# 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

    # Plasma glucose:float
    # Blood Work Result 1:float
    # Blood Pressure :float
    # Blood Work Result 2:float
    # Blood Work Result 3:float
    # Body mass index :float
    # Blood Work Result 4:float
    # patients age :int

# Load the pickled XGBoost model
model_input = joblib.load("model_1.joblib")


@app.post("/sepsis_prediction")
async def predict(input:model_input):  
     #Numeric Features 

     num_features = [['Plasma glucose','Blood Work Result-1','Blood Pressure,
       'Blood Work Result-2',' Blood Work Result-3', 'Body mass index,
       'Blood Work Result-4', 'Age']]

    XGB= Pipeline([
    ("col_trans", full_pipeline),
    ("feature_selection", SelectKBest(score_func=f_classif, k='all')),
    ("model", BaggingClassifier(base_estimator=XGBClassifier(random_state=42)))
])

#print(model_input)
    df = pd.DataFrame([input])
    final_input = np.array(predict_input.fit_transform(df), dtype = np.str)
    prediction = model.predict(np.array([[final_input]]).reshape(1, 1))

    return prediction

if __name__ == '__main__':
    uvicorn.run("Main:app", reload = True)






# @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))