definitions.py

import platform
import streamlit as st
import psutil
from typing import List, Dict, Optional, Any, Tuple
from dataclasses import dataclass
from enum import Enum
import logging
import time
import ast
import pylint.lint
import radon.complexity
import radon.metrics
from pylint.lint import Run
from pylint.reporters import JSONReporter
from coverage import Coverage
import bandit
from bandit.core import manager
from datetime import datetime
import os
import sys
import requests
import asyncio
import statistics
import json
import traceback
from pathlib import Path



# Set logging level from environment variable
logging.basicConfig(level=os.getenv('LOG_LEVEL', 'INFO'))

class CodeMetricsAnalyzer:
    """Analyzes code metrics using various tools"""

    def __init__(self):
        self.metrics_history = []

    def analyze_code_quality(self, file_path: str) -> Dict[str, Any]:
        """Analyzes code quality using multiple metrics"""
        try:
            # Pylint analysis
            pylint_score = self._run_pylint(file_path)

            # Complexity analysis
            complexity_score = self._analyze_complexity(file_path)

            # Test coverage analysis
            coverage_score = self._analyze_test_coverage(file_path)
            # Security analysis
            security_score = self._analyze_security(file_path)

            # Calculate overall quality score
            quality_score = self._calculate_overall_score(
                pylint_score,
                complexity_score,
                coverage_score,
                security_score
            )

            metrics = {
                "quality_score": quality_score,
                "pylint_score": pylint_score,
                "complexity_score": complexity_score,
                "coverage_score": coverage_score,
                "security_score": security_score,
                "timestamp": datetime.now()
            }

            self.metrics_history.append(metrics)
            return metrics

        except Exception as e:
            logging.error(f"Error analyzing code metrics: {str(e)}")
            return {
                "error": str(e),
                "quality_score": 0.0,
                "timestamp": datetime.now()
            }

    def _run_pylint(self, file_path: str) -> float:
        """Runs pylint analysis"""
        try:
            reporter = JSONReporter()
            Run([file_path], reporter=reporter, do_exit=False)
            score = reporter.data.get('score', 0.0)
            return float(score) / 10.0  # Normalize to 0-1 scale
        except Exception as e:
            logging.error(f"Pylint analysis error: {str(e)}")
            return 0.0

    def _analyze_complexity(self, file_path: str) -> float:
        """Analyzes code complexity"""
        try:
            with open(file_path, 'r') as file:
                code = file.read()

            # Calculate cyclomatic complexity
            complexity = radon.complexity.cc_visit(code)
            avg_complexity = sum(item.complexity for item in complexity) / len(complexity) if complexity else 0

            # Normalize complexity score (0-1 scale, lower is better)
            normalized_score = 1.0 - min(avg_complexity / 10.0, 1.0)
            return normalized_score

        except Exception as e:
            logging.error(f"Complexity analysis error: {str(e)}")
            return 0.0

    async def _analyze_current_state(self, project_name: str) -> Dict[str, Any]:
        """Analyze current project state with detailed metrics."""
        try:
            self.logger.info(f"Analyzing current state for project: {project_name}")

            # Collect code metrics
            code_metrics = await self._collect_code_metrics(project_name)
            self.logger.info("Code metrics collected successfully.")

            # Analyze test coverage
            test_coverage = await self._analyze_test_coverage(project_name)
            self.logger.info("Test coverage analysis completed.")

            # Check security vulnerabilities
            security_analysis = await self._analyze_security(project_name)
            self.logger.info("Security analysis completed.")

            # Measure performance metrics
            performance_metrics = await self._measure_performance(project_name)
            self.logger.info("Performance metrics measured.")

            # Determine if requirements are met
            meets_requirements = await self._check_requirements(
                code_metrics,
                test_coverage,
                security_analysis,
                performance_metrics
            )
            self.logger.info("Requirements check completed.")

            return {
                "code_metrics": code_metrics,
                "test_coverage": test_coverage,
                "security_analysis": security_analysis,
                "performance_metrics": performance_metrics,
                "meets_requirements": meets_requirements,
                "timestamp": datetime.now()
            }

        except Exception as e:
            self.logger.error(f"Error analyzing current state: {str(e)}")
            raise

    def _analyze_security(self, file_path: str) -> float:
        """Analyzes code security using bandit"""
        try:
            conf = manager.BanditManager()
            conf.discover_files([file_path])
            conf.run_tests()

            # Calculate security score based on findings
            total_issues = len(conf.get_issue_list())
            max_severity = max((issue.severity for issue in conf.get_issue_list()), default=0)

