from fastapi import FastAPI
from fastapi.middleware.wsgi import WSGIMiddleware
from flask import Flask, render_template, request
from flask_cors import CORS
import numpy as np
import pickle

# Initialize Flask app
flask_app = Flask(__name__)
CORS(flask_app)

def load_model():
    with open('rf_model_ilpd_smote.pkl', 'rb') as model_file:
        return pickle.load(model_file)

rf_model = load_model()

@flask_app.route('/')
def home():
    return render_template('index.html')

@flask_app.route('/predict', methods=['POST'])
def predict():
    try:
        input_data = [
            float(request.form['Age']),
            float(request.form['Gender']),
            float(request.form['Total_Bilirubin']),
            float(request.form['Alkaline_Phosphatase']),
            float(request.form['Alamine_Aminotransferase']),
            float(request.form['Total_Protiens']),
            float(request.form['Albumin_and_Globulin_Ratio']),
        ]
        
        input_data = np.array(input_data).reshape(1, -1) 

        prediction = rf_model.predict(input_data)
        predicted_class = prediction[0]

        return render_template('index.html', prediction_text=f"Prediction: {'Have Liver Disease' if predicted_class == 1 else 'Not Have Liver Disease'}")

    except Exception as e:
        return render_template('index.html', prediction_text="Error: " + str(e))

# Initialize FastAPI app
app = FastAPI()  # Rename fastapi_app to app

# Mount Flask app inside FastAPI using WSGIMiddleware
app.mount("/", WSGIMiddleware(flask_app))