Update app.py

app.py

@@ -1,12 +1,33 @@
-# app.py
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.ensemble import RandomForestRegressor
-import matplotlib.pyplot as plt
-import seaborn as sns
+# Comprehensive imports with error handling
+import sys
+import subprocess
+
+# Ensure required packages are installed
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+try:
+    import streamlit as st
+    import pandas as pd
+    import numpy as np
+    from sklearn.model_selection import train_test_split
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.ensemble import RandomForestRegressor
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+except ImportError as e:
+    print(f"Missing package: {e}")
+    package = str(e).split("'")[1]
+    install(package)
+    # Retry imports
+    import streamlit as st
+    import pandas as pd
+    import numpy as np
+    from sklearn.model_selection import train_test_split
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.ensemble import RandomForestRegressor
+    import matplotlib.pyplot as plt
+    import seaborn as sns
 
 # Seed for reproducibility
 np.random.seed(42)
@@ -40,143 +61,149 @@ def generate_student_data(n_samples=500):
     
     return pd.DataFrame(data)
 
-# Generate dataset and prepare model
-df = generate_student_data()
-X = df.drop('final_grade', axis=1)
-y = df['final_grade']
-
-# Split and scale data
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-scaler = StandardScaler()
-X_train_scaled = scaler.fit_transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-
-# Train Random Forest model
-rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
-rf_model.fit(X_train_scaled, y_train)
-
-# Streamlit App
-st.set_page_config(page_title="Student Performance Predictor", page_icon="🎓")
-
-# App Title
-st.title("🎓 Student Performance Prediction Model")
-
-# Sidebar for Input
-st.sidebar.header("Student Information")
-
-# Input Sliders and Selectors
-attendance_rate = st.sidebar.slider(
-    "Attendance Rate", 
-    min_value=0.5, 
-    max_value=1.0, 
-    value=0.85, 
-    step=0.05
-)
-
-study_hours = st.sidebar.slider(
-    "Weekly Study Hours", 
-    min_value=0, 
-    max_value=40, 
-    value=25
-)
-
-prev_gpa = st.sidebar.slider(
-    "Previous Semester GPA", 
-    min_value=2.0, 
-    max_value=4.0, 
-    value=3.5, 
-    step=0.1
-)
-
-socioeconomic_status = st.sidebar.selectbox(
-    "Socioeconomic Status", 
-    [1, 2, 3], 
-    index=1,
-    format_func=lambda x: {1:"Low", 2:"Medium", 3:"High"}[x]
-)
-
-extracurricular_activities = st.sidebar.slider(
-    "Extracurricular Activities", 
-    min_value=0, 
-    max_value=4, 
-    value=2
-)
-
-family_support = st.sidebar.selectbox(
-    "Family Support", 
-    [0, 1], 
-    index=1,
-    format_func=lambda x: "Yes" if x == 1 else "No"
-)
-
-# Prediction Function
-def predict_student_performance(input_data):
-    input_scaled = scaler.transform(input_data)
-    prediction = rf_model.predict(input_scaled)[0]
-    return np.round(prediction, 2)
-
-# Predict Button
-if st.sidebar.button("Predict Performance"):
-    # Prepare input data
-    input_data = np.array([[
-        attendance_rate, 
-        study_hours, 
-        prev_gpa, 
-        socioeconomic_status, 
-        extracurricular_activities, 
-        family_support
-    ]])
-    
-    # Make prediction
-    predicted_grade = predict_student_performance(input_data)
-    
-    # Display prediction
-    st.subheader("Prediction Results")
-    
-    # Color-coded performance indicator
-    if predicted_grade >= 8:
-        color = "green"
-        performance = "Excellent"
-    elif predicted_grade >= 6:
-        color = "blue"
-        performance = "Good"
-    elif predicted_grade >= 4:
-        color = "orange"
-        performance = "Average"
-    else:
-        color = "red"
-        performance = "Needs Improvement"
-    
-    st.markdown(f"""
-    <div style="background-color:{color}; padding:10px; border-radius:10px;">
-    <h2 style="color:white; text-align:center;">
-    Predicted Final Grade: {predicted_grade}/10
-    <br>Performance: {performance}
-    </h2>
-    </div>
-    """, unsafe_allow_html=True)
-
-# Feature Importance Visualization
-st.subheader("Feature Importance")
-feature_importance = pd.DataFrame({
-    'feature': X.columns,
-    'importance': rf_model.feature_importances_
-}).sort_values('importance', ascending=False)
-
-fig, ax = plt.subplots(figsize=(10, 6))
-sns.barplot(x='importance', y='feature', data=feature_importance, ax=ax)
