import pickle

import numpy as np

from flask import Flask, render_template, request, redirect, url_for

from sklearn.preprocessing import StandardScaler

from keras.models import load_model

app = Flask(__name__)

# Load the trained machine learning model

model = load_model('my_model.h5')

# Load the scaler object used during training

scaler = StandardScaler()

scaler_file = 'scaler.pkl'

with open(scaler_file, 'rb') as f:

    scaler = pickle.load(f)

# Define the home page route

@app.route('/')

def home():

    return render_template('home.html', favicon_url=url_for('static', filename='images/favicon.png'))

# Define the form page route

@app.route('/form')

def form():

    return render_template('form.html')

@app.route('/results', methods=['GET', 'POST'])

def results():

    if request.method == 'POST':

        # Print out the form data for debugging

        print(request.form)

        # Extract the form data

        age = int(request.form.get('age'))

        gender = request.form.get('gender')

        if gender == 'Male': gender = 1

        else: gender = 2

        air_pollution = int(request.form.get('air_pollution'))

        alcohol_use = int(request.form.get('alcohol_use'))

        dust_allergy = int(request.form.get('dust_allergy'))

        occupational_hazards = int(request.form.get('occupational_hazards'))

        genetic_risk = int(request.form.get('genetic_risk'))

        chronic_lung_disease = int(request.form.get('chronic_lung_disease'))

        balanced_diet = int(request.form.get('balanced_diet'))

        obesity = int(request.form.get('obesity'))

        smoking = int(request.form.get('smoking'))

        passive_smoker = int(request.form.get('passive_smoker'))

        chest_pain = int(request.form.get('chest_pain'))

        coughing_blood = int(request.form.get('coughing_blood'))

        fatigue = int(request.form.get('fatigue'))

        weight_loss = int(request.form.get('weight_loss'))

        shortness_of_breath = int(request.form.get('shortness_of_breath'))

        wheezing = int(request.form.get('wheezing'))

        swallowing_difficulty = int(request.form.get('swallowing_difficulty'))

        clubbing = int(request.form.get('clubbing'))

        frequent_cold = int(request.form.get('frequent_cold'))

        dry_cough = int(request.form.get('dry_cough'))

        snoring = int(request.form.get('snoring'))

        data = np.zeros((23))

        data[0] = age

        data[1] = gender

        data[2] = air_pollution

        data[3] = alcohol_use

        data[4] = dust_allergy

        data[5] = occupational_hazards

        data[6] = genetic_risk

        data[7] = chronic_lung_disease

        data[8] = balanced_diet

        data[9] = obesity

        data[10] = smoking

        data[11] = passive_smoker

        data[12] = chest_pain

        data[13] = coughing_blood

        data[14] = fatigue

        data[15] = weight_loss

        data[16] = shortness_of_breath

        data[17] = wheezing

        data[18] = swallowing_difficulty

        data[19] = clubbing

        data[20] = frequent_cold

        data[21] = dry_cough

        data[22] = snoring

        # Convert the list to a numpy array with the desired shape

        new_data = np.array([data])

        # Standardize the new data using the loaded scaler

        new_data_scaled = scaler.transform(new_data)

        # Make predictions

        predictions = model.predict(new_data_scaled)

        # Convert the predictions to class labels

        predicted_classes = np.argmax(predictions, axis=1)

        # Determine the predicted outcome based on the prediction

        if predicted_classes[0] == 0:

            outcome = 'Low'

        elif predicted_classes[0] == 1:

            outcome = 'Medium'

        else:

            outcome = 'High'

        # Render the results template with the predicted outcome

        return render_template('results.html', outcome=outcome)

    else:

        # If the request method is not POST, redirect to the home page

        return redirect('/')

if __name__ == '__main__':

    app.run(debug=True)