# Setup Section
  
 # Create FastAPI instance
app = FastAPI(title="Sepsis Prediction API",description="API for Predicting Sespsis ")
## A function to load machine Learning components to re-use
def Ml_loading_components(fp):
    with open(fp, "rb") as f:
        object=pickle.load(f)
        return(object)
                                                                                                              # Loading the machine learning components
DIRPATH = os.path.dirname(os.path.realpath(__file__))
ml_core_fp = os.path.join(DIRPATH,"ML","ML_Model.pkl")
ml_components_dict = Ml_loading_components(fp=ml_core_fp)



# Defining the variables for each component
label_encoder = ml_components_dict['label_encoder'] # The label encoder
# Loaded scaler component
scaler = ml_components_dict['scaler']
#Loaded model 
model = ml_components_dict['model']
# Defining our input variables


class InputData(BaseModel):
    PRG:int
    PL: int
    BP: int
    SK: int
    TS: int
    BMI: float
    BD2: float
    Age: int

"""
* PRG: Plasma glucose

* PL: Blood Work Result-1 (mu U/ml)

* PR: Blood Pressure (mmHg)

* SK: Blood Work Result-2(mm)

* TS: Blood Work Result-3 (muU/ml)

* M11: Body mass index (weight in kg/(height in m)^2

* BD2: Blood Work Result-4 (mu U/ml)

* Age: patients age(years)

"""  
# Index route
@app.get("/")
def index():
    return{'message':'Hello, Welcome to My Sepsis Prediction FastAPI'}

 
# Create prediction endpoint
@app.post("/predict")
def predict (df:InputData):

 
    # Prepare the feature and structure them like in the notebook
    df = pd.DataFrame([df.dict().values()],columns=df.dict().keys())


    print(f"[Info] The inputed dataframe is : {df.to_markdown()}")
    age = df['Age']
    print(age)
    # Scaling the inputs
    df_scaled = scaler.transform(df)


    # Prediction
    raw_prediction = model.predict(df_scaled)

    if raw_prediction == 0:
        raise HTTPException(status_code=status.HTTP_200_OK, detail="The patient will Not Develop Sepsis")
    elif raw_prediction == 1:
        raise HTTPException(status_code=status.HTTP_200_OK, detail="The patient Will Develop Sepsis")
    else:
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Prediction Error")


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