app.py

import gradio as gr
import json
import logging
from enum import Enum, auto
from typing import Protocol, List, Dict, Any
from dataclasses import dataclass, field
from datetime import datetime
import difflib
import pytest
from concurrent.futures import ThreadPoolExecutor
import asyncio

# Initialize logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class Config:
    """Configuration class for the agent system"""
    rag_system_path: str
    max_workers: int = 10
    log_level: str = "INFO"
    model_settings: Dict[str, Any] = field(default_factory=dict)
    api_keys: Dict[str, str] = field(default_factory=dict)

    def __post_init__(self):
        """Validate configuration after initialization"""
        if not hasattr(self, 'rag_system_path'):
            raise ValueError("RAG system path must be specified in config")

class RAGSystem:
    """Retrieval Augmented Generation System"""
    def __init__(self, config: Config):
        self.config = config
        self.model_settings = config.model_settings

    async def generate_reasoning(self, prompt: str) -> str:
        """Generate reasoning based on the provided prompt"""
        try:
            # Placeholder for actual RAG implementation
            return f"Generated reasoning for: {prompt}"
        except Exception as e:
            logger.error(f"Error in RAG system: {e}")
            raise

class AgentRole(Enum):
    ARCHITECT = auto()
    FRONTEND = auto()
    BACKEND = auto()
    DATABASE = auto()
    TESTER = auto()
    REVIEWER = auto()
    DEPLOYER = auto()

@dataclass
class AgentDecision:
    agent: 'Agent'
    decision: str
    confidence: float
    reasoning: str
    timestamp: datetime = field(default_factory=datetime.now)
    dependencies: List['AgentDecision'] = field(default_factory=list)

class AgentProtocol(Protocol):
    async def decide(self, context: Dict[str, Any]) -> AgentDecision: ...
    async def validate(self, decision: AgentDecision) -> bool: ...
    async def implement(self, decision: AgentDecision) -> Any: ...
    async def test(self, implementation: Any) -> bool: ...

@dataclass
class Agent:
    role: AgentRole
    name: str
    autonomy_level: float  # 0-10
    expertise: List[str]
    confidence_threshold: float = 0.7
    rag_system: RAGSystem = None

    async def reason(self, context: Dict[str, Any]) -> str:
        """Generate reasoning based on context and expertise"""
        if not self.rag_system:
            raise ValueError("RAG system not initialized")
            
        prompt = f"""
        As {self.name}, a {self.role.name} expert with expertise in {', '.join(self.expertise)},
        analyze the following context and provide reasoning:

        Context:
        {json.dumps(context, indent=2)}

        Consider:
        1. Required components and their interactions
        2. Potential challenges and solutions
        3. Best practices and patterns
        4. Security and performance implications

        Reasoning:
        """
        return await self.rag_system.generate_reasoning(prompt)

    async def decide(self, context: Dict[str, Any]) -> AgentDecision:
        """Make a decision based on context and expertise"""
        reasoning = await self.reason(context)
        confidence = 0.8  # Placeholder for actual confidence calculation
        
        return AgentDecision(
            agent=self,
            decision=f"Decision based on {reasoning}",
            confidence=confidence,
            reasoning=reasoning
        )

class AgentSystem:
    def __init__(self, config: Config):
        self.config = config
        self.autonomy_level = 0.0  # 0-10
        self.rag_system = RAGSystem(config)
        self.agents: Dict[AgentRole, Agent] = self._initialize_agents()
        self.decision_history: List[AgentDecision] = []
        self.executor = ThreadPoolExecutor(max_workers=config.max_workers)
        self.validator = AgentValidator()
        self.tester = AgentTester()

