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 all paths in the flow graph."""
        try:
            results = []
            for path in paths:
                path_results = []
                for role in path:
                    # Get the agent's prompt based on previous results
                    prompt = self._generate_prompt(role, path_results)
                    
                    # Execute the agent's task
                    result = await self._execute_agent_task(role, prompt)
                    path_results.append(result)
                    
                    # Store result in context
                    self.context_manager.add_memory(
                        f"{role.value}_result",
                        result,
                        {"timestamp": datetime.now()}
                    )
                
                results.extend(path_results)
            
            # Store all results in context
            self.context_manager.add_memory(
                "path_results",
                results,
                {"timestamp": datetime.now()}
            )
            
            return results
        except Exception as e:
            logger.error(f"Failed to execute paths: {str(e)}")
            raise
    
    def _generate_prompt(self, role: AgentRole, previous_results: List[str]) -> str:
        """Generate a prompt for an agent based on previous results."""
        requirements = self.context_manager.global_context.get("requirements", "")
        
        # Base prompt with requirements
        prompt = f"Requirements: {requirements}\n\n"
        
        # Add context from previous results
        if previous_results:
            prompt += "Previous work:\n"
            for i, result in enumerate(previous_results):
                prompt += f"{i+1}. {result}\n"
        
        # Add role-specific instructions
        if role == AgentRole.ARCHITECT:
            prompt += "\nAs the Architect, design the high-level system architecture."
        elif role == AgentRole.UI_DESIGNER:
            prompt += "\nAs the UI Designer, create the user interface design."
        elif role == AgentRole.BACKEND_DEVELOPER:
            prompt += "\nAs the Backend Developer, implement the server-side logic."
        elif role == AgentRole.DATABASE_ENGINEER:
            prompt += "\nAs the Database Engineer, design the data model and storage."
        elif role == AgentRole.SECURITY_EXPERT:
            prompt += "\nAs the Security Expert, ensure security best practices."
        elif role == AgentRole.QA_ENGINEER:
            prompt += "\nAs the QA Engineer, create test cases and validation."
        elif role == AgentRole.DEVOPS_ENGINEER:
            prompt += "\nAs the DevOps Engineer, set up deployment and CI/CD."
        elif role == AgentRole.DOCUMENTATION_WRITER:
            prompt += "\nAs the Documentation Writer, create comprehensive documentation."
        
        return prompt
    
    def _compile_results(self) -> str:
        """Compile all results into a final output."""
        try:
            results = []
            
            # Get all results from memory
            for role in AgentRole:
                result = self.context_manager.get_memory(f"{role.value}_result")
                if result:
                    results.append(f"## {role.value}\n{result['value']}\n")
            
            return "\n".join(results)
        except Exception as e:
            logger.error(f"Failed to compile results: {str(e)}")
            raise
    
    async def _execute_agent_task(self, role: AgentRole, prompt: str) -> str:
        """Execute a specific agent's task with the given prompt."""
        try:
            if role == AgentRole.ARCHITECT:
                agent = get_agent("architect")
            elif role == AgentRole.UI_DESIGNER:
                agent = get_agent("ui_designer")
            elif role == AgentRole.BACKEND_DEVELOPER:
                agent = get_agent("backend_developer")
            elif role == AgentRole.DATABASE_ENGINEER:
                agent = get_agent("database_engineer")
            elif role == AgentRole.SECURITY_EXPERT:
                agent = get_agent("security_expert")
            elif role == AgentRole.QA_ENGINEER:
                agent = get_agent("qa_engineer")
            elif role == AgentRole.DEVOPS_ENGINEER:
                agent = get_agent("devops_engineer")
            elif role == AgentRole.DOCUMENTATION_WRITER:
                agent = get_agent("documentation_writer")
            else:
                raise ValueError(f"Unknown agent role: {role}")
            
            # Execute the agent's task
            result = agent.run(prompt)
            
            # Log the execution
            logger.info(f"Agent {role.value} completed task")
            
            return result
        except Exception as e:
            logger.error(f"Agent {role.value} failed: {str(e)}")
            raise

@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_agent_task(self, role: AgentRole, prompt: str) -> str:
        """Execute a specific agent's task with the given prompt."""
        try:
            if role == AgentRole.ARCHITECT:
                agent = get_agent("architect")
            elif role == AgentRole.UI_DESIGNER:
                agent = get_agent("ui_designer")
            elif role == AgentRole.BACKEND_DEVELOPER:
                agent = get_agent("backend_developer")
            elif role == AgentRole.DATABASE_ENGINEER:
                agent = get_agent("database_engineer")
            elif role == AgentRole.SECURITY_EXPERT:
                agent = get_agent("security_expert")
            elif role == AgentRole.QA_ENGINEER:
                agent = get_agent("qa_engineer")
            elif role == AgentRole.DEVOPS_ENGINEER:
                agent = get_agent("devops_engineer")
            elif role == AgentRole.DOCUMENTATION_WRITER:
                agent = get_agent("documentation_writer")
            else:
                raise ValueError(f"Unknown agent role: {role}")
            
            # Execute the agent's task
            result = agent.run(prompt)
            
            # Log the execution
            logger.info(f"Agent {role.value} completed task")
            
            return result
        except Exception as e:
            logger.error(f"Agent {role.value} failed: {str(e)}")
            raise
    
    async def execute_flow(self, requirements: str):
        """Execute the AI Flow and build the project."""
        try:
            # Initialize flow with context
            self.initialize_flow()
            self.global_context["requirements"] = requirements
            self.context_manager.global_context.update(self.global_context)
            
