Initial commit

README.md

@@ -0,0 +1,34 @@
+# Central AI Hub
+
+This is a Hugging Face Space for the Central AI Hub.
+
+## How to Run
+
+1.  Make sure you have the required dependencies installed. You can install them using:
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+2.  To run the space, execute the following command:
+
+    ```bash
+    python app.py
+    ```
+
+    This will start the Gradio interface, which you can access in your browser.
+
+3.  You can optionally provide a local path to the Qwen2.5-14B GGUF model by setting the `LOCAL_MODEL_PATH` environment variable. For example:
+
+    ```bash
+    LOCAL_MODEL_PATH=/path/to/your/model.gguf python app.py
+    ```
+
+    If the `LOCAL_MODEL_PATH` environment variable is not set, the model will be downloaded from Hugging Face Hub and cached in the `.cache` directory.
+
+## Usage
+
+1.  Select a task type from the dropdown menu.
+2.  Enter the task content in the text box.
+3.  Enter any task requirements, separated by commas.
+4.  Click the "Submit Task" button.
+5.  The task status will be displayed in the output text box.

agents/central_ai_hub_test.py

@@ -0,0 +1,59 @@
+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
+import pytest
+import asyncio
+from core.central_ai_hub import CentralAIHub
+from utils.llm_orchestrator import LLMOrchestrator
+
+@pytest.fixture
+async def hub():
+    """Fixture to create a CentralAIHub instance."""
+    api_key = os.getenv('LLM_API_KEY')
+    hub = CentralAIHub(api_key)
+    await hub.start()
+    yield hub
+    await hub.shutdown()
+
+@pytest.mark.asyncio
+async def test_system_initialization(hub):
+    """Test system initialization and LLM connection."""
+    assert hub.llm_orchestrator is not None
+    assert isinstance(hub.llm_orchestrator, LLMOrchestrator)
+    assert len(hub.agents) > 0
+
+@pytest.mark.asyncio
+async def test_task_delegation(hub):
+    """Test task delegation functionality."""
+    task = {
+        'type': 'code_analysis',
+        'content': 'def hello(): print("Hello, World!")',
+        'requirements': ['code understanding', 'static analysis']
+    }
+    task_id = await hub.delegate_task(task)
+    assert task_id is not None
+    status = await hub.get_task_status(task_id)
+    assert status['status'] in ['active', 'completed']
+
+@pytest.mark.asyncio
+async def test_agent_selection(hub):
+    """Test agent selection logic."""
+    task = {
+        'type': 'code_generation',
+        'content': 'Create a simple REST API',
+        'requirements': ['code generation', 'API design']
+    }
+    agent_id = await hub.select_agent(task)
+    assert agent_id in hub.agents
+
+@pytest.mark.asyncio
+async def test_error_handling(hub):
+    """Test error handling capabilities."""
+    with pytest.raises(ValueError):
+        await hub.initialize_agent('non_existent_agent')
+
+    with pytest.raises(Exception):
+        await hub.delegate_task(None)
+
+if __name__ == '__main__':
+    pytest.main(['-v', 'central_ai_hub_test.py'])

agents/code_analysis_agent.py

@@ -0,0 +1,182 @@
+import ast
+from typing import Dict, Any, List
+from loguru import logger
+import os
+import re
+
+
+class CodeAnalysisAgent:
+    def __init__(self):
+        """Initialize the Code Analysis Agent."""
+        logger.info("Initializing CodeAnalysisAgent")
+        self.capabilities = [
+            "code_analysis",
+            "static_analysis",
+            "repository_analysis",
+            "code_summarization",
+            "vulnerability_detection",
+            "debugging",
+            "bug_detection",
+            "code_smell_detection"
+        ]
+        self.setup_logger()
+
+    async def find_bugs(self, file_path: str) -> List[str]:
+        """Find potential bugs in a code file using static analysis."""
+        logger.info(f"Finding bugs in file {file_path}")
+        bugs = []
+        try:
+            with open(file_path, "r") as f:
+                source_code = f.read()
+
+            tree = ast.parse(source_code)
+
+            # Example: Find unclosed files
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Call) and isinstance(
+                        node.func, ast.Name) and node.func.id == 'open':
+                    if not any(isinstance(parent, ast.With)
+                               for parent in ast.walk(node)):
+                        bugs.append(
+                            f"Line {node.lineno}: Potential unclosed file")
+
+            # Add more bug detection patterns here
+
+        except Exception as e:
+            logger.error(f"Error finding bugs in file {file_path}: {str(e)}")
+
+        return bugs
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/code_analysis_agent.log", rotation="500 MB")
+
+    async def analyze_repository(self, repo_path: str) -> Dict[str, Any]:
+        """Analyze a code repository."""
+        logger.info(f"Analyzing repository at {repo_path}")
+        try:
+            code_files = self.collect_code_files(repo_path)
+            analysis_results = []
+
+            for file_path in code_files:
+                analysis_results.append(await self.analyze_file(file_path))
+
+            summary = self.generate_repository_summary(analysis_results)
+            logger.info(f"Finished analyzing repository at {repo_path}")
+            return {
+                "status": "success",
+                "results": analysis_results,
+                "summary": summary
+            }
+        except Exception as e:
+            logger.error(f"Error analyzing repository {repo_path}: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }
+
+    def collect_code_files(self, repo_path: str) -> List[str]:
+        """Collect all code files in a repository."""
+        code_files = []
+        for root, _, files in os.walk(repo_path):
+            for file in files:
+                if file.endswith(
+                        ".py"):  # Currently only supports Python files
+                    code_files.append(os.path.join(root, file))
+        return code_files
+
+    async def analyze_file(self, file_path: str) -> Dict[str, Any]:
+        """Analyze a single code file."""
+        logger.info(f"Analyzing file {file_path}")
+        try:
+            with open(file_path, "r") as f:
+                source_code = f.read()
+
+            # Basic static analysis using AST
+            tree = ast.parse(source_code)
+
+            # Extract functions and classes
+            functions = [node.name for node in ast.walk(
+                tree) if isinstance(node, ast.FunctionDef)]
+            classes = [node.name for node in ast.walk(
+                tree) if isinstance(node, ast.ClassDef)]
+
+            # Basic complexity analysis (Cyclomatic Complexity)
+            complexity = self.calculate_complexity(tree)
+
+            # Identify potential vulnerabilities (basic example)
+            vulnerabilities = self.detect_vulnerabilities(source_code)
+
+            return {
+                "file_path": file_path,
+                "functions": functions,
+                "classes": classes,
+                "complexity": complexity,
+                "vulnerabilities": vulnerabilities
+            }
+        except Exception as e:
+            logger.error(f"Error analyzing file {file_path}: {str(e)}")
+            return {
+                "file_path": file_path,
+                "error": str(e)
+            }
+
+    def calculate_complexity(self, tree: ast.AST) -> int:
+        """Calculate the Cyclomatic Complexity of a code snippet."""
+        complexity = 1
+        for node in ast.walk(tree):
+            if isinstance(node, (ast.If, ast.While, ast.For,
+                          ast.AsyncFor, ast.With, ast.AsyncWith, ast.Try)):
+                complexity += 1
+        return complexity
+
+    def detect_vulnerabilities(self, source_code: str) -> List[str]:
+        """Detect potential vulnerabilities in the code (basic example)."""
+        vulnerabilities = []
+
+        # Detect SQL injection patterns
+        if re.search(r"SELECT \* FROM .* WHERE .*", source_code):
+            vulnerabilities.append("Potential SQL injection vulnerability")
+
+        # Detect hardcoded credentials
+        if re.search(r"password\s*=\s*['\"].*['\"]",
+                     source_code, re.IGNORECASE):
+            vulnerabilities.append("Potential hardcoded credentials")
+
+        return vulnerabilities
+
+    def detect_code_smells(self, tree: ast.AST) -> List[str]:
+        """Detect potential code smells in the code."""
+        code_smells = []
+        # Example: Long function
+        for node in ast.walk(tree):
+            if isinstance(node, ast.FunctionDef) and len(node.body) > 50:
+                code_smells.append(f"Line {node.lineno}: Long function")
+        # Example: Large class
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ClassDef) and len(node.body) > 100:
+                code_smells.append(f"Line {node.lineno}: Large class")
+        return code_smells
+
+    def generate_repository_summary(
+            self, analysis_results: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Generate a summary of the repository analysis."""
+        total_files = len(analysis_results)
+        total_functions = sum(len(result.get("functions", []))
+                              for result in analysis_results)
+        total_classes = sum(len(result.get("classes", []))
+                            for result in analysis_results)
+        average_complexity = sum(
+            result.get(
+                "complexity",
+                0) for result in analysis_results) / total_files if total_files > 0 else 0
+        total_vulnerabilities = sum(
+            len(result.get("vulnerabilities", [])) for result in analysis_results)
+
+        return {
+            "total_files": total_files,
+            "total_functions": total_functions,
+            "total_classes": total_classes,
+            "average_complexity": average_complexity,
+            "total_vulnerabilities": total_vulnerabilities
+        }

