Path, str]):
        """Apply changes to multiple files atomically."""
        try:
            # Validate all changes first
            for path, content in changes.items():
                if not self._validate_change(path, content):
                    raise ValueError(f"Invalid change for {path}")
            
            # Apply changes
            for path, content in changes.items():
                await self._apply_change(path, content)
                
            # Update contexts
            for path in changes:
                self.context_manager.update_file_context(path)
                
        except Exception as e:
            logger.error(f"Failed to apply multi-file changes: {str(e)}")
            raise
    
    def _validate_change(self, path: Path, content: str) -> bool:
        """Validate a proposed file change."""
        try:
            # Check file exists or can be created
            if not path.parent.exists():
                path.parent.mkdir(parents=True)
            
            # Validate syntax if it's a Python file
            if path.suffix == '.py':
                compile(content, str(path), 'exec')
                
            return True
        except Exception as e:
            logger.error(f"Validation failed for {path}: {str(e)}")
            return False
    
    async def _apply_change(self, path: Path, content: str):
        """Apply a single file change."""
        path.write_text(content)

class CommandManager:
    """Manages command suggestions and execution."""
    
    def __init__(self, context_manager: ContextManager):
        self.context_manager = context_manager
        self.command_templates: Dict[str, str] = {}
        
    def suggest_commands(self, context: dict) -> List[Dict]:
        """Suggest relevant commands based on context."""
        suggestions = []
        for cmd_name, template in self.command_templates.items():
            if self._is_relevant(cmd_name, context):
                suggestions.append({
                    'command': cmd_name,
                    'template': template,
                    'confidence': self._calculate_confidence(cmd_name, context)
                })
        return sorted(suggestions, key=lambda x: x['confidence'], reverse=True)
    
    async def execute_command(self, command: str, args: List[str]) -> Any:
        """Execute a command and track its result."""
        try:
            # Execute the command
            result = await self._run_command(command, args)
            
            # Track the execution
            self.context_manager.track_command(command, args, result)
            
            return result
        except Exception as e:
            logger.error(f"Command execution failed: {str(e)}")
            raise
    
    def _is_relevant(self, cmd_name: str, context: dict) -> bool:
        """Determine if a command is relevant to the current context."""
        # Implementation depends on specific rules
        return True
    
    def _calculate_confidence(self, cmd_name: str, context: dict) -> float:
        """Calculate confidence score for a command suggestion."""
        # Implementation depends on specific metrics
        return 1.0

class RuleSystem:
    """Manages system rules and constraints."""
    
    def __init__(self):
        self.rules: Dict[str, callable] = {}
        self.constraints: Dict[str, callable] = {}
        
    def add_rule(self, name: str, rule_func: callable):
        """Add a new rule to the system."""
        self.rules[name] = rule_func
        
    def add_constraint(self, name: str, constraint_func: callable):
        """Add a new constraint to the system."""
        self.constraints[name] = constraint_func
        
    def evaluate_rules(self, context: dict) -> Dict[str, bool]:
        """Evaluate all rules against the current context."""
        return {name: rule(context) for name, rule in self.rules.items()}
    
    def check_constraints(self, context: dict) -> Dict[str, bool]:
        """Check all constraints against the current context."""
        return {name: constraint(context) for name, constraint in self.constraints.items()}

class ProjectBuilder:
    """Handles autonomous creation of project files and folders."""
    
    def __init__(self, base_path: Path):
        self.base_path = Path(base_path)
        self.current_build = None
        self.file_manifest = []
    
    async def create_project(self, app_name: str, structure: dict) -> Path:
        """Create a new project with the specified structure."""
        try:
            # Create temporary build directory
            build_dir = Path(tempfile.mkdtemp())
            self.current_build = build_dir / app_name
            self.current_build.mkdir(parents=True)
            