            # Normalize security score (0-1 scale, higher is better)
            security_score = 1.0 - (total_issues * max_severity) / 10.0
            return max(0.0, min(1.0, security_score))

        except Exception as e:
            logging.error(f"Security analysis error: {str(e)}")
            return 0.0

    def _calculate_overall_score(self, pylint_score: float, complexity_score: float,
                               coverage_score: float, security_score: float) -> float:
        """Calculates overall code quality score"""
        weights = {
            'pylint': 0.3,
            'complexity': 0.2,
            'coverage': 0.25,
            'security': 0.25
        }

        overall_score = (
            weights['pylint'] * pylint_score +
            weights['complexity'] * complexity_score +
            weights['coverage'] * coverage_score +
            weights['security'] * security_score
        )

        return max(0.0, min(1.0, overall_score))

    def get_metrics_history(self) -> List[Dict[str, Any]]:
        """Returns the history of metrics measurements"""
        return self.metrics_history

def get_trend_analysis(self) -> Dict[str, Any]:
    """Analyzes trends in metrics over time"""
    if not self.metrics_history:
        return {"status": "No metrics history available"}

    trends = {
        "quality_score": self._calculate_trend([m["quality_score"] for m in self.metrics_history]),
        "coverage_score": self._calculate_trend([m["coverage_score"] for m in self.metrics_history]),
        "security_score": self._calculate_trend([m["security_score"] for m in self.metrics_history])
    }

    return trends

def _calculate_trend(self, values: List[float]) -> Dict[str, Any]:
    """Calculates trend statistics for a metric"""
    if not values:
        return {"trend": "unknown", "change": 0.0}

    recent_values = values[-3:]  # Look at last 3 measurements
    if len(recent_values) < 2:
        return {"trend": "insufficient data", "change": 0.0}

    change = recent_values[-1] - recent_values[0]
    trend = "improving" if change > 0 else "declining" if change < 0 else "stable"

    return {
        "trend": trend,
        "change": change,
        "current": recent_values[-1],
        "previous": recent_values[0]
    }

class AutonomousAgentApp:
    """Main application class for the Autonomous Agent System"""

    def __init__(self):
        self.workspace_manager = self.WorkspaceManager(workspace_dir=os.getenv('WORKSPACE_DIR', 'workspace'))  # Use self.WorkspaceManager
        self.pipeline = self._initialize_pipeline()
        self.refinement_loop = self.RefinementLoop(pipeline=self.pipeline)  # Use self.RefinementLoop
        self.interface = self.StreamlitInterface(self)  # Use self.StreamlitInterface

    def _initialize_pipeline(self) -> 'AutonomousAgentApp.DevelopmentPipeline':
        """Initialize the development pipeline"""
        return self.DevelopmentPipeline(
            workspace_manager=self.workspace_manager,
            tool_manager=self._setup_tool_manager()
        )

    def _setup_tool_manager(self):
        """Setup tool manager with configuration"""
        return self.ToolManager()  # Use self.ToolManager

    class ChatSystem:
        """Manages the chat interaction between users and the autonomous system"""
        
        def __init__(self, agent: 'AutonomousAgentApp.AutonomousAgent'):
            self.agent = agent
            self.chat_history = []
            self.active_tasks = {}
            self.command_handlers = {
                '/task': self.handle_task_command,
                '/status': self.handle_status_command,
                '/stop': self.handle_stop_command,
                '/help': self.handle_help_command,
                '/modify': self.handle_modify_command
            }

        def render_chat_interface(self):
            """Render the chat interface in Streamlit sidebar"""
            with st.sidebar:
                st.markdown("---")
                st.subheader("System Chat")
            
                # Chat controls
                if st.button("Clear Chat History"):
                    self.clear_chat_history()

                # Chat history display
                chat_container = st.container()
                with chat_container:
                    for message in self.chat_history:
                        self._render_message(message)
                            
                # Input area
                user_input = st.text_input("Type message/command...", key="chat_input")
                if st.button("Send", key="send_message"):
                    self.process_user_input(user_input)
                
    class RefinementLoop:
        """Manages the iterative refinement process"""
        
        def __init__(self, pipeline):
            self.pipeline = pipeline
            self.max_iterations = 10
            self.quality_metrics = QualityMetrics()
            self.logger = logging.getLogger(__name__)
            self.current_iteration = 0
            self.history = []

        async def run_refinement_cycle(self, task: str) -> Dict[str, Any]:
            """Run a complete refinement cycle for the given task"""
            self.logger.info(f"Starting refinement cycle for task: {task}")
            self.current_iteration = 0
            
            try:
                while self.current_iteration < self.max_iterations:
                    self.logger.info(f"Starting iteration {self.current_iteration + 1}")
                    
