# app/agent_executor.py
import json
from langchain.agents import initialize_agent, AgentType
from app.recommend_tool import RecommendTool
from app.generateRecom import RecommendToolGeneration
from app.llm_client import LLMClient

class AgentExecutor:
    def __init__(self, api_key: str):
        llm_client = LLMClient(api_key)
        rec_tool = RecommendTool(llm_client).tool
        self.agent = initialize_agent(
            tools=[rec_tool],
            llm=llm_client.llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=False
        )
        gen_tool = RecommendToolGeneration(llm_client).tool
        self.agent = initialize_agent(
        tools=[rec_tool, gen_tool],
        llm=llm_client.llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=False
        )

    def run_completion(self, data: dict) -> dict:
        prompt = f"Tool: recommend_missing\nInput: {json.dumps(data, ensure_ascii=False)}"
        return self._call_agent(prompt)

    def run_generation(self, data: dict) -> dict:
        prompt = f"Tool: generate_recommendation_sections\nInput: {json.dumps(data, ensure_ascii=False)}"
        return self._call_agent(prompt)

    def _call_agent(self, prompt: str) -> dict:
        result_str = self.agent.run(input=prompt)
        result_str=result_str.replace("```json\n", "").replace("```", "").strip()
        try:

            result = json.loads(result_str)  # convertir la string JSON en dict Python
        except json.JSONDecodeError:
            return {
                "error": "Invalid JSON returned",
                "raw": result_str
                }

        return result  # ce dict sera validé contre response_model