agents/code_generation_agent.py

@@ -0,0 +1,47 @@
+from typing import Dict, Any
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+
+
+class CodeGenerationAgent:
+    def __init__(self, llm_api_key: str):
+        """Initialize the Code Generation Agent."""
+        logger.info("Initializing CodeGenerationAgent")
+        self.llm_orchestrator = LLMOrchestrator(llm_api_key)
+        self.capabilities = [
+            "code_generation",
+            "code_completion",
+            "code_modification",
+            "code_refactoring"
+        ]
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/code_generation_agent.log", rotation="500 MB")
+
+    async def generate_code(self, description: str,
+                            language: str = "python") -> Dict[str, Any]:
+        """Generate code based on a natural language description."""
+        logger.info(f"Generating code for description: {description}")
+        try:
+            prompt = f"""
+            Generate {language} code that accomplishes the following:
+            {description}
+
+            Provide only the code, without any introductory or explanatory text.
+            """
+            code = await self.llm_orchestrator.generate_completion(prompt)
+
+            logger.info(f"Code generated successfully:\n{code}")
+            return {
+                "status": "success",
+                "code": code
+            }
+
+        except Exception as e:
+            logger.error(f"Error generating code: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }

agents/error_fixing_agent.py

@@ -0,0 +1,49 @@
+from typing import Dict, Any, List
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+
+
+class ErrorFixingAgent:
+    def __init__(self, llm_api_key: str):
+        """Initialize the Error Fixing Agent."""
+        logger.info("Initializing ErrorFixingAgent")
+        self.llm_orchestrator = LLMOrchestrator(llm_api_key)
+        self.capabilities = [
+            "error_fixing",
+            "patch_generation",
+            "code_repair"
+        ]
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/error_fixing_agent.log", rotation="500 MB")
+
+    async def fix_errors(
+            self, code: str, error_messages: List[str]) -> Dict[str, Any]:
+        """Generate code patches to fix detected errors."""
+        logger.info(f"Attempting to fix errors: {error_messages}")
+        try:
+            prompt = f"""
+            Fix the following errors in the code:
+            {code}
+
+            Errors:
+            {chr(10).join(error_messages)}
+
+            Provide only the corrected code, without any introductory or explanatory text.
+            """
+            fixed_code = await self.llm_orchestrator.generate_completion(prompt)
+
+            logger.info(f"Code with fixes generated:\n{fixed_code}")
+            return {
+                "status": "success",
+                "fixed_code": fixed_code
+            }
+
+        except Exception as e:
+            logger.error(f"Error fixing code: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }

agents/planning_agent.py

@@ -0,0 +1,86 @@
+from typing import Dict, Any, List
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+
+
+class PlanningAgent:
+    def __init__(self, llm_api_key: str):
+        """Initialize the Planning Agent."""
+        logger.info("Initializing PlanningAgent")
+        self.llm_orchestrator = LLMOrchestrator(llm_api_key)
+        self.capabilities = [
+            "task_planning",
+            "goal_decomposition",
+            "plan_refinement",
+            "task_prioritization"
+        ]
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/planning_agent.log", rotation="500 MB")
+
+    async def generate_plan(
+            self, goal: str, available_agents: List[str]) -> Dict[str, Any]:
+        """Generate a task plan based on a high-level goal."""
+        logger.info(f"Generating plan for goal: {goal}")
+        try:
+            prompt = f"""
+            You are an expert planner. Generate a detailed task plan to achieve the following goal:
+            Goal: {goal}
+
+            Available agents: {', '.join(available_agents)}
+
+            Think step-by-step and explain your reasoning for each step.
+            The plan should be a list of steps, each with:
+            - A clear description of the task.
+            - The agent best suited to execute the task.
+            - Any necessary input or parameters for the task.
+
+            Example:
+            1. Task: Summarize the latest news on topic X.
+               Agent: web_browsing_agent
+               Input: topic=X
+               Reasoning: To get the latest news, we need to use the web_browsing_agent to search for news on topic X.
+            2. Task: Analyze the sentiment of the news summary.
+               Agent: data_analysis_agent
+               Input: summary from step 1
+               Reasoning: To analyze the sentiment, we can use the data_analysis_agent to process the summary from the previous step.
+            """
+            plan_str = await self.llm_orchestrator.generate_completion(prompt)
+            plan = self.parse_plan(plan_str)
+            logger.info(f"Plan generated successfully: {plan}")
+            return {
+                "status": "success",
+                "plan": plan
+            }
+
+        except Exception as e:
+            logger.error(f"Error generating plan: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }
+
+    def parse_plan(self, plan_str: str) -> List[Dict[str, Any]]:
+        """Parse the plan generated by the LLM into a structured format."""
+        plan = []
+        steps = plan_str.strip().split("\n")
+        current_step = {}
+
+        for line in steps:
+            if line.startswith(tuple(f"{i}." for i in range(1, 10))):
+                if current_step:
+                    plan.append(current_step)
+                current_step = {"task": line.split("Task: ")[1]}
+            elif "Agent: " in line:
+                current_step["agent"] = line.split("Agent: ")[1]
+            elif "Input: " in line:
+                current_step["input"] = line.split("Input: ")[1]
+            elif "Reasoning: " in line:
+                current_step["reasoning"] = line.split("Reasoning: ")[1]
+
+        if current_step:
+            plan.append(current_step)
+
+        return plan