                    # Execute pipeline stages
                    planning_result = await self.pipeline.execute_stage(
                        self.pipeline.PipelineStage.PLANNING,
                        {"task": task}
                    )
                    
                    development_result = await self.pipeline.execute_stage(
                        self.pipeline.PipelineStage.DEVELOPMENT,
                        planning_result["result"]
                    )
                    
                    testing_result = await self.pipeline.execute_stage(
                        self.pipeline.PipelineStage.TESTING,
                        development_result["result"]
                    )
                    
                    # Analyze results
                    quality_analysis = self._analyze_quality(testing_result["result"])
                    
                    # Record iteration history
                    self.history.append({
                        "iteration": self.current_iteration,
                        "quality_metrics": quality_analysis,
                        "timestamp": datetime.now()
                    })
                    
                    # Check if quality requirements are met
                    if self._meets_quality_requirements(quality_analysis):
                        self.logger.info("Quality requirements met. Refinement cycle complete.")
                        return self._prepare_final_result(quality_analysis)
                    
                    self.current_iteration += 1
                
                return {
                    "status": "max_iterations_reached",
                    "iterations_completed": self.current_iteration,
                    "final_quality": quality_analysis
                }
                
            except Exception as e:
                self.logger.error(f"Error in refinement cycle: {str(e)}")
                return {"status": "error", "error": str(e)}

        def _analyze_quality(self, result: Dict[str, Any]) -> Dict[str, float]:
            """Analyze the quality metrics of the current iteration"""
            return {
                "code_quality": self.quality_metrics.code_quality_score,
                "test_coverage": self.quality_metrics.test_coverage,
                "security_score": float(self.quality_metrics.security_score)
            }

        def _meets_quality_requirements(self, quality_analysis: Dict[str, float]) -> bool:
            """Check if the current quality metrics meet the requirements"""
            thresholds = self.quality_metrics.thresholds
            return (
                quality_analysis["code_quality"] >= thresholds["code_quality"] and
                quality_analysis["test_coverage"] >= thresholds["test_coverage"] and
                quality_analysis["security_score"] >= thresholds["security"]
            )

        def _prepare_final_result(self, quality_analysis: Dict[str, float]) -> Dict[str, Any]:
            """Prepare the final result of the refinement cycle"""
            return {
                "status": "success",
                "iterations_completed": self.current_iteration,
                "final_quality": quality_analysis,
                "history": self.history
            }

        def get_refinement_history(self) -> List[Dict[str, Any]]:
            """Get the history of refinement iterations"""
            return self.history    
    
    def run(self):
        """Main application entry point"""
        try:
            logging.info("Starting Autonomous Agent Application")
            self.interface.render_main_interface()
        except Exception as e:
            logging.error(f"Application error: {str(e)}")
            st.error("An error occurred while starting the application. Please check the logs.")
            raise
            
    @dataclass
    class QualityMetrics:
        """Advanced quality metrics tracking and analysis"""
        metrics_analyzer: CodeMetricsAnalyzer = None
        code_quality_score: float = 0.0
        test_coverage: float = 0.0
        security_score: str = "unknown"
        performance_score: float = 0.0
        metrics_analyzer: CodeMetricsAnalyzer = None

        def __post_init__(self):
            self.metrics_analyzer = CodeMetricsAnalyzer()
            self.history = []
            self.thresholds = {
                    "code_quality": 0.85,
                    "test_coverage":     0.90,
                    "security": 0.85,
                    "performance": 0.80
                }

    class CodeAnalyzer:
        def __init__(self):
            self.history = []
            self.code_quality_score = 0.0
            self.test_coverage = 0.0
            self.security_score = "0.0"

        def _get_project_files(self, project_name: str) -> list:
            # Dummy implementation for example purposes
            return ["file1.py", "file2.py"]

        def analyze_code(self, project_name: str) -> Dict[str, Any]:
            """Comprehensive code analysis"""
            try:
                # Get all Python files in the project
                project_files = self._get_project_files(project_name)
        
                aggregated_metrics = {
                    "code_quality": 0.0,
                    "test_coverage": 0.0,
                    "security": 0.0,
                    "performance": 0.0,
                    "files_analyzed": len(project_files),
                    "detailed_metrics": []
                }

                for file_path in project_files:
                    metrics = self.metrics_analyzer.analyze_code_quality(file_path)
                    aggregated_metrics["detailed_metrics"].append({
                        "file": file_path,
                        "metrics": metrics
                    })

