import typing as t
import os
# from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import logging

from src.utils import (
    OpenAIClient,
    TogetherAIClient,
    GeminiClient,
    GroqClient,
    MistralClient,
)
from src.models_enums import ModelProvider

# load_dotenv()

assert os.environ['TOGETHER_API_KEY'] is not None

# Configure basic logging to see messages in stdout (and thus in HF Space logs)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class RequestData(BaseModel):
    prompt: str
    max_tokens: int = 50
    system_prompt: t.Optional[str] = None

MODEL_PROVIDER2CLIENT = {
    ModelProvider.OPENAI.value: OpenAIClient,
    ModelProvider.GEMINI.value: GeminiClient,
    ModelProvider.TOGETHERAI.value: TogetherAIClient,
    ModelProvider.GROQ.value: GroqClient,
    ModelProvider.MISTRAL.value: MistralClient,
}


app = FastAPI()
logger.info("FastAPI app initialized.")


# The application now starts without initializing a specific LLM,
# which makes it more flexible.

@app.post("/generate/{model_provider}/{model_name:path}")
async def generate_text(
    model_provider: str,
    model_name: str,
    request: RequestData
):
    """
    Generates text using a specified LLM provider and model.

    Example:
    POST /generate/togetherai/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free
    with body: {"prompt": "...", "max_tokens": 100}
    """
    logger.info(f"Received POST request to /generate/{model_provider}/{model_name}.")

    # Check if the requested model provider exists
    if model_provider not in MODEL_PROVIDER2CLIENT:
        logger.error(f"Invalid model provider: {model_provider}")
        raise HTTPException(
            status_code=400,
            detail=f"Invalid model provider: {model_provider}. "
                   f"Available providers: {[p.value for p in ModelProvider]}"
        )

    try:
        # Get the correct client class and instantiate it dynamically
        llm_client_class = MODEL_PROVIDER2CLIENT[model_provider]
        llm_client = llm_client_class(model=model_name)

        # Call the client's async method
        output = await llm_client(
            prompt=request.prompt, 
            system_prompt=request.system_prompt,
            max_tokens=request.max_tokens
        )
        
        return output
    
    except Exception as e:
        logger.error(
            f"Error during text generation for {model_provider}/{model_name}: {str(e)}", 
            exc_info=True
        )
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health_check():
    logger.info("Received GET request to /health.")
    return {"status": "ok"}