agents/q_learning_agent.py

@@ -0,0 +1,72 @@
+import numpy as np
+import random
+from loguru import logger
+from typing import Dict, Any, List
+
+
+class QLearningAgent:
+    def __init__(self, learning_rate: float = 0.1,
+                 discount_factor: float = 0.9, exploration_rate: float = 0.1):
+        """Initialize the Q-Learning Agent."""
+        self.learning_rate = learning_rate
+        self.discount_factor = discount_factor
+        self.exploration_rate = exploration_rate
+        self.q_table = {}  # Initialize Q-table as an empty dictionary
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/q_learning_agent.log", rotation="500 MB")
+
+    def get_q_value(self, state: Dict[str, Any], action: str) -> float:
+        """Get the Q-value for a given state-action pair."""
+        state_key = self.serialize_state(state)
+        if state_key not in self.q_table:
+            self.q_table[state_key] = {}
+
+        return self.q_table[state_key].get(
+            action, 0.0)  # Default Q-value is 0.0
+
+    def set_q_value(self, state: Dict[str, Any], action: str, value: float):
+        """Set the Q-value for a given state-action pair."""
+        state_key = self.serialize_state(state)
+        if state_key not in self.q_table:
+            self.q_table[state_key] = {}
+        self.q_table[state_key][action] = value
+
+    def choose_action(self, state: Dict[str, Any],
+                      available_actions: List[str]) -> str:
+        """Choose an action based on the current state and Q-table."""
+        if random.random() < self.exploration_rate:
+            # Explore: choose a random action
+            return random.choice(available_actions)
+        else:
+            # Exploit: choose the action with the highest Q-value
+            q_values = [self.get_q_value(state, action)
+                        for action in available_actions]
+            max_q = max(q_values)
+            # If multiple actions have the same max Q-value, choose randomly
+            # among them
+            best_actions = [
+                action for action,
+                q in zip(
+                    available_actions,
+                    q_values) if q == max_q]
+            return random.choice(best_actions)
+
+    def update_q_table(self, state: Dict[str, Any], action: str,
+                       reward: float, next_state: Dict[str, Any], next_actions: List[str]):
+        """Update the Q-table based on the observed reward and next state."""
+        current_q = self.get_q_value(state, action)
+        max_next_q = max([self.get_q_value(next_state, next_action)
+                         for next_action in next_actions], default=0)
+        new_q = current_q + self.learning_rate * \
+            (reward + self.discount_factor * max_next_q - current_q)
+        self.set_q_value(state, action, new_q)
+        logger.info(
+            f"Q-table updated for state-action pair: ({self.serialize_state(state)}, {action})")
+
+    def serialize_state(self, state: Dict[str, Any]) -> str:
+        """Serialize the state into a string representation for use as a dictionary key."""
+        # Convert the state dictionary to a string representation
+        return str(state)

agents/reasoning_agent.py

@@ -0,0 +1,135 @@
+from typing import Dict, Any, List
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+
+
+class ReasoningAgent:
+    def __init__(self, llm_api_key: str):
+        """Initialize the Reasoning Agent."""
+        logger.info("Initializing ReasoningAgent")
+        self.llm_orchestrator = LLMOrchestrator(llm_api_key)
+        self.capabilities = [
+            "step_by_step_reasoning",
+            "context_management",
+            "agent_coordination",
+            "result_aggregation"
+        ]
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/reasoning_agent.log", rotation="500 MB")
+
+    async def perform_reasoning(
+            self, goal: str, available_agents: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Perform step-by-step reasoning to achieve a goal."""
+        logger.info(f"Performing reasoning for goal: {goal}")
+        try:
+            context = {
+                "goal": goal,
+                "available_agents": available_agents,
+                "steps": []
+            }
+
+            # Example of a simple reasoning process with 2 steps.
+            # This can be made more sophisticated based on the specific needs.
+            for step in range(2):
+                prompt = self.generate_reasoning_prompt(context)
+                response = await self.llm_orchestrator.generate_completion(prompt)
+
+                logger.info(f"Reasoning step {step + 1}: {response}")
+
+                # Placeholder for action execution based on reasoning
+                action = self.extract_action(response)
+
+                if action and action["agent"] != "reasoning_agent":
+                    # Here we simulate executing an action with another agent
+                    # In a real scenario, this would involve calling the
+                    # appropriate agent
+                    action_result = await self.execute_agent_action(action, context)
+                    context["steps"].append({
+                        "step": step + 1,
+                        "prompt": prompt,
+                        "response": response,
+                        "action": action,
+                        "action_result": action_result
+                    })
+                else:
+                    context["steps"].append({
+                        "step": step + 1,
+                        "prompt": prompt,
+                        "response": response,
+                        "action": action
+                    })
+
+            return {
+                "status": "success",
+                "reasoning_process": context["steps"],
+                "result": "Reasoning process completed."  # Placeholder for final result
+            }
+
+        except Exception as e:
+            logger.error(f"Error during reasoning: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }
+
+    def generate_reasoning_prompt(self, context: Dict[str, Any]) -> str:
+        """Generate a prompt for the LLM to guide the reasoning process."""
+        prompt = f"""
+        Goal: {context['goal']}
+        Available Agents: {', '.join([agent['name'] for agent in context['available_agents']])}
+
+        Reasoning Steps:
+        """
+
+        for step in context["steps"]:
+            prompt += f"- Step {step['step']}: {step['response']}\n"
+            if "action" in step and step["action"]:
+                prompt += f"  Action: {step['action']}\n"
+            if "action_result" in step and step["action_result"]:
+                prompt += f"  Result: {step['action_result']}\n"
+
+        prompt += "What is the next logical step to achieve the goal? Explain your reasoning."
+
+        return prompt
+
+    def extract_action(self, response: str) -> Dict[str, Any]:
+        """Extract the next action to be taken based on the LLM's response."""
+        # Basic implementation: Assume the last line of the response contains
+        # the action
+        lines = response.strip().split("\n")
+        last_line = lines[-1]
+        if ":" in last_line:
+            parts = last_line.split(":")
+            agent = parts[0].strip()
+            parameters = parts[1].strip() if len(parts) > 1 else ""
+            return {
+                "agent": agent,
+                "parameters": parameters
+            }
+        else:
+            return None
+
+    async def execute_agent_action(
+            self, action: Dict[str, Any], context: Dict[str, Any]) -> str:
+        """Simulate executing an action with another agent."""
+        # This is a placeholder for actual agent execution
+        # In a real scenario, this method would call the appropriate agent based on action["agent"]
+        # and pass the necessary parameters from action["parameters"]
+
+        # Find the agent in the available agents list
+        agent_info = next(
+            (agent for agent in context["available_agents"]
+             if agent["name"] == action["agent"]),
+            None)
+
+        if agent_info:
+            logger.info(
+                f"Executing action with agent: {action['agent']} with parameters: {action['parameters']}")
+            # Simulate an action result
+            return f"Result of action with {action['agent']}: Success"
+        else:
+            logger.error(f"Agent {action['agent']} not found.")
+            return "Error: Agent not found"