                    # Update aggregated scores
                    aggregated_metrics["code_quality"] += metrics["quality_score"]
                    aggregated_metrics["test_coverage"] += metrics["coverage_score"]
                    aggregated_metrics["security"] += metrics["security_score"]

                # Calculate averages
                if project_files:
                    for key in ["code_quality", "test_coverage", "security"]:
                        aggregated_metrics[key] /= len(project_files)

                # Update instance variables
                self.code_quality_score = aggregated_metrics["code_quality"]
                self.test_coverage = aggregated_metrics["test_coverage"]
                self.security_score = str(aggregated_metrics["security"])

                # Add to history
                self.history.append({
                    "timestamp": datetime.now(),
                    "metrics": aggregated_metrics
                })

                return aggregated_metrics

            except Exception as e:
                print(f"An error occurred: {e}")
                return {}

            except Exception as e:
                logging.error(f"Error in code analysis: {str(e)}")
                return {
                    "error": str(e),
                    "code_quality": 0.0,
                    "test_coverage": 0.0,
                    "security": "error",
                    "performance": 0.0
                }

        def _get_project_files(self, project_name: str) -> List[str]:
            """Get all Python files in the project"""
            project_dir = os.path.join(os.getcwd(), project_name)
            python_files = []

            for root, _, files in os.walk(project_dir):
                for file in files:
                    if file.endswith('.py'):
                        python_files.append(os.path.join(root, file))

            return python_files

        def get_improvement_suggestions(self) -> List[str]:
            """Generate improvement suggestions based on metrics"""
            suggestions = []
            latest_metrics = self.history[-1]["metrics"] if self.history else None

            if not latest_metrics:
                 return ["No metrics available for analysis"]

            if latest_metrics["code_quality"] < self.thresholds["code_quality"]:
                suggestions.append(
                    f"Code quality score ({latest_metrics['code_quality']:.2f}) is below threshold "
                    f"({self.thresholds['code_quality']}). Consider refactoring complex methods."
                )

            if latest_metrics["test_coverage"] < self.thresholds["test_coverage"]:
                suggestions.append(
                    f"Test coverage ({latest_metrics['test_coverage']:.2f}) is below threshold "
                    f"({self.thresholds['test_coverage']}). Add more unit tests."
                )

            if float(latest_metrics["security"]) < self.thresholds["security"]:
                suggestions.append(
                    f"Security score ({latest_metrics['security']}) is below threshold "
                    f"({self.thresholds['security']}). Address security vulnerabilities."
                )

            return suggestions

    class StreamlitInterface:
        """Streamlit UI integration for the Autonomous Agent system."""
        
        def main():
            autonomous_agent_app = AutonomousAgentApp()
            app.run()

        def __init__(self, app: 'AutonomousAgentApp'):  # Use string forward reference
            self.app = app
            self.chat_system = self.app.ChatSystem(self.app.autonomous_agent)

        def render_main_interface(self):
            """Render the main Streamlit interface."""
            st.title("Autonomous Agent System")

            # Add chat interface to sidebar
            self.chat_system.render_chat_interface()

            # Main content tabs
            tab_names = ["Autonomous Agent", "Workspace Management", "Settings"]
            selected_tab = st.selectbox("Select a Tab", tab_names)

            if selected_tab == "Autonomous Agent":
                self.render_autonomous_agent_tab()
            elif selected_tab == "Workspace Management":
                self.render_workspace_management_tab()
            elif selected_tab == "Settings":
                self.render_settings_tab()

        def render_autonomous_agent_tab(self):
            """Render the Autonomous Agent tab."""
            st.header("Autonomous Agent")
            task = st.text_area("Enter a task for the autonomous agent:")

            if st.button("Run Autonomous Agent"):
                if task:
                    # Run the autonomous agent with the provided task
                    try:
                        result = asyncio.run(self.app.refinement_loop.run_refinement_cycle(task))
                        st.success(f"Result: {result}")
                    except Exception as e:
                        st.error(f"An error occurred: {str(e)}")

        def render_workspace_management_tab(self):
            """Render the Workspace Management tab with a workspace explorer."""
            st.header("Workspace Management")

            # Workspace Explorer
            st.subheader("Workspace Explorer")
            workspace_tree = self.app.workspace_manager.get_workspace_tree()
            self._render_tree(workspace_tree)

            # File creation
            st.subheader("Create a File")
            new_filename = st.text_input("Enter filename:")
            new_file_content = st.text_area("Enter file content:")
            if st.button("Create File"):
                if new_filename and new_file_content:
                    result = self.app.workspace_manager.create_file(new_filename, new_file_content)
                    st.success(result)
                else:
                    st.error("Filename and content are required.")

