Update app.py

app.py

@@ -1,25 +1,23 @@
 import streamlit as st
+from tensorflow.keras.models import load_model
 import pickle
 import numpy as np
-from tensorflow.keras.models import load_model
 
 # Load the saved models
 with open('rf_model.pkl', 'rb') as file:
     rf_model = pickle.load(file)
 
-reloaded_model = load_model('deep_model.h5')
-
-# Define the function to make predictions
-def make_prediction(rf_model, nn_model, input_data):
-    # Predictions from RandomForestRegressor
-    rf_predictions = rf_model.predict(input_data)
+deep_model = load_model('deep_model.h5')
 
-    # Predictions from Neural Network model
-    nn_predictions = nn_model.predict(input_data).flatten()
+# Define the function to make predictions using the RandomForestRegressor model
+def make_rf_prediction(model, input_data):
+    prediction = model.predict(input_data)
+    return prediction
 
-    # Combine predictions
-    combined_predictions = (rf_predictions + nn_predictions) / 2  # Taking the average of predictions
-    return combined_predictions
+# Define the function to make predictions using the deep learning model
+def make_deep_prediction(model, input_data):
+    prediction = model.predict(input_data).flatten()
+    return prediction
 
 # Define the function to calculate GPA
 def calculate_gpa(total_score):
@@ -40,9 +38,9 @@ def main():
     st.set_page_config(page_title="Exam Score Prediction", layout="wide")
 
     # Add a title and description
-    st.title("Exam Score Prediction")
+    st.title("Exam Score Prediction (DEEP NEURAL NETWORKS AND ENSEMBLE APPROACH")
     st.markdown(
-        "This app predicts exam scores based on input features such as level, course units, attendance, mid-semester score, and assignments using a combined model."
+        "This app predicts exam scores based on input features such as level, course units, attendance, mid-semester score, and assignments."
     )
 
     # Create input fields
@@ -57,19 +55,28 @@ def main():
 
     # Make prediction
     if st.button("Predict Exam Score"):
-        # Add total_score to the input data
+        # Create input data
         input_data = np.array([[level, course_units, attendance, mid_semester, assignments]])
-        prediction = make_prediction(rf_model, reloaded_model, input_data)
+
+        # Make predictions using both models
+        rf_prediction = make_rf_prediction(rf_model, input_data)
+        deep_prediction = make_deep_prediction(deep_model, input_data)
+
+        # Combine predictions
+        combined_prediction = (rf_prediction + deep_prediction) / 2
 
         # Calculate total score
-        total_score = attendance + mid_semester + assignments + prediction[0]
+        total_score = attendance + mid_semester + assignments + combined_prediction[0]
 
-        st.write(f"Predicted Exam Score: {prediction[0]:.2f}")
-        st.write(f"Total Score: {total_score}")
+        # Ensure total score does not exceed 100
+        total_score = min(total_score, 100)
 
+        st.write(f"Predicted Exam Score: {combined_prediction[0]:.2f}")
+        st.write(f"Total Score: {total_score:.2f}")
+        
         # Calculate GPA
         gpa = calculate_gpa(total_score)
         st.write(f"Predicted GPA: {gpa}")
 
 if __name__ == '__main__':
-    main()
+    main()