agents/setup_environment.sh

@@ -0,0 +1,39 @@
+#!/bin/bash
+
+# setup_environment.sh
+echo "Setting up Agentic AI Environment..."
+
+# Create and activate conda environment
+conda create -n agentic_ai python=3.9 -y
+source activate agentic_ai
+
+# Install requirements
+pip install -r requirements.txt
+
+# Create conda env directories
+mkdir -p $CONDA_PREFIX/etc/conda/activate.d
+mkdir -p $CONDA_PREFIX/etc/conda/deactivate.d
+
+# Create activation script
+cat > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh << EOL
+#!/bin/bash
+export OPENAI_API_KEY='your-openai-key-here'
+export SERPAPI_KEY='your-serpapi-key-here'
+# Add more API keys as needed
+EOL
+
+# Create deactivation script
+cat > $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh << EOL
+#!/bin/bash
+unset OPENAI_API_KEY
+unset SERPAPI_KEY
+# Add more unset commands for additional API keys
+EOL
+
+# Make scripts executable
+chmod +x $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
+chmod +x $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
+
+echo "Environment setup complete!"
+echo "Please edit $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh to add your actual API keys"
+echo "Then run: source activate agentic_ai"

agents/test_runner.py

@@ -0,0 +1,36 @@
+import unittest
+import sys
+import io
+import contextlib
+import os
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+def run_tests(test_code):
+    """Execute the generated test cases."""
+    try:
+        # Redirect stdout to capture test results
+        with io.StringIO() as buf, contextlib.redirect_stdout(buf):
+            # Execute the test code
+            exec(test_code, globals())
+
+            # Find test suite and run it
+            for var_name, var_value in globals().items():
+                if isinstance(var_value, type) and issubclass(
+                        var_value, unittest.TestCase):
+                    suite = unittest.TestLoader().loadTestsFromTestCase(var_value)
+                    unittest.TextTestRunner(stream=buf, verbosity=2).run(suite)
+
+            test_output = buf.getvalue()
+
+        print(test_output)
+        return 0  # Return 0 to indicate successful execution
+
+    except Exception as e:
+        print(f"Error executing tests: {str(e)}")
+        return 1  # Return 1 to indicate an error
+
+if __name__ == "__main__":
+    test_code = sys.stdin.read()
+    exit_code = run_tests(test_code)
+    sys.exit(exit_code)

agents/testing_agent.py

@@ -0,0 +1,91 @@
+from typing import Dict, Any, List
+from utils.llm_orchestrator import LLMOrchestrator
+from loguru import logger
+from utils import llm_orchestrator
+import unittest
+import io
+import contextlib
+import asyncio
+import sys
+
+class TestingAgent:
+    def __init__(self, llm_orchestrator: LLMOrchestrator):
+        """Initialize the Testing Agent."""
+        logger.info("Initializing TestingAgent")
+        self.llm_orchestrator = llm_orchestrator
+        self.capabilities = [
+            "test_generation",
+            "test_execution",
+            "test_analysis"
+        ]
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/testing_agent.log", rotation="500 MB")
+
+    async def generate_tests(
+            self, code: str, focus_areas: List[str] = None) -> Dict[str, Any]:
+        """Generate test cases for the given code."""
+        logger.info(f"Generating tests for code:\n{code}")
+        try:
+            prompt = f"""
+            Generate test cases for the following code:
+            ```python
+            {code}
+            ```
+
+            Focus on the following areas:
+            {', '.join(focus_areas) if focus_areas else 'General functionality'}
+
+            Provide the test cases in a format compatible with Python's unittest framework.
+            """
+            test_code = await self.llm_orchestrator.generate_completion(prompt)
+
+            logger.info(f"Test cases generated:\n{test_code}")
+            return {
+                "status": "success",
+                "test_code": test_code
+            }
+
+        except Exception as e:
+            logger.error(f"Error generating tests: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }
+
+    async def execute_tests(self, test_code: str) -> Dict[str, Any]:
+        """Execute the generated test cases in a separate process."""
+        logger.info(f"Executing tests in a separate process:\n{test_code}")
+        try:
+            process = await asyncio.create_subprocess_exec(
+                sys.executable,  # Use the same Python interpreter
+                "agents/test_runner.py",
+                stdin=asyncio.subprocess.PIPE,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.PIPE,
+            )
+
+            stdout, stderr = await process.communicate(input=test_code.encode())
+            test_output = stdout.decode()
+            error_output = stderr.decode()
+
+            if process.returncode == 0:
+                logger.info(f"Test execution completed:\n{test_output}")
+                return {
+                    "status": "success",
+                    "results": test_output
+                }
+            else:
+                logger.error(f"Error executing tests:\n{error_output}")
+                return {
+                    "status": "error",
+                    "message": error_output
+                }
+        except Exception as e:
+            logger.error(f"Error executing tests: {str(e)}")
+            return {
+                "status": "error",
+                "message": str(e)
+            }