            # File deletion
            st.subheader("Delete a File")
            delete_filename = st.text_input("Enter filename to delete:")
            if st.button("Delete File"):
                if delete_filename:
                    result = self.app.workspace_manager.delete_file(delete_filename)
                    st.success(result)
                else:
                    st.error("Filename is required.")

        def _render_tree(self, tree: Dict[str, Any], level: int = 0):
            """Recursively render the workspace directory tree."""
            if tree["type"] == "file":
                st.write("  " * level + f"📄 {tree['name']}")
            elif tree["type"] == "directory":
                st.write("  " * level + f"📁 {tree['name']}")
                for child in tree["children"]:
                    self._render_tree(child, level + 1)

        def render_settings_tab(self):
            """Render the Settings tab."""
            st.header("Application Settings")

            # Section 1: Refinement Process Configuration
            st.subheader("Refinement Process Settings")

            # Adjust maximum refinement iterations
            current_max_iter = self.app.refinement_loop.max_iterations
            new_max_iter = st.number_input(
                "Maximum Refinement Iterations",
                min_value=1,
                max_value=20,
                value=current_max_iter,
                help="Maximum number of refinement cycles to perform"
            )
            if new_max_iter != current_max_iter:
                self.app.refinement_loop.max_iterations = new_max_iter
                st.success(f"Updated maximum iterations to {new_max_iter}")

            # Section 2: Quality Threshold Configuration
            st.subheader("Quality Thresholds")

            # Get current thresholds
            thresholds = self.app.refinement_loop.quality_metrics.thresholds

            col1, col2, col3 = st.columns(3)
            with col1:
                new_code_quality = st.slider(
                    "Code Quality Threshold",
                    0.0, 1.0, thresholds["code_quality"],
                    help="Minimum acceptable code quality score"
                )
            with col2:
                new_test_coverage = st.slider(
                    "Test Coverage Threshold",
                    0.0, 1.0, thresholds["test_coverage"],
                    help="Minimum required test coverage"
                )
            with col3:
                new_security = st.slider(
                    "Security Threshold",
                    0.0, 1.0, thresholds["security"],
                    help="Minimum acceptable security score"
                )

            if st.button("Update Quality Thresholds"):
                self.app.refinement_loop.quality_metrics.thresholds.update({
                    "code_quality": new_code_quality,
                    "test_coverage": new_test_coverage,
                    "security": new_security
                })
                st.success("Quality thresholds updated!")

            # Section 3: Performance Configuration
            st.subheader("Performance Settings")

            # Concurrency settings
            concurrency_level = st.selectbox(
                "Max Concurrency",
                options=[1, 2, 4, 8],
                index=2,
                help="Maximum parallel tasks for code analysis"
            )

            # Resource limits
            mem_limit = st.slider(
                "Memory Limit (GB)",
                1, 16, 4,
                help="Maximum memory allocation for pipeline operations"
            )

            # Section 4: Security Settings
            st.subheader("Security Configuration")

            # Security rules toggle
            enable_security_scan = st.checkbox(
                "Enable Real-time Security Scanning",
                value=True,
                help="Perform continuous security analysis during development"
            )

            # Severity level filtering
            security_level = st.selectbox(
                "Minimum Security Severity Level",
                ["Low", "Medium", "High", "Critical"],
                index=1,
                help="Minimum severity level to trigger security alerts"
            )

            # Section 5: Workspace Configuration
            st.subheader("Workspace Settings")
            current_workspace = self.app.workspace_manager.workspace_dir
            st.write(f"Current Workspace: `{current_workspace}`")

            # Workspace actions
            if st.button("Clear Workspace Cache"):
                self.app.workspace_manager.clean_cache()
                st.success("Workspace cache cleared!")

            # Section 6: Diagnostic Settings
            st.subheader("Diagnostics")

            # Logging controls
            log_level = st.selectbox(
                "Logging Level",
                ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
                index=1
            )
            st.session_state.log_level = log_level  # Store in session state
            logging.getLogger().setLevel(log_level)

            # Debug mode toggle
            debug_mode = st.checkbox("Enable Debug Mode")
            st.session_state.debug_mode = debug_mode  # Store in session state
            if debug_mode:
                self.app.refinement_loop.logger.setLevel(logging.DEBUG)
            else:
                self.app.refinement_loop.logger.setLevel(logging.INFO)

            # Section 7: System Information
            st.subheader("System Info")
            st.write(f"Python Version: {sys.version}")
            st.write(f"Platform: {platform.platform()}")
            st.write(f"Available Memory: {psutil.virtual_memory().available / (1024**3):.1f} GB free")
if __name__ == "__main__":
    
    app.launch()