-plt.title('Factors Influencing Student Performance')
-plt.xlabel('Importance Score')
-st.pyplot(fig)
-
-# Additional Information
-st.markdown("""
-### About the Model
-This machine learning model predicts student performance based on:
-- Attendance Rate
-- Weekly Study Hours
-- Previous Semester GPA
-- Socioeconomic Status
-- Extracurricular Activities
-- Family Support
-""")
+# Main application logic
+def main():
+    # Generate dataset and prepare model
+    df = generate_student_data()
+    X = df.drop('final_grade', axis=1)
+    y = df['final_grade']
+
+    # Split and scale data
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+
+    # Train Random Forest model
+    rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
+    rf_model.fit(X_train_scaled, y_train)
+
+    # Streamlit App Configuration
+    st.set_page_config(page_title="Student Performance Predictor", page_icon="🎓")
+
+    # App Title
+    st.title("🎓 Student Performance Prediction Model")
+
+    # Sidebar for Input
+    st.sidebar.header("Student Information")
+
+    # Input Sliders and Selectors
+    attendance_rate = st.sidebar.slider(
+        "Attendance Rate", 
+        min_value=0.5, 
+        max_value=1.0, 
+        value=0.85, 
+        step=0.05
+    )
+
+    study_hours = st.sidebar.slider(
+        "Weekly Study Hours", 
+        min_value=0, 
+        max_value=40, 
+        value=25
+    )
+
+    prev_gpa = st.sidebar.slider(
+        "Previous Semester GPA", 
+        min_value=2.0, 
+        max_value=4.0, 
+        value=3.5, 
+        step=0.1
+    )
+
+    socioeconomic_status = st.sidebar.selectbox(
+        "Socioeconomic Status", 
+        [1, 2, 3], 
+        index=1,
+        format_func=lambda x: {1:"Low", 2:"Medium", 3:"High"}[x]
+    )
+
+    extracurricular_activities = st.sidebar.slider(
+        "Extracurricular Activities", 
+        min_value=0, 
+        max_value=4, 
+        value=2
+    )
+
+    family_support = st.sidebar.selectbox(
+        "Family Support", 
+        [0, 1], 
+        index=1,
+        format_func=lambda x: "Yes" if x == 1 else "No"
+    )
+
+    # Prediction Function
+    def predict_student_performance(input_data):
+        input_scaled = scaler.transform(input_data)
+        prediction = rf_model.predict(input_scaled)[0]
+        return np.round(prediction, 2)
+
+    # Predict Button
+    if st.sidebar.button("Predict Performance"):
+        # Prepare input data
+        input_data = np.array([[
+            attendance_rate, 
+            study_hours, 
+            prev_gpa, 
+            socioeconomic_status, 
+            extracurricular_activities, 
+            family_support
+        ]])
+        
+        # Make prediction
+        predicted_grade = predict_student_performance(input_data)
+        
+        # Display prediction
+        st.subheader("Prediction Results")
+        
+        # Color-coded performance indicator
+        if predicted_grade >= 8:
+            color = "green"
+            performance = "Excellent"
+        elif predicted_grade >= 6:
+            color = "blue"
+            performance = "Good"
+        elif predicted_grade >= 4:
+            color = "orange"
+            performance = "Average"
+        else:
+            color = "red"
+            performance = "Needs Improvement"
+        
+        st.markdown(f"""
+        <div style="background-color:{color}; padding:10px; border-radius:10px;">
+        <h2 style="color:white; text-align:center;">
+        Predicted Final Grade: {predicted_grade}/10
+        <br>Performance: {performance}
+        </h2>
+        </div>
+        """, unsafe_allow_html=True)
+
+    # Feature Importance Visualization
+    st.subheader("Feature Importance")
+    feature_importance = pd.DataFrame({
+        'feature': X.columns,
+        'importance': rf_model.feature_importances_
+    }).sort_values('importance', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    sns.barplot(x='importance', y='feature', data=feature_importance, ax=ax)
+    plt.title('Factors Influencing Student Performance')
+    plt.xlabel('Importance Score')
+    st.pyplot(fig)
+
+    # Additional Information
+    st.markdown("""
+    ### About the Model
+    This machine learning model predicts student performance based on:
+    - Attendance Rate
+    - Weekly Study Hours
+    - Previous Semester GPA
+    - Socioeconomic Status
+    - Extracurricular Activities
+    - Family Support
+    """)
+
+# Run the main application
+if __name__ == "__main__":
+    main()