agents/web_browsing_agent.py

@@ -0,0 +1,211 @@
+import aiohttp
+from bs4 import BeautifulSoup
+from typing import Dict, Any, List
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+import asyncio
+from urllib.parse import urljoin, urlparse
+
+
+class WebBrowsingAgent:
+    def __init__(self, llm_api_key: str):
+        """Initialize the Web Browsing Agent."""
+        logger.info("Initializing WebBrowsingAgent")
+        self.llm_orchestrator = LLMOrchestrator(llm_api_key)
+        self.session = None
+        self.setup_logger()
+        self.visited_urls = set()
+        self.headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+        }
+        self.capabilities = [
+            "web_browsing",
+            "data_collection",
+            "content_processing",
+            "information_extraction",
+            "link_crawling"
+        ]
+
+    def setup_logger(self):
+        """Configure logging for the agent."""
+        logger.add("logs/web_browsing_agent.log", rotation="500 MB")
+
+    async def initialize(self):
+        """Initialize the aiohttp session."""
+        logger.info("Initializing aiohttp session")
+        if not self.session:
+            self.session = aiohttp.ClientSession(headers=self.headers)
+
+    async def execute(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a web browsing task."""
+        logger.info(f"Executing task: {task}")
+        await self.initialize()
+
+        if 'url' not in task:
+            logger.error("URL not provided in task")
+            raise ValueError("URL not provided in task")
+
+        try:
+            content = await self.collect_data(task['url'])
+            processed_data = await self.process_content(content, task)
+            logger.info(f"Successfully executed task: {task}")
+            return {
+                'status': 'success',
+                'data': processed_data,
+                'url': task['url']
+            }
+        except Exception as e:
+            logger.error(f"Error executing task: {str(e)}")
+            return {
+                'status': 'error',
+                'error': str(e),
+                'url': task['url']
+            }
+
+    async def collect_data(self, url: str, retries: int = 3,
+                           delay: int = 1) -> Dict[str, Any]:
+        """Collect data from a URL with error handling and retries."""
+        for attempt in range(retries):
+            try:
+                async with self.session.get(url) as response:
+                    if response.status == 200:
+                        html = await response.text()
+                        soup = BeautifulSoup(html, 'html.parser')
+
+                        # Extract various types of content
+                        text_content = soup.get_text(separator=' ', strip=True)
+                        links = [
+                            link.get('href') for link in soup.find_all(
+                                'a', href=True)]
+                        images = [
+                            img.get('src') for img in soup.find_all(
+                                'img', src=True)]
+
+                        # Process links to get absolute URLs
+                        processed_links = [urljoin(url, link)
+                                           for link in links]
+
+                        logger.info(f"Successfully collected data from {url}")
+                        return {
+                            'url': url,
+                            'text_content': text_content,
+                            'links': processed_links,
+                            'images': images,
+                            'status_code': response.status,
+                            'headers': dict(response.headers)
+                        }
+                    else:
+                        logger.error(
+                            f"HTTP {response.status}: Failed to fetch {url} on attempt {attempt + 1}")
+                        if attempt < retries - 1:
+                            # Exponential backoff
+                            await asyncio.sleep(delay * (2 ** attempt))
+                        else:
+                            raise Exception(
+                                f"HTTP {response.status}: Failed to fetch {url} after multiple retries")
+            except aiohttp.ClientError as e:
+                logger.error(
+                    f"Network error on attempt {attempt + 1} for {url}: {str(e)}")
+                if attempt < retries - 1:
+                    # Exponential backoff
+                    await asyncio.sleep(delay * (2 ** attempt))
+                else:
+                    raise Exception(
+                        f"Network error: Failed to fetch {url} after multiple retries")
+            except aiohttp.HttpProcessingError as e:
+                logger.error(
+                    f"HTTP processing error on attempt {attempt + 1} for {url}: {str(e)}")
+                if attempt < retries - 1:
+                    # Exponential backoff
+                    await asyncio.sleep(delay * (2 ** attempt))
+                else:
+                    raise Exception(
+                        f"HTTP processing error: Failed to fetch {url} after multiple retries")
+            except Exception as e:
+                logger.error(
+                    f"Unexpected error on attempt {attempt + 1} for {url}: {str(e)}")
+                if attempt < retries - 1:
+                    # Exponential backoff
+                    await asyncio.sleep(delay * (2 ** attempt))
+                else:
+                    raise Exception(
+                        f"Unexpected error: Failed to fetch {url} after multiple retries")
+
+    async def process_content(
+            self, content: Dict[str, Any], task: Dict[str, Any]) -> Dict[str, Any]:
+        """Process collected content using LLM."""
+        logger.info(f"Processing content for {content['url']}")
+        try:
+            # Generate summary of the content
+            summary = await self.llm_orchestrator.generate_completion(
+                f"Summarize the following content:\n{content['text_content'][:1000]}..."
+            )
+
+            # Extract key information based on task requirements
+            extracted_info = await self.extract_relevant_information(content, task)
+
+            logger.info(f"Successfully processed content for {content['url']}")
+            return {
+                'summary': summary,
+                'extracted_info': extracted_info,
+                'metadata': {
+                    'url': content['url'],
+                    'num_links': len(content['links']),
+                    'num_images': len(content['images'])
+                }
+            }
+        except Exception as e:
+            logger.error(f"Error processing content: {str(e)}")
+            raise
+
+    async def extract_relevant_information(
+            self, content: Dict[str, Any], task: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract relevant information based on task requirements."""
+        logger.info(f"Extracting relevant information for {content['url']}")
+        # Use LLM to extract specific information based on task requirements
+        prompt = f"""
+        Extract relevant information from the following content based on these requirements:
+        Task requirements: {task.get('requirements', 'general information')}
+
+        Content:
+        {content['text_content'][:1500]}...
+        """
+
+        extracted_info = await self.llm_orchestrator.generate_completion(prompt)
+        logger.info(f"Successfully extracted information for {content['url']}")
+        return {'extracted_information': extracted_info}
+
+    async def crawl_links(self, base_url: str,
+                          max_depth: int = 2) -> List[Dict[str, Any]]:
+        """Crawl links starting from a base URL up to a maximum depth."""
+        logger.info(f"Crawling links from {base_url} up to depth {max_depth}")
+        results = []
+
+        async def crawl(url: str, depth: int):
+            if depth > max_depth or url in self.visited_urls:
+                return
+
+            self.visited_urls.add(url)
+            try:
+                content = await self.collect_data(url)
+                results.append(content)
+
+                if depth < max_depth:
+                    tasks = []
+                    for link in content['links']:
+                        if link not in self.visited_urls:
+                            tasks.append(crawl(link, depth + 1))
+                    await asyncio.gather(*tasks)
+            except Exception as e:
+                logger.error(f"Error crawling {url}: {str(e)}")
+
+        await crawl(base_url, 0)
+        logger.info(f"Finished crawling links from {base_url}")
+        return results
+
+    async def shutdown(self):
+        """Cleanup resources."""
+        logger.info("Shutting down WebBrowsingAgent")
+        if self.session:
+            await self.session.close()
+            self.session = None

