from fastapi import FastAPI, HTTPException
import uvicorn
from typing import Union, List
from predict_dept_model import DepartmentPredictor
from contextlib import asynccontextmanager
from response_schema import ClassificationOutput, TextInput 
from huggingface_hub import  HfApi
import os

# Define the model repository ID
model_repo = os.getenv("MODEL_REPO")

# hf api
api = HfApi()



# Setting up startup and shutdown logic
@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the model 
    global predictor
    predictor = DepartmentPredictor(model_repo= model_repo)
    yield
    

app = FastAPI(
    title="Sambodhan Department Classifier API",
    description="AI model that classifies citizen grievances into municipal departments with confidence scores.",
    version="1.0.0",
    lifespan=lifespan
)


@app.post("/predict", response_model=Union[ClassificationOutput, List[ClassificationOutput]])
def predict_department(input_data: TextInput):
    try:
        # Attempt to make a prediction
        prediction = predictor.predict(input_data.text)

        # Return prediction result as JSON
        return prediction

    except Exception as e:
        # Handle unexpected errors
        raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}")



@app.get("/")
def root():
    # Fetch the latest commit hash (revision) from the model repository
    latest_tag = api.list_repo_refs(repo_id=model_repo, repo_type="model").tags[0].name

    return {
        "message": "Sambodhan Department Classification API is running.",
        "status": "Active" if predictor  else "Inactive",
        "model_version": latest_tag
    }



    

# if __name__ == "__main__":
#     # Important for Hugging Face Spaces (port detection)
#     import os
#     port = int(os.getenv("PORT", 7860))
#     uvicorn.run("main:app", host="0.0.0.0", port=port)