            # Create project structure
            await self._create_structure(self.current_build, structure)
            
            return self.current_build
        except Exception as e:
            logger.error(f"Project creation failed: {str(e)}")
            if self.current_build and self.current_build.exists():
                shutil.rmtree(self.current_build)
            raise
    
    async def _create_structure(self, parent: Path, structure: dict):
        """Recursively create project structure."""
        for name, content in structure.items():
            path = parent / name
            if isinstance(content, dict):
                path.mkdir(exist_ok=True)
                await self._create_structure(path, content)
            else:
                path.write_text(str(content))
                self.file_manifest.append(path)

class OutputManager:
    """Manages project outputs and creates downloadable artifacts."""
    
    def __init__(self, project_builder: ProjectBuilder):
        self.project_builder = project_builder
        self.output_dir = Path(tempfile.mkdtemp())
        self.downloads = {}
    
    def create_download(self, app_name: str) -> str:
        """Create a downloadable zip file of the project."""
        try:
            if not self.project_builder.current_build:
                raise ValueError("No project has been built yet")
            
            # Create zip file
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            zip_name = f"{app_name}_{timestamp}.zip"
            zip_path = self.output_dir / zip_name
            
            with ZipFile(zip_path, 'w') as zipf:
                for file_path in self.project_builder.file_manifest:
                    rel_path = file_path.relative_to(self.project_builder.current_build)
                    zipf.write(file_path, rel_path)
            
            # Store download info
            self.downloads[zip_name] = {
                'path': zip_path,
                'created_at': datetime.now(),
                'size': zip_path.stat().st_size
            }
            
            return str(zip_path)
        except Exception as e:
            logger.error(f"Failed to create download: {str(e)}")
            raise

class EnhancedAIFlow(AIFlow):
    """Enhanced AI Flow with project building and output management."""
    
    def __init__(self):
        super().__init__()
        self.project_builder = ProjectBuilder(Path(tempfile.mkdtemp()))
        self.output_manager = OutputManager(self.project_builder)
        self.context_manager = ContextManager()
        self.file_manager = FileOperationManager(self.context_manager)
        self.command_manager = CommandManager(self.context_manager)
        self.rule_system = RuleSystem()
        
    async def execute_flow(self, requirements: str):
        """Execute the AI Flow and build the project."""
        try:
            # Execute normal flow
            results = await super().execute_flow(requirements)
            
            # Extract app name and generate documentation
            app_name = requirements.split()[0].lower().replace(" ", "_")
            
            # Create basic project structure
            structure = {
                "frontend": {
                    "src": {
                        "components": {},
                        "pages": {},
                        "styles": {},
                    },
                    "package.json": "{\n  \"name\": \"frontend\",\n  \"version\": \"1.0.0\"\n}",
                    "README.md": "# Frontend\n"
                },
                "backend": {
                    "src": {
                        "routes": {},
                        "models": {},
                        "controllers": {},
                    },
                    "requirements.txt": "fastapi\nuvicorn\n",
                    "README.md": "# Backend\n"
                },
                "README.md": f"# {app_name}\nGenerated by AI Flow\n"
            }
            
            # Build the project
            await self.project_builder.create_project(app_name, structure)
            
            # Create download
            download_path = self.output_manager.create_download(app_name)
            
            # Add download information to results
            results += f"""
## Download
Your project has been created and is ready for download:
- File: {Path(download_path).name}
- Size: {self.output_manager.downloads[Path(download_path).name]['size'] / 1024:.1f} KB
- Created: {self.output_manager.downloads[Path(download_path).name]['created_at'].strftime('%Y-%m-%d %H:%M:%S')}

To download your project, use this path: {download_path}

## Project Structure
The following files have been created:
"""
            for file_path in self.project_builder.file_manifest:
                rel_path = file_path.relative_to(self.project_builder.current_build)
                results += f"- {rel_path}\n"
            
            return results
        except Exception as e:
            logger.error(f"Enhanced flow execution failed: {str(e)}")
            raise
        
    async def _execute_agent(self, role: AgentRole):
        """Execute a single agent's tasks with enhanced context."""
        context = self.contexts[role]
        context.state = FlowState.RUNNING
        
        try:
            # Get agent-specific prompt with context
            prompt = self._get_agent_prompt(role)
            