app.py

@@ -0,0 +1,47 @@
+import asyncio
+import os
+import gradio as gr
+from core.central_ai_hub import CentralAIHub
+from loguru import logger
+
+# Initialize the Central AI Hub
+hub = None
+
+async def initialize_hub():
+    global hub
+    if hub is None:
+        logger.info("Initializing Central AI Hub...")
+        # Check if a local model path is provided as an environment variable
+        model_path = os.getenv("LOCAL_MODEL_PATH")
+        hub = CentralAIHub(model_path=model_path)
+        await hub.start()
+        logger.info("Central AI Hub initialized.")
+
+async def process_task(task_type, task_content, task_requirements):
+    await initialize_hub()
+    task = {
+        'type': task_type,
+        'content': task_content,
+        'requirements': task_requirements.split(',') if task_requirements else []
+    }
+    task_id = await hub.delegate_task(task)
+    status = await hub.get_task_status(task_id)
+    return f"Task ID: {task_id}, Status: {status['status']}, Result: {status.get('result', 'N/A')}"
+
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.Markdown("# Central AI Hub")
+        with gr.Row():
+            task_type = gr.Dropdown(choices=['code_analysis', 'code_generation', 'error_fixing'], label="Task Type")
+            task_content = gr.Textbox(label="Task Content")
+            task_requirements = gr.Textbox(label="Task Requirements (comma separated)")
+        submit_button = gr.Button("Submit Task")
+        output_text = gr.Textbox(label="Task Status")
+
+        submit_button.click(
+            process_task,
+            inputs=[task_type, task_content, task_requirements],
+            outputs=[output_text]
+        )
+
+    demo.launch(server_name="0.0.0.0", server_port=7860)

core/central_ai_hub.py

@@ -0,0 +1,154 @@
+"""Central AI Hub for coordinating all AI agents and operations."""
+import os
+import uuid
+import asyncio
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+from ctransformers import AutoModelForCausalLM
+import torch
+from huggingface_hub import hf_hub_download
+
+class CentralAIHub:
+    def __init__(self, api_key=None, model_path=None):
+        """Initialize the Central AI Hub."""
+        self.api_key = api_key
+        self.model_path = model_path
+        self.cache_dir = os.path.join(os.getcwd(), ".cache")
+        os.makedirs(self.cache_dir, exist_ok=True)
+        self.llm = None
+        self.llm_orchestrator = None
+        self.agents = {
+            'code_analysis': 'CodeAnalysisAgent',
+            'code_generation': 'CodeGenerationAgent',
+            'error_fixing': 'ErrorFixingAgent'
+        }
+        self.tasks = {}
+        self.active_agents = {}
+        self._initialized = False
+        self.max_retries = 3
+        self.retry_delay = 2
+
+    async def _initialize_llm_client(self):
+        """Initialize LLM client with retry mechanism and proper model selection."""
+        try:
+            if self.model_path:
+                # Load local model
+                logger.info(f"Loading local model from {self.model_path}")
+                self.llm = AutoModelForCausalLM.from_pretrained(
+                    self.model_path,
+                    model_type="qwen",
+                    cache_dir=self.cache_dir,
+                    local_files_only=True
+                )
+                logger.info(f"Local model loaded successfully")
+            else:
+                # Download model from Hugging Face Hub
+                model_name = "Qwen/Qwen2.5-14B-Instruct-GGUF"
+                model_filename = "Qwen2.5-14B_Uncensored_Instruct-Q8_0.gguf"
+                cached_model_path = os.path.join(self.cache_dir, model_filename)
+                if not os.path.exists(cached_model_path):
+                    logger.info(f"Downloading model {model_filename} from Hugging Face Hub")
+                    hf_hub_download(
+                        repo_id=model_name,
+                        filename=model_filename,
+                        cache_dir=self.cache_dir,
+                        local_files_only=False
+                    )
+                    logger.info(f"Model downloaded to {cached_model_path}")
+                else:
+                    logger.info(f"Using cached model at {cached_model_path}")
+                self.llm = AutoModelForCausalLM.from_pretrained(
+                    cached_model_path,
+                    model_type="qwen",
+                    local_files_only=True
+                )
+                logger.info(f"Model loaded successfully")
+            self.llm_orchestrator = LLMOrchestrator(self.llm)
+            return True
+        except Exception as e:
+            logger.error(f"Failed to initialize LLM client: {e}")
+            return False
+
+    async def start(self):
+        """Start the Central AI Hub and initialize agents only after successful LLM connection."""
+
+        if self._initialized:
+            return
+
+        logger.info("Starting Central AI Hub...")
+
+        if not await self._initialize_llm_client():  # Initialize LLM client first
+            raise Exception("Failed to initialize LLM client.")
+
+        for agent_type, agent_class in self.agents.items():
+            try:
+                await self.initialize_agent(agent_class)
+                logger.info(f"Initialized {agent_class}")
+            except Exception as e:
+                logger.error(f"Failed to initialize agent {agent_class}: {e}")
+                raise  # Re-raise the exception to halt the startup
+
+        self._initialized = True
+        logger.info("Central AI Hub initialization complete.")
+
+    async def delegate_task(self, task):
+        """Delegate a task to appropriate agent."""
+        if not task:
+            raise Exception("Task cannot be None")
+
+        task_id = str(uuid.uuid4())
+        agent_type = await self.select_agent(task)
+
+        if not agent_type:
+            raise Exception(f"No suitable agent found for task type: {task['type']}")
+
+        self.tasks[task_id] = {
+            'status': 'active',
+            'task': task,
+            'agent': agent_type,
+            'result': None
+        }
+
+        # Process task asynchronously
+        asyncio.create_task(self._process_task(task_id))
+
+        return task_id
+
+    async def _process_task(self, task_id):
+        """Process a task asynchronously."""
+        task_info = self.tasks[task_id]
+        try:
+            # Simulate task processing
+            await asyncio.sleep(2)  # Simulated work
+            task_info['status'] = 'completed'
+            task_info['result'] = "Task processed successfully"
+            logger.info(f"Task {task_id} completed")
+        except Exception as e:
+            task_info['status'] = 'failed'
+            task_info['error'] = str(e)
+            logger.error(f"Error processing task {task_id}: {str(e)}")
+
+    async def get_task_status(self, task_id):
+        """Get status of a task."""
+        task_info = self.tasks.get(task_id, {'status': 'not_found'})
+        return task_info
+
+    async def select_agent(self, task):
+        """Select appropriate agent for task."""
+        return self.agents.get(task['type'])
+
+    async def initialize_agent(self, agent_id):
+        """Initialize an agent."""
+        if agent_id not in self.agents.values():
+            raise ValueError(f"Agent {agent_id} not found")
+        self.active_agents[agent_id] = True
+
+    async def shutdown(self):
+        """Shutdown the Central AI Hub."""
+        logger.info("Shutting down Central AI Hub...")
+        # Clean up active agents
+        self.active_agents.clear()
+        # Cancel any pending tasks
+        for task_id, task in self.tasks.items():
+            if task['status'] == 'active':
+                task['status'] = 'cancelled'

