Update app.py

app.py

@@ -6,7 +6,7 @@ from sklearn.preprocessing import LabelEncoder
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score, classification_report
 
-# Step 1: Load dataset
+# Load dataset
 def load_data():
     df = pd.read_csv(r"C:\Users\pavilion\Desktop\INNOMATICS_PROJECTS(SL)\Student_Depresion_Prediction(Machine_Learning)\student_depression_dataset.csv")
     
@@ -20,67 +20,52 @@ def load_data():
 
     return df
 
-# Step 2: Preprocess data (Convert categorical features to numerical)
+# Preprocess data
 def preprocess_data(df):
     label_encoders = {}
-    
-    # Identify categorical columns
     categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
     
     for col in categorical_cols:
         le = LabelEncoder()
-        df[col] = le.fit_transform(df[col])  # Convert to numeric values
-        label_encoders[col] = le  # Store the encoder for later use
+        df[col] = le.fit_transform(df[col])  
+        label_encoders[col] = le  
 
     return df, label_encoders
 
-# Step 3: Train the Random Forest model
+# Train model
 def train_model(X_train, y_train):
     model = RandomForestClassifier(n_estimators=100, random_state=42)
-    model.fit(X_train, y_train)  # Train the model
+    model.fit(X_train, y_train)  
     return model
 
-# Step 4: Streamlit App
+# Streamlit App
 def main():
     st.set_page_config(page_title="Student Depression Prediction", layout="wide")
     
-    st.markdown("<h1 style='text-align: center;'>🎓 Student Depression Prediction App</h1>", unsafe_allow_html=True)
+    st.markdown("<h1 style='text-align: center;'>🎓 Student Depression Prediction</h1>", unsafe