from pydantic import BaseModel, field_validator 
from typing import Optional
import os
from llmdantic import LLMdantic, LLMdanticConfig  
from sambanova.langchain_wrappers import SambaNovaFastAPI
from dotenv import load_dotenv
from llmdantic import LLMdanticResult


current_dir = os.getcwd()
utils_dir = os.path.abspath(os.path.join(current_dir, '..'))
load_dotenv(os.path.join(utils_dir, '.env'), override=True)
# load_dotenv('.env', override=True)


class Catergories_Classify_Input(BaseModel):
    text: str

class Catergories_Classify_Output(BaseModel):
    result: str

    @field_validator("result")
    def catergory_result_must_not_be_empty(cls, v) -> bool:
        """Category result must not be empty"""
        if not v.strip():
            raise ValueError("Category result must not be empty")
        return v


class Evaluator:
    def __init__(self, llm : Optional[str], prompt: str):
        self.llm = SambaNovaFastAPI(model=llm, fastapi_url = "https://fast-api.snova.ai/v1/chat/completions" , fastapi_api_key = "dHVhbmFuaC5uay4xOF9fZ21haWwuY29tOlRWbG9yQkxhNUY=")
        self.prompt = prompt
        self.config = LLMdanticConfig(
            objective=self.prompt,
            inp_schema=Catergories_Classify_Input,
            out_schema=Catergories_Classify_Output, 
            retries=5,
        )
        self.llmdantic = LLMdantic(llm=self.llm, config=self.config)
    
    def classify_text(self, text: str) -> Optional[Catergories_Classify_Output]:
        data = Catergories_Classify_Input(text=text)
        result: LLMdanticResult = self.llmdantic.invoke(data)
        return result.output