code_agent.py

# code_agent.py
import json
import logging
# import requests # Uncomment when requests is installed

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

class CodeAgent:
    _API_ENDPOINT = "https://api.example.com/llm" # Placeholder
    _API_KEY = "YOUR_API_KEY" # Placeholder for actual API key

    def __init__(self):
        logger.info("Code Agent initialized.")
        # In a real implementation, this would initialize the connection to a
        # DeepSeek Coder-like model API.

    def execute_command(self, command: dict) -> dict:
        """
        Executes a code-related command based on the structured input.
        """
        cmd_name = command.get("command")
        params = command.get("parameters", {})
        logger.info(f"Executing Code Agent command: {cmd_name} with params: {params}")

        if cmd_name == "generate_code":
            return self._generate_code(params)
        elif cmd_name == "analyze_code":
            return self._analyze_code(params)
        elif cmd_name == "refactor_code":
            return self._refactor_code(params)
        elif cmd_name == "fix_bug":
            return self._fix_bug(params)
        else:
            return self._create_error_response(
                cmd_name, "UNKNOWN_COMMAND", f"Onbekend commando: {cmd_name}"
            )

    def _call_llm_api(self, task_type: str, data: dict) -> dict:
        """
        Simulates a call to an external LLM API.
        In a real scenario, this would make an HTTP request.
        """
        logger.info(f"Simulating LLM API call for task: {task_type} with data: {data}")

        # Simulate API response based on task_type
        if task_type == "generate":
            prompt = data.get("prompt", "")
            language = data.get("language", "python")
            generated_code = (f"# Generated {language} code for: {prompt}\n"
                              f"print('Hello from simulated LLM!')\n"
                              f"# API Context: {data.get('context', '')}")
            return {"generated_code": generated_code, "language": language}
        elif task_type == "analyze":
            code = data.get("code", "")
            analysis_type = data.get("analysis_type", "general")
            analysis_report = {
                "type": analysis_type,
                "findings": [{"line": 1, "description": f"Simulated finding for {analysis_type}."}],
                "summary": f"Simulated analysis of code for {analysis_type}."
            }
            return {"analysis_report": analysis_report}
        elif task_type == "refactor":
            code = data.get("code", "")
            refactoring_goal = data.get("refactoring_goal", "general_improvement")
            refactored_code = (f"# Refactored code for {refactoring_goal} (simulated)\n" 
                              f"{code}\n" 
                               f"# API Context: {data.get('context', '')}")
            return {"refactored_code": refactored_code}
        elif task_type == "fix":
            code = data.get("code", "")
            error_message = data.get("error_message", "")
            fixed_code = (f"# Bug fixed code for error: {error_message} (simulated)\n" 
                         f"{code}\n" 
                         f"# API Context: {data.get('context', '')}")
            return {"fixed_code": fixed_code}
        else:
            return {"error": "Unknown LLM task type."}

        # Example of how a real API call might look (uncomment when requests is installed)
        # try:
        #     headers = {"Authorization": f"Bearer {self._API_KEY}"}
        #     payload = {"task_type": task_type, "data": data}
        #     response = requests.post(self._API_ENDPOINT, json=payload, headers=headers)
        #     response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
        #     return response.json()
        # except requests.exceptions.RequestException as e:
        #     logger.error(f"LLM API call failed: {e}")
        #     return {"error": f"LLM API call failed: {e}"}


    def _generate_code(self, params: dict) -> dict:
        prompt = params.get("prompt")
        language = params.get("language", "python")
        context = params.get("context", "")

        if not prompt:
            return self._create_error_response("generate_code", "MISSING_PARAMETERS", "Prompt is vereist.")

        llm_response = self._call_llm_api("generate", {"prompt": prompt, "language": language, "context": context})
        
        if "error" in llm_response:
            return self._create_error_response("generate_code", "LLM_API_ERROR", llm_response["error"])

        return self._create_success_response(
            "generate_code",
            {"generated_code": llm_response.get("generated_code"), "language": llm_response.get("language")},
            "Code succesvol gegenereerd (via LLM simulatie)."
        )

    def _analyze_code(self, params: dict) -> dict:
        code = params.get("code")
        analysis_type = params.get("analysis_type", "general")
        context = params.get("context", "")

        if not code:
            return self._create_error_response("analyze_code", "MISSING_PARAMETERS", "Code is vereist.")

        llm_response = self._call_llm_api("analyze", {"code": code, "analysis_type": analysis_type, "context": context})

        if "error" in llm_response:
            return self._create_error_response("analyze_code", "LLM_API_ERROR", llm_response["error"])

        return self._create_success_response(
            "analyze_code",
            {"analysis_report": llm_response.get("analysis_report")},
            "Code-analyse voltooid (via LLM simulatie)."
        )

    def _refactor_code(self, params: dict) -> dict:
        code = params.get("code")
        refactoring_goal = params.get("refactoring_goal", "general_improvement")
        context = params.get("context", "")

        if not code:
            return self._create_error_response("refactor_code", "MISSING_PARAMETERS", "Code is vereist.")

        llm_response = self._call_llm_api("refactor", {"code": code, "refactoring_goal": refactoring_goal, "context": context})

        if "error" in llm_response:
            return self._create_error_response("refactor_code", "LLM_API_ERROR", llm_response["error"])

        return self._create_success_response(
            "refactor_code",
            {"refactored_code": llm_response.get("refactored_code")},
            "Code succesvol gerefactored (via LLM simulatie)."
        )

    def _fix_bug(self, params: dict) -> dict:
        code = params.get("code")
        error_message = params.get("error_message", "")
        context = params.get("context", "")

        if not code:
            return self._create_error_response("fix_bug", "MISSING_PARAMETERS", "Code is vereist.")

        llm_response = self._call_llm_api("fix", {"code": code, "error_message": error_message, "context": context})

        if "error" in llm_response:
            return self._create_error_response("fix_bug", "LLM_API_ERROR", llm_response["error"])

        return self._create_success_response(
            "fix_bug",
            {"fixed_code": llm_response.get("fixed_code")},
            "Bug succesvol opgelost (via LLM simulatie)."
        )

    def _create_success_response(self, cmd_name: str, data: dict, message: str) -> dict:
        return {
            "status": "success",
            "command_executed": cmd_name,
            "data": data,
            "message": message,
        }

    def _create_error_response(self, cmd_name: str, error_code: str, message: str, details: dict = None) -> dict:
        return {
            "status": "error",
            "command_executed": cmd_name,
            "error_code": error_code,
            "message": message,
            "details": details if details is not None else {},
        }

# Example usage (for testing purposes)
if __name__ == "__main__":
    agent = CodeAgent()

    # Example: Generate code
    # response = agent.execute_command({
    #     "command": "generate_code",
    #     "parameters": {"prompt": "a Python function to add two numbers", "language": "python"}
    # })
    # print(json.dumps(response, indent=2))

    # Example: Analyze code
    # response = agent.execute_command({
    #     "command": "analyze_code",
    #     "parameters": {"code": "def func(): pass", "analysis_type": "bug_detection"}
    # })
    # print(json.dumps(response, indent=2))