    def _initialize_agents(self) -> Dict[AgentRole, Agent]:
        agents = {
            AgentRole.ARCHITECT: Agent(
                role=AgentRole.ARCHITECT,
                name="System Architect",
                autonomy_level=self.autonomy_level,
                expertise=["system design", "architecture patterns", "integration"]
            ),
            AgentRole.FRONTEND: Agent(
                role=AgentRole.FRONTEND,
                name="Frontend Developer",
                autonomy_level=self.autonomy_level,
                expertise=["UI/UX", "React", "Vue", "Angular"]
            ),
            AgentRole.BACKEND: Agent(
                role=AgentRole.BACKEND,
                name="Backend Developer",
                autonomy_level=self.autonomy_level,
                expertise=["API design", "database", "security"]
            ),
            AgentRole.TESTER: Agent(
                role=AgentRole.TESTER,
                name="Quality Assurance",
                autonomy_level=self.autonomy_level,
                expertise=["testing", "automation", "quality assurance"]
            ),
            AgentRole.REVIEWER: Agent(
                role=AgentRole.REVIEWER,
                name="Code Reviewer",
                autonomy_level=self.autonomy_level,
                expertise=["code quality", "best practices", "security"]
            ),
        }
        
        # Initialize RAG system for each agent
        for agent in agents.values():
            agent.rag_system = self.rag_system
            
        return agents

    async def set_autonomy_level(self, level: float) -> None:
        """Update autonomy level for all agents"""
        self.autonomy_level = max(0.0, min(10.0, level))
        for agent in self.agents.values():
            agent.autonomy_level = self.autonomy_level

    async def process_request(self, description: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
        """Process a user request with current autonomy level"""
        try:
            context = context or {}
            context['description'] = description
            context['autonomy_level'] = self.autonomy_level

            # Start with architect's decision
            arch_decision = await self.agents[AgentRole.ARCHITECT].decide(context)
            self.decision_history.append(arch_decision)

            if self.autonomy_level < 3:
                # Low autonomy: Wait for user confirmation
                return {
                    'status': 'pending_confirmation',
                    'decision': arch_decision,
                    'next_steps': self._get_next_steps(arch_decision)
                }

            # Medium to high autonomy: Proceed with implementation
            implementation_plan = await self._create_implementation_plan(arch_decision)

            if self.autonomy_level >= 7:
                # High autonomy: Automatic implementation and testing
                return await self._automated_implementation(implementation_plan)

            # Medium autonomy: Return plan for user review
            return {
                'status': 'pending_review',
                'plan': implementation_plan,
                'decisions': self.decision_history
            }

        except Exception as e:
            logger.error(f"Error in request processing: {e}")
            return {'status': 'error', 'message': str(e)}

    async def _create_implementation_plan(self, arch_decision: AgentDecision) -> Dict[str, Any]:
        """Create detailed implementation plan based on architect's decision"""
        tasks = []

        # Frontend tasks
        if 'frontend' in arch_decision.decision.lower():
            tasks.append(self._create_frontend_tasks(arch_decision))

        # Backend tasks
        if 'backend' in arch_decision.decision.lower():
            tasks.append(self._create_backend_tasks(arch_decision))

        # Testing tasks
        tasks.append(self._create_testing_tasks(arch_decision))

        return {
            'tasks': await asyncio.gather(*tasks),
            'dependencies': arch_decision.dependencies,
            'estimated_time': self._estimate_implementation_time(tasks)
        }

    async def _create_frontend_tasks(self, arch_decision: AgentDecision) -> Dict[str, Any]:
        """Create frontend implementation tasks"""
        return {
            'type': 'frontend',
            'components': [],  # Add component definitions
            'dependencies': arch_decision.dependencies
        }

    async def _create_backend_tasks(self, arch_decision: AgentDecision) -> Dict[str, Any]:
        """Create backend implementation tasks"""
        return {
            'type': 'backend',
            'endpoints': [],  # Add endpoint definitions
            'dependencies': arch_decision.dependencies
        }

    async def _create_testing_tasks(self, arch_decision: AgentDecision) -> Dict[str, Any]:
        """Create testing tasks"""
        return {
            'type': 'testing',
            'test_cases': [],  # Add test case definitions
            'dependencies': arch_decision.dependencies
        }