            # Add memory of requirements
            self.context_manager.add_memory(
                "requirements",
                requirements,
                {"timestamp": datetime.now()}
            )
            
            # Get all paths through the flow graph
            paths = list(nx.all_simple_paths(
                self.flow_graph,
                AgentRole.ARCHITECT,
                AgentRole.DOCUMENTATION_WRITER
            ))
            
            # Execute paths with enhanced context
            await self._execute_paths(paths)
            
            # Extract app name
            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)
            
            # Compile results
            results = self._compile_results()
            
            # 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
        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()

class StreamHandler:
    """Handles streaming output for the Gradio interface."""
    
    def __init__(self):
        self.output = []
        self.current_status = ""
    
    def update(self, message: str, status: str = None):
        """Update the output stream."""
        self.output.append(message)
        if status:
            self.current_status = status
        return "\n".join(self.output), self.current_status

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as ui:
    stream_handler = StreamHandler()
    
    gr.Markdown("# Autonomous App Generator with AI Flow")
    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
    
    ### Example:
    ```
    Create a personal task management application with:
    - User authentication (email/password, Google OAuth)
    - Task management (CRUD, priorities, due dates, reminders)
    - Modern UI with dark/light theme
    - Real-time updates using WebSocket
    - PostgreSQL and Redis for storage
    ```
    """)
    
    with gr.Row():
        with gr.Column(scale=4):
            requirements_input = gr.Textbox(
                label="App Requirements",
                placeholder="Describe the app you want to build...",
                lines=10
            )
            with gr.Row():
                generate_button = gr.Button("Generate App", variant="primary")
                cancel_button = gr.Button("Cancel", variant="stop")
            
            status = gr.Textbox(
                label="Status",
                value="Ready",
                interactive=False
            )
            
        with gr.Column(scale=6):
            with gr.Tabs():
                with gr.TabItem("Output"):
                    output = gr.Markdown(
                        label="Generated App Details",
                        value="Your app details will appear here..."
                    )
                with gr.TabItem("Download"):
                    file_output = gr.File(
                        label="Download Generated App",
                        interactive=False
                    )
                with gr.TabItem("Live Log"):
                    log_output = gr.Markdown(
                        label="Generation Logs",
                        value="Logs will appear here...",
                        autoscroll=True
                    )
    
    def stream_output(requirements, progress=gr.Progress()):
        """Stream the output during app generation."""
        try:
            # Initialize
            stream_handler.update("Starting app generation...", "Initializing")
            yield "Starting...", None, "Starting app generation...", "Initializing"
            
            # Update progress
            for i in progress.tqdm(range(5)):
                if i == 0:
                    msg = "Analyzing requirements..."
                    stream_handler.update(msg, "Analyzing")
                    yield None, None, stream_handler.output[-1], "Analyzing"
                elif i == 1:
                    msg = "Generating architecture..."
                    stream_handler.update(msg, "Designing")
                    yield None, None, stream_handler.output[-1], "Designing"
                elif i == 2:
                    msg = "Creating project structure..."
                    stream_handler.update(msg, "Creating")
                    yield None, None, stream_handler.output[-1], "Creating"
                elif i == 3:
                    msg = "Implementing features..."
                    stream_handler.update(msg, "Implementing")
                    yield None, None, stream_handler.output[-1], "Implementing"
                elif i == 4:
                    msg = "Finalizing..."
                    stream_handler.update(msg, "Finalizing")
                    yield None, None, stream_handler.output[-1], "Finalizing"
                time.sleep(1)  # Simulate work
            
            # Generate the app
            result = asyncio.run(app_generator(requirements))
            
            # Extract download path and logs
            download_path = None
            logs = []
            
            for line in result.split('\n'):
                if line.startswith("To download your project, use this path:"):
                    download_path = line.split(": ")[1].strip()
                if line.startswith("2025-") or line.startswith("INFO") or line.startswith("ERROR"):
                    logs.append(line)
                    stream_handler.update(line)
                    yield None, None, "\n".join(stream_handler.output), "Processing"
            
            if download_path and Path(download_path).exists():
                stream_handler.update(" App generated successfully!", "Complete")
                yield result, download_path, "\n".join(stream_handler.output), "Complete"
            else:
                error_msg = " Failed to generate download file"
                stream_handler.update(error_msg, "Error")
                yield result, None, "\n".join(stream_handler.output), "Error"
                
        except Exception as e:
            error_msg = f" Failed to generate app: {str(e)}"
            logger.error(error_msg)
            stream_handler.update(error_msg, "Error")
            yield error_msg, None, "\n".join(stream_handler.output), "Error"
    
    def cancel_generation():
        """Cancel the current generation process."""
        stream_handler.update(" Generation cancelled by user", "Cancelled")
        return "Generation cancelled", None, "\n".join(stream_handler.output), "Cancelled"
    
    generate_button.click(
        stream_output,
        inputs=[requirements_input],
        outputs=[output, file_output, log_output, status],
        show_progress=True
    )
    
    cancel_button.click(
        cancel_generation,
        outputs=[output, file_output, log_output, status]
    )

# Run the Gradio app
if __name__ == "__main__":
    try:
        ui.launch(
            share=True,  # Enable sharing
            server_name="0.0.0.0",
            server_port=7860,
            show_error=True,
            enable_queue=True
        )
    except Exception as e:
        logger.error(f"Failed to launch Gradio interface: {str(e)}")