core/knowledge_management_layer.py

@@ -0,0 +1,254 @@
+from typing import Dict, List, Any
+import networkx as nx
+from rdflib import Graph, Literal, RDF, URIRef
+from loguru import logger
+from utils.llm_orchestrator import LLMOrchestrator
+import json
+from datetime import datetime
+
+
+class KnowledgeManagementLayer:
+    def __init__(self, llm_api_key: str):
+        """Initialize the Knowledge Management Layer."""
+        self.llm_orchestrator = LLMOrchestrator(llm_api_key)
+        self.knowledge_graph = nx.DiGraph()
+        self.rdf_graph = Graph()
+        self.setup_logger()
+
+    def setup_logger(self):
+        """Configure logging for the knowledge management layer."""
+        logger.add("logs/knowledge_management.log", rotation="500 MB")
+
+    async def update_knowledge_graph(
+            self, new_info: Dict[str, Any]) -> Dict[str, str]:
+        """Update the knowledge graph with new information."""
+        logger.info("Updating knowledge graph with new information")
+        entities_added = 0
+        relations_added = 0
+        try:
+            # Process new information using LLM
+            processed_info = await self.process_information(new_info)
+
+            # Add nodes and edges to the graph
+            for entity in processed_info['entities']:
+                self.knowledge_graph.add_node(
+                    entity['id'],
+                    **entity['attributes']
+                )
+                entities_added += 1
+
+            for relation in processed_info['relations']:
+                self.knowledge_graph.add_edge(
+                    relation['source'],
+                    relation['target'],
+                    **relation['attributes']
+                )
+                relations_added += 1
+
+            # Update RDF graph
+            await self.update_rdf_graph(processed_info)
+
+            logger.info(
+                f"Successfully updated knowledge graph: Added {entities_added} entities and {relations_added} relations")
+            return {
+                'status': 'success',
+                'message': f"Added {entities_added} entities and {relations_added} relations"
+            }
+        except Exception as e:
+            logger.error(f"Error updating knowledge graph: {str(e)}")
+            logger.error(
+                f"Processed {entities_added} entities and {relations_added} relations before error")
+            return {
+                'status': 'error',
+                'message': str(e)
+            }
+
+    async def process_information(
+            self, info: Dict[str, Any]) -> Dict[str, Any]:
+        """Process raw information using LLM to extract entities and relations."""
+        logger.info("Processing information to extract entities and relations")
+        try:
+            # Generate prompt for entity extraction
+            entity_prompt = f"""
+            Extract entities and their attributes from the following information:
+            {json.dumps(info, indent=2)}
+
+            Return the entities in the following format:
+            - Entity ID
+            - Entity Type
+            - Attributes (key-value pairs)
+            """
+
+            entity_response = await self.llm_orchestrator.generate_completion(entity_prompt)
+            entities = self.parse_llm_response(entity_response, 'entities')
+            logger.info(f"Extracted {len(entities)} entities")
+
+            # Generate prompt for relation extraction
+            relation_prompt = f"""
+            Extract relations between entities from the following information:
+            {json.dumps(info, indent=2)}
+
+            Entities found:
+            {json.dumps(entities, indent=2)}
+
+            Return the relations in the following format:
+            - Source Entity ID
+            - Target Entity ID
+            - Relation Type
+            - Attributes (key-value pairs)
+            """
+
+            relation_response = await self.llm_orchestrator.generate_completion(relation_prompt)
+            relations = self.parse_llm_response(relation_response, 'relations')
+            logger.info(f"Extracted {len(relations)} relations")
+
+            return {
+                'entities': entities,
+                'relations': relations
+            }
+        except Exception as e:
+            logger.error(f"Error processing information: {str(e)}")
+            raise
+
+    async def update_rdf_graph(self, processed_info: Dict[str, Any]):
+        """Update the RDF graph with processed information."""
+        try:
+            for entity in processed_info['entities']:
+                subject = URIRef(f"entity:{entity['id']}")
+                self.rdf_graph.add(
+                    (subject, RDF.type, URIRef(f"type:{entity['type']}")))
+
+                for key, value in entity['attributes'].items():
+                    self.rdf_graph.add(
+                        (subject, URIRef(f"attribute:{key}"), Literal(value)))
+
+            for relation in processed_info['relations']:
+                subject = URIRef(f"entity:{relation['source']}")
+                obj = URIRef(f"entity:{relation['target']}")
+                predicate = URIRef(f"relation:{relation['type']}")
+                self.rdf_graph.add((subject, predicate, obj))
+
+                for key, value in relation['attributes'].items():
+                    self.rdf_graph.add(
+                        (predicate, URIRef(f"attribute:{key}"), Literal(value)))
+        except Exception as e:
+            logger.error(f"Error updating RDF graph: {str(e)}")
+            raise
+
+    async def query_knowledge(self, query: Dict[str, Any]) -> Dict[str, Any]:
+        """Query the knowledge graph based on specific criteria."""
+        try:
+            # Generate SPARQL query using LLM
+            sparql_prompt = f"""
+            Generate a SPARQL query for the following search criteria:
+            {json.dumps(query, indent=2)}
+            """
+
+            sparql_query = await self.llm_orchestrator.generate_completion(sparql_prompt)
+
+            # Execute query on RDF graph
+            results = self.rdf_graph.query(sparql_query)
+
+            # Process and format results
+            formatted_results = await self.format_query_results(results)
+
+            return {
+                'status': 'success',
+                'results': formatted_results
+            }
+        except Exception as e:
+            logger.error(f"Error querying knowledge graph: {str(e)}")
+            return {
+                'status': 'error',
+                'message': str(e)
+            }
+
+    async def generate_insights(
+            self, context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Generate insights from the knowledge graph."""
+        try:
+            # Extract relevant subgraph based on context
+            subgraph = self.extract_relevant_subgraph(context)
+
+            # Generate insights using LLM
+            insight_prompt = f"""
+            Generate insights from the following knowledge graph data:
+            Nodes: {len(subgraph.nodes)}
+            Edges: {len(subgraph.edges)}
+            Context: {json.dumps(context, indent=2)}
+
+            Graph Summary:
+            {self.summarize_subgraph(subgraph)}
+            """
+
+            insights = await self.llm_orchestrator.generate_completion(insight_prompt)
+
+            return self.parse_llm_response(insights, 'insights')
+        except Exception as e:
+            logger.error(f"Error generating insights: {str(e)}")
+            raise
+
+    def extract_relevant_subgraph(self, context: Dict[str, Any]) -> nx.DiGraph:
+        """Extract a relevant subgraph based on context."""
+        # Implementation would include logic to extract relevant portions of the graph
+        # based on the provided context
+        return self.knowledge_graph
+
+    def summarize_subgraph(self, subgraph: nx.DiGraph) -> str:
+        """Generate a summary of the subgraph."""
+        summary = {
+            'node_types': {},
+            'edge_types': {},
+            'key_entities': []
+        }
+
+        # Count node types
+        for node in subgraph.nodes(data=True):
+            node_type = node[1].get('type', 'unknown')
+            summary['node_types'][node_type] = summary['node_types'].get(
+                node_type, 0) + 1
+
+        # Count edge types
+        for edge in subgraph.edges(data=True):
+            edge_type = edge[2].get('type', 'unknown')
+            summary['edge_types'][edge_type] = summary['edge_types'].get(
+                edge_type, 0) + 1
+
+        # Identify key entities (e.g., nodes with highest degree)
+        for node in sorted(subgraph.degree, key=lambda x: x[1], reverse=True)[
+                :5]:
+            summary['key_entities'].append({
+                'id': node[0],
+                'degree': node[1]
+            })
+
+        return json.dumps(summary, indent=2)
+
+    @staticmethod
+    def parse_llm_response(
+            response: str, response_type: str) -> List[Dict[str, Any]]:
+        """Parse LLM response into structured data."""
+        # Implementation would include logic to parse the LLM's response
+        # into a structured format based on the response_type
+        return []  # Placeholder return
+
+    async def backup_knowledge(self, backup_path: str):
+        """Backup the knowledge graph to a file."""
+        try:
+            timestamp = datetime.utcnow().strftime('%Y%m%d_%H%M%S')
+
+            # Backup NetworkX graph
+            nx.write_gpickle(
+                self.knowledge_graph,
+                f"{backup_path}/knowledge_graph_{timestamp}.gpickle")
+
+            # Backup RDF graph
+            self.rdf_graph.serialize(
+                f"{backup_path}/rdf_graph_{timestamp}.ttl",
+                format="turtle")
+
+            logger.info(
+                f"Knowledge graph backed up successfully at {timestamp}")
+        except Exception as e:
+            logger.error(f"Error backing up knowledge graph: {str(e)}")
+            raise