    def _estimate_implementation_time(self, tasks: List[Dict[str, Any]]) -> float:
        """Estimate implementation time based on tasks"""
        return sum(len(task.get('components', [])) + len(task.get('endpoints', [])) 
                  for task in tasks) * 2.0  # hours per task

    async def _automated_implementation(self, plan: Dict[str, Any]) -> Dict[str, Any]:
        """Execute implementation plan automatically"""
        results = {
            'frontend': None,
            'backend': None,
            'tests': None,
            'review': None
        }

        try:
            # Parallel implementation of frontend and backend
            impl_tasks = []
            if 'frontend' in plan['tasks']:
                impl_tasks.append(self._implement_frontend(plan['tasks']['frontend']))
            if 'backend' in plan['tasks']:
                impl_tasks.append(self._implement_backend(plan['tasks']['backend']))

            implementations = await asyncio.gather(*impl_tasks)

            # Testing
            test_results = await self.agents[AgentRole.TESTER].test(implementations)

            # Code review
            review_results = await self.agents[AgentRole.REVIEWER].validate({
                'implementations': implementations,
                'test_results': test_results
            })

            return {
                'status': 'completed',
                'implementations': implementations,
                'test_results': test_results,
                'review': review_results,
                'decisions': self.decision_history
            }

        except Exception as e:
            return {
                'status': 'error',
                'message': str(e),
                'partial_results': results
            }

    async def _implement_frontend(self, tasks: Dict[str, Any]) -> Dict[str, Any]:
        """Implement frontend components"""
        return {'components': [], 'status': 'implemented'}

    async def _implement_backend(self, tasks: Dict[str, Any]) -> Dict[str, Any]:
        """Implement backend components"""
        return {'endpoints': [], 'status': 'implemented'}

    def _get_next_steps(self, decision: AgentDecision) -> List[str]:
        """Get next steps based on decision"""
        return [
            f"Review {decision.decision}",
            "Provide feedback on the proposed approach",
            "Approve or request changes"
        ]

    async def _handle_implementation_failure(self, error: Exception, context: Dict[str, Any]) -> Dict[str, Any]:
        """Handle implementation failures with adaptive response"""
        try:
            # Analyze error
            error_analysis = await self.agents[AgentRole.REVIEWER].reason({
                'error': str(error),
                'context': context
            })

            # Determine correction strategy
            if self.autonomy_level >= 8:
                # High autonomy: Attempt automatic correction
                correction = await self._attempt_automatic_correction(error_analysis)
                if correction['success']:
                    return await self.process_request(context['description'], correction['context'])

            return {
                'status': 'failure',
                'error': str(error),
                'analysis': error_analysis,
                'suggested_corrections': self._suggest_corrections(error_analysis)
            }

        except Exception as e:
            logger.error(f"Error handling implementation failure: {e}")
            return {'status': 'critical_error', 'message': str(e)}

    async def _attempt_automatic_correction(self, error_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Attempt to automatically correct implementation issues"""
        return {
            'success': False,
            'context': {},
            'message': 'Automatic correction not implemented'
        }

    def _suggest_corrections(self, error_analysis: Dict[str, Any]) -> List[str]:
        """Generate suggested corrections based on error analysis"""
        return [
            "Review error details",
            "Check implementation requirements",
            "Verify dependencies"
        ]

class AgentTester:
    def __init__(self):
        self.test_suites = {
            'frontend': self._test_frontend,
            'backend': self._test_backend,
            'integration': self._test_integration
        }

    async def _test_frontend(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Run frontend tests"""
        results = {
            'passed': [],
            'failed': [],
            'warnings': []
        }