            # Add current context to prompt
            prompt += f"\n\nContext:\n{json.dumps(self.context_manager.global_context, indent=2)}"
            
            # Execute agent's task
            result = await self._execute_agent_task(role, prompt)
            
            # Store results with context
            context.artifacts["output"] = result
            context.state = FlowState.COMPLETED
            
            # Update memory
            self.context_manager.add_memory(
                f"agent_result_{role.value}",
                result,
                {"role": role.value, "timestamp": datetime.now()}
            )
            
        except Exception as e:
            context.state = FlowState.FAILED
            context.feedback.append(str(e))
            raise

# Update the multi_agent_workflow function to use AI Flows
async def multi_agent_workflow(requirements: str) -> str:
    """
    Execute a multi-agent workflow using AI Flows to generate a complex app.
    Args:
        requirements (str): App requirements.
    Returns:
        str: Generated app code and API details.
    """
    try:
        # Initialize and execute AI Flow
        flow = EnhancedAIFlow()
        results = await flow.execute_flow(requirements)
        
        # Extract app name and generate documentation
        app_name = requirements.split()[0].lower().replace(" ", "_")
        
        # Generate project structure and documentation
        project_structure = generate_project_structure(app_name, flow.contexts[AgentRole.ARCHITECT].artifacts)
        documentation = generate_documentation(app_name, requirements, flow.contexts[AgentRole.DOCUMENTATION_WRITER].artifacts)
        
        return f"""
# {app_name.title()} - Generated Application

## Project Structure
```
{project_structure}
```

{results}

## Documentation
{documentation}

## Next Steps
1. Review the generated architecture and components
2. Set up the development environment
3. Implement the components following the provided structure
4. Run the test suite
5. Deploy using the provided configurations

## Support
For any issues or questions, please refer to the documentation or create an issue in the repository.
"""
    except Exception as e:
        error_msg = f"Workflow failed: {str(e)}"
        logger.error(error_msg)
        return error_msg
    finally:
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

# Update the app_generator function to handle async execution
def app_generator(requirements: str):
    """
    Generate an app based on the provided requirements using AI Flows.
    Args:
        requirements (str): App requirements.
    Returns:
        str: Generated app code and API details.
    """
    if not requirements or len(requirements.strip()) == 0:
        return "Please provide app requirements."
    
    try:
        # Run the async workflow in a new event loop
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        return loop.run_until_complete(multi_agent_workflow(requirements))
    except Exception as e:
        error_msg = f"App generation failed: {str(e)}"
        logger.error(error_msg)
        return error_msg
    finally:
        loop.close()

# Gradio UI
with gr.Blocks() as ui:
    gr.Markdown("# Autonomous App Generator with LangChain Agents")
    gr.Markdown("""
    ## Instructions
    1. Describe the app you want to build in detail
    2. Include any specific requirements or features
    3. Click 'Generate App' to start the process
    4. Download your generated app from the provided link
    """)
    
    with gr.Row():
        requirements_input = gr.Textbox(
            label="App Requirements",
            placeholder="Describe the app you want to build...",
            lines=5
        )
        generate_button = gr.Button("Generate App")
    
    with gr.Row():
        output = gr.Textbox(
            label="Generated App Details",
            lines=20
        )
        file_output = gr.File(
            label="Download Generated App",
            interactive=False
        )
    
    def process_output(requirements):
        try:
            # Generate the app
            result = app_generator(requirements)
            
            # Extract download path from the result
            download_path = None
            for line in result.split('\n'):
                if line.startswith("To download your project, use this path:"):
                    download_path = line.split(": ")[1].strip()
                    break
            
            if download_path and Path(download_path).exists():
                return result, download_path
            else:
                return result, None
        except Exception as e:
            error_msg = f"Failed to generate app: {str(e)}"
            logger.error(error_msg)
            return error_msg, None
    
    generate_button.click(
        process_output,
        inputs=[requirements_input],
        outputs=[output, file_output]
    )

# Run the Gradio app
if __name__ == "__main__":
    try:
        ui.launch()
    except Exception as e:
        logger.error(f"Failed to launch Gradio interface: {str(e)}")