import numpy as np
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import pickle

app = FastAPI()

def load_model():
    try:
        model = joblib.load("model.joblib")
        print("Model Load Successfully")
        return model
    except Exception as e:
        print(f"Error loading model: {e}")
        model = None
        return model

def load_scaler():
    try:
        with open("scaler.pkl", 'rb') as file:
            scaler = pickle.load(file)
        print("Scaler loaded successfully")
        return scaler
    except:
        return ("Error while loading scaler")

def load_encoder():
    try:
        with open("encoder.pkl", 'rb') as file:
            encoder = pickle.load(file)
        print("Encoder loaded successfully")
        return encoder
    except:
        print("Error while loading encoder")

model = load_model()
encoder = load_encoder()
scaler = load_scaler()

def load_data(data):
    gender = 1 if data.Gender == 'Male' else 0
    age = data.Age

    neighbourhood = encoder.transform([data.Neighbourhood])[0]
    
    scholarship = 1 if data.Scholarship == 'Yes' else 0
    hipertension = 1 if data.Hipertension == 'Yes' else 0
    diabetes = 1 if data.Diabetes == 'Yes' else 0
    alcoholism = 1 if data.Alcoholism == 'Yes' else 0
    handcap = 1 if data.Handcap == 'Yes' else 0
    smsreceived = 1 if data.SMSreceived == "Yes" else 0
    waitingtime = data.WaitingTime
    appointmentDayWeek = data.AppointmentDayOfWeek

    lst = ['SameDay', 'Short', 'Mid', 'Long', 'VeryLong']
    waitingGroup = lst.index(data.WaitingGroup)

    cronic_count = hipertension + diabetes + alcoholism
    chronicGroup = 0
    if cronic_count >= -1 or cronic_count < 0:
        chronicGroup = 0
    elif cronic_count == 0 or cronic_count <= 1:
        chronicGroup = 1
    elif cronic_count > 1 or cronic_count <=3:
        chronicGroup = 2

    return np.array([gender, 
                    age, 
                    neighbourhood, 
                    scholarship, 
                    hipertension, 
                    diabetes, 
                    alcoholism, 
                    handcap, 
                    smsreceived, 
                    waitingtime, 
                    appointmentDayWeek,
                    waitingGroup,
                    chronicGroup])

class InputData(BaseModel):
    Gender: str
    Age: int
    Neighbourhood: str
    Scholarship: str
    Hipertension: str
    Diabetes: str
    Alcoholism: str
    Handcap: str
    SMSreceived: str
    WaitingTime: int
    AppointmentDayOfWeek: int
    WaitingGroup: str

@app.get("/")
def home():
    return {"Message": "This is an API for no-show prediction"}

@app.post("/predict")
def prediction(data: InputData):
    print("Data -> ", data)
    print("-"*40)
    raw_data = load_data(data)
    print("Row Data -> ", raw_data)
    print("-"*40)
    scaled_data = scaler.transform(raw_data.reshape(1, -1))
    prediction = model.predict(scaled_data)

    response = {"message": "Data received successfully!", "prediction": "Patient Will No-Show" if int(prediction[0]) == 1 else "Patient Will Show"}
    return response