        # Component rendering tests
        for component in implementation.get('components', []):
            try:
                # Test component rendering
                result = await self._test_component_render(component)
                if result['success']:
                    results['passed'].append(f"Component {component['name']} renders correctly")
                else:
                    results['failed'].append(f"Component {component['name']}: {result['error']}")
            except Exception as e:
                results['failed'].append(f"Error testing {component['name']}: {str(e)}")

        return results

    async def _test_backend(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Run backend tests"""
        results = {
            'passed': [],
            'failed': [],
            'warnings': []
        }

        # API endpoint tests
        for endpoint in implementation.get('endpoints', []):
            try:
                # Test endpoint functionality
                result = await self._test_endpoint(endpoint)
                if result['success']:
                    results['passed'].append(f"Endpoint {endpoint['path']} works correctly")
                else:
                    results['failed'].append(f"Endpoint {endpoint['path']}: {result['error']}")
            except Exception as e:
                results['failed'].append(f"Error testing {endpoint['path']}: {str(e)}")

        return results

    async def _test_integration(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Run integration tests"""
        results = {
            'passed': [],
            'failed': [],
            'warnings': []
        }

        # Test frontend-backend integration
        try:
            result = await self._test_frontend_backend_integration(implementation)
            if result['success']:
                results['passed'].append("Frontend-Backend integration successful")
            else:
                results['failed'].append(f"Integration error: {result['error']}")
        except Exception as e:
            results['failed'].append(f"Integration test error: {str(e)}")

        return results

    async def _test_component_render(self, component: Dict[str, Any]) -> Dict[str, Any]:
        """Test component rendering"""
        # Placeholder for actual component rendering test
        return {'success': True, 'error': None}

    async def _test_endpoint(self, endpoint: Dict[str, Any]) -> Dict[str, Any]:
        """Test endpoint functionality"""
        # Placeholder for actual endpoint test
        return {'success': True, 'error': None}

    async def _test_component_render(self, component: Dict[str, Any]) -> Dict[str, Any]:
        """Test component rendering"""
        # Placeholder for actual component rendering test
        return {'success': True, 'error': None}

    async def _test_endpoint(self, endpoint: Dict[str, Any]) -> Dict[str, Any]:
        """Test endpoint functionality"""
        # Placeholder for actual endpoint test
        return {'success': True, 'error': None}

        async def _test_frontend_backend_integration(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Test frontend-backend integration"""
        # Placeholder for actual integration test
        return {'success': True, 'error': None}


class AgentValidator:
    def __init__(self):
        self.validators = {
            'code_quality': self._validate_code_quality,
            'security': self._validate_security,
            'performance': self._validate_performance
        }

    async def _validate_code_quality(self, code: str) -> Dict[str, Any]:
        """Validate code quality metrics"""
        results = {
            'passed': [],
            'failed': [],
            'warnings': []
        }
        
        # Add code quality validation logic here
        return results

    async def _validate_security(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Validate security best practices"""
        results = {
            'passed': [],
            'failed': [],
            'warnings': []
        }
        
        # Add security validation logic here
        return results

    async def _validate_performance(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Validate performance metrics"""
        results = {
            'passed': [],
            'failed': [],
            'warnings': []
        }
        
        # Add performance validation logic here
        return results

    async def validate(self, implementation: Dict[str, Any]) -> Dict[str, Any]:
        """Run all validators on the implementation"""
        results = {
            'code_quality': await self._validate_code_quality(implementation.get('code', '')),
            'security': await self._validate_security(implementation),
            'performance': await self._validate_performance(implementation)
        }
        return results


# Example usage
if __name__ == "__main__":
    async def main():
        config = Config(
            rag_system_path="/path/to/rag",
            max_workers=10,
            log_level="INFO",
            model_settings={},
            api_keys={}
        )

        agent_system = AgentSystem(config)
        await agent_system.set_autonomy_level(5.0)

        result = await agent_system.process_request(
            description="Create a new web application",
            context={"requirements": ["user authentication", "dashboard", "API"]}
        )
        print(json.dumps(result, indent=2))

    # Run the async main function
    asyncio.run(main())