logs/code_analysis_agent.log

@@ -0,0 +1,30 @@
+2025-01-11 22:23:20.639 | INFO     | agents.code_analysis_agent:analyze_repository:56 - Analyzing repository at .
+2025-01-11 22:23:20.640 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\temp_analysis_script.py
+2025-01-11 22:23:20.642 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\central_ai_hub_test.py
+2025-01-11 22:23:20.643 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\code_analysis_agent.py
+2025-01-11 22:23:20.647 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\code_generation_agent.py
+2025-01-11 22:23:20.647 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\error_fixing_agent.py
+2025-01-11 22:23:20.647 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\planning_agent.py
+2025-01-11 22:23:20.655 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\q_learning_agent.py
+2025-01-11 22:23:20.657 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\reasoning_agent.py
+2025-01-11 22:23:20.660 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\testing_agent.py
+2025-01-11 22:23:20.664 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\test_runner.py
+2025-01-11 22:23:20.666 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\web_browsing_agent.py
+2025-01-11 22:23:20.675 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\core\central_ai_hub.py
+2025-01-11 22:23:20.681 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\core\knowledge_management_layer.py
+2025-01-11 22:23:20.690 | INFO     | agents.code_analysis_agent:analyze_repository:65 - Finished analyzing repository at .
+2025-01-11 22:23:34.023 | INFO     | agents.code_analysis_agent:analyze_repository:56 - Analyzing repository at .
+2025-01-11 22:23:34.023 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\temp_analysis_script.py
+2025-01-11 22:23:34.023 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\central_ai_hub_test.py
+2025-01-11 22:23:34.023 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\code_analysis_agent.py
+2025-01-11 22:23:34.039 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\code_generation_agent.py
+2025-01-11 22:23:34.040 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\error_fixing_agent.py
+2025-01-11 22:23:34.043 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\planning_agent.py
+2025-01-11 22:23:34.044 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\q_learning_agent.py
+2025-01-11 22:23:34.049 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\reasoning_agent.py
+2025-01-11 22:23:34.052 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\testing_agent.py
+2025-01-11 22:23:34.054 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\test_runner.py
+2025-01-11 22:23:34.056 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\agents\web_browsing_agent.py
+2025-01-11 22:23:34.062 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\core\central_ai_hub.py
+2025-01-11 22:23:34.066 | INFO     | agents.code_analysis_agent:analyze_file:90 - Analyzing file .\core\knowledge_management_layer.py
+2025-01-11 22:23:34.068 | INFO     | agents.code_analysis_agent:analyze_repository:65 - Finished analyzing repository at .

requirements.txt

@@ -0,0 +1,4 @@
+huggingface_hub
+transformers
+torch
+ctransformers

temp_analysis_script.py

@@ -0,0 +1,11 @@
+from agents.code_analysis_agent import CodeAnalysisAgent
+import asyncio
+import json
+
+async def main():
+    analyzer = CodeAnalysisAgent()
+    report = await analyzer.analyze_repository('.')
+    print(json.dumps(report, indent=4))
+
+if __name__ == "__main__":
+    asyncio.run(main())