from transformers import pipeline
from pydantic import BaseModel
import logging
from fastapi import Request, HTTPException
import json
from typing import Optional

class TranslationRequest(BaseModel):
    inputs: str
    parameters: Optional[dict] = None
    options: Optional[dict] = None

class TranslationTaskService:

    __logger: logging.Logger

    def __init__(self, logger: logging.Logger):
        self.__logger = logger

    async def get_translation_request(
        self, 
        request: Request
    )  -> TranslationRequest: 
        content_type = request.headers.get("content-type", "")
        if content_type.startswith("application/json"):
            data = await request.json()
            return TranslationRequest(**data)
        if content_type.startswith("application/x-www-form-urlencoded"):
            raw = await request.body()
            try:
                data = json.loads(raw)
                return TranslationRequest(**data)
            except Exception:
                try:
                    data = json.loads(raw.decode("utf-8"))
                    return TranslationRequest(**data)
                except Exception:
                    raise HTTPException(status_code=400, detail="Invalid request body")
        raise HTTPException(status_code=400, detail="Unsupported content type")


    async def translate(
            self, 
            request: Request, 
            model_name: str
        ):

        translationRequest: TranslationRequest = await self.get_translation_request(request)

        try:
            pipe = pipeline("translation", model=model_name)
        except Exception as e:
            self.__logger.error(f"Failed to load model '{model_name}': {str(e)}")
            raise HTTPException(
                status_code=404,
                detail=f"Model '{model_name}' could not be loaded: {str(e)}"
            )

        try:       
            result = pipe(translationRequest.inputs, **(translationRequest.parameters or {}))
            return result
        except Exception as e:
            self.__logger.error(f"Inference failed for model '{model_name}': {str(e)}")
            raise HTTPException(
                status_code=500,
                detail=f"Inference failed: {str(e)}"
            )