Clover/models/api_client.py

"""
API client for Clover - A terminal assistant for AI-powered project management
Handles communication with the OpenAI-compatible LLM server
"""

import json
import os
from typing import Any, Dict, Optional

import requests

from config.settings import load_config


class APIClient:
    """
    API client for communicating with the LLM server
    """

    def __init__(self):
        """Initialize the API client with configuration"""
        self.config = load_config()
        self.base_url = self.config.get("base_url", "http://192.168.8.223:11434")
        self.api_key = self.config.get("api_key")

    def _make_request(
        self, endpoint: str, method: str = "GET", data: Optional[Dict] = None
    ) -> Dict[str, Any]:
        """
        Make a request to the LLM API server

        Args:
            endpoint (str): API endpoint
            method (str): HTTP method (GET, POST)
            data (dict): Request data

        Returns:
            dict: Response from the API
        """
        try:
            # Ensure base_url doesn't have trailing slash and endpoint has leading slash
            base = self.base_url.rstrip("/")
            endpoint = endpoint if endpoint.startswith("/") else f"/{endpoint}"
            url = f"{base}{endpoint}"

            headers = {
                "Content-Type": "application/json",
                "User-Agent": "clover-cli/1.0",
                "Accept": "application/json",
            }

            # Add API key if available
            if self.api_key:
                headers["Authorization"] = f"Bearer {self.api_key}"

            if method == "GET":
                response = requests.get(
                    url, headers=headers, timeout=self.config.get("timeout", 300)
                )
            elif method == "POST":
                response = requests.post(
                    url,
                    headers=headers,
                    json=data,
                    timeout=self.config.get("timeout", 300),
                )
            else:
                raise ValueError(f"Unsupported HTTP method: {method}")

            response.raise_for_status()
            return {"success": True, "data": response.json()}

        except requests.exceptions.RequestException as e:
            return {"success": False, "error": f"HTTP Request failed: {str(e)}"}
        except json.JSONDecodeError as e:
            return {"success": False, "error": f"Invalid JSON response: {str(e)}"}
        except Exception as e:
            return {"success": False, "error": f"Unexpected error: {str(e)}"}

    def list_models(self) -> Dict[str, Any]:
        """
        Get list of available models from the LLM server

        Returns:
            dict: Available models information
        """
        # Use Ollama standard endpoint for model listing
        result = self._make_request("/api/tags", "GET")
        if not result["success"]:
            return {"error": result["error"], "models": []}

        try:
            data = result["data"]
            # Handle Ollama format correctly - the response has a "models" key with array
            if isinstance(data, dict) and "models" in data:
                models_list = data["models"]
            else:
                # If it's already just a list of models
                models_list = data if isinstance(data, list) else []

            return {"models": models_list}
        except Exception as e:
            return {"error": f"Failed to parse models response: {str(e)}", "models": []}

    def chat_completion(
        self, messages: list, model: str = None, **kwargs
    ) -> Dict[str, Any]:
        """
        Get a completion from the LLM using Ollama chat endpoint.

        Uses /api/chat with proper message list format to preserve
        multi-turn conversation semantics (system, user, assistant roles).

        Args:
            messages (list): List of message dictionaries with 'role' and 'content'
            model (str): Model to use
            **kwargs: Additional parameters for the API

        Returns:
            dict: Response from the LLM in OpenAI-compatible format
        """
        if model is None:
            # Reload config to get latest model setting
            from config.settings import load_config

            current_config = load_config()
            model = current_config.get("model", "qwen3-coder:30b")

        # Filter messages to only include valid roles for chat endpoint
        chat_messages = []
        for message in messages:
            role = message.get("role", "user")
            content = message.get("content", "")
            if role in ("system", "user", "assistant"):
                chat_messages.append({"role": role, "content": content})

        if not chat_messages:
            return {"error": "No valid messages provided for chat completion"}

        # Use Ollama chat endpoint with proper message format
        data = {
            "model": model,
            "messages": chat_messages,
            "stream": False,
            **kwargs,
        }

        result = self._make_request("/api/chat", "POST", data)
        if not result["success"]:
            return {"error": result["error"]}

        # Extract the response from Ollama's chat format
        try:
            response_data = result["data"]
            # Ollama chat endpoint returns message in message.content
            if "message" in response_data:
                return {
                    "choices": [
                        {
                            "message": {
                                "role": response_data["message"].get(
                                    "role", "assistant"
                                ),
                                "content": response_data["message"].get(
                                    "content", ""
                                ),
                            }
                        }
                    ]
                }
            elif "response" in response_data:
                return {
                    "choices": [{"message": {"content": response_data["response"]}}]
                }
            else:
                return response_data
        except Exception as e:
            return {"error": f"Failed to process chat completion result: {str(e)}"}

    def generate_text(self, prompt: str, model: str = None, **kwargs) -> Dict[str, Any]:
        """
        Generate text using the LLM

        Args:
            prompt (str): Prompt to send to the LLM
            model (str): Model to use
            **kwargs: Additional parameters for the API

        Returns:
            dict: Generated response
        """
        if model is None:
            model = self.config.get("model", "qwen3-coder:30b")

        # Format as Ollama generate request
        data = {"model": model, "prompt": prompt, "stream": False, **kwargs}

        result = self._make_request("/api/generate", "POST", data)
        if not result["success"]:
            return {"error": result["error"]}

        try:
            response_data = result["data"]
            # Extract the actual response text for generate endpoint
            if "response" in response_data:
                return {
                    "choices": [{"message": {"content": response_data["response"]}}]
                }
            else:
                # If we got back something else, return it as-is
                return response_data
        except Exception as e:
            return {"error": f"Failed to process generate result: {str(e)}"}

    def get_model_info(self, model_name: str) -> Dict[str, Any]:
        """
        Get information about a specific model

        Args:
            model_name (str): Name of the model

        Returns:
            dict: Model information
        """
        # Use Ollama's show endpoint
        result = self._make_request(f"/api/show/{model_name}", "POST")
        if not result["success"]:
            return {"error": result["error"]}

        return result["data"]