# main.py
from fastapi import FastAPI, Query, Request, HTTPException
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.templating import Jinja2Templates
import xgboost as xgb
import joblib
import pandas as pd
from pydantic import BaseModel  # Import Pydantic's BaseModel

app = FastAPI()
templates = Jinja2Templates(directory="templates")



class InputFeatures(BaseModel):
    prg: float
    pl: float
    pr: float
    sk: float
    ts: float
    m11: float
    bd2: float
    age: int

# Load the pickled XGBoost model
xgb_model = joblib.load("model.joblib")


# @app.get("/")
# async def read_root():
#     return {"message": "Welcome to the Sepsis Prediction API"}

# @app.get("/form/")
# async def show_form():

@app.post("/predict/")
async def predict_sepsis(
    request: Request,
    input_data: InputFeatures  # Use the Pydantic model for input validation
):
    try:
        # Convert Pydantic model to a DataFrame for prediction
        input_df = pd.DataFrame([input_data.dict()])

        # Make predictions using the loaded XGBoost model
        prediction = xgb_model.predict_proba(xgb.DMatrix(input_df))

        # Create a JSON response
        response = {
            "input_features": input_data,
            "prediction": {
                "class_0_probability": prediction[0],
                "class_1_probability": prediction[1]
            }
        }

        return templates.TemplateResponse(
            "display_params.html",
            {
                "request": request,
                "input_features": response["input_features"],
                "prediction": response["prediction"]
            }
        )
    except Exception as e:
        #raise HTTPException(status_code=500, detail="An error occurred while processing the request.")
        raise HTTPException(status_code=500, detail=str(e))