--- a
+++ b/gui.py
@@ -0,0 +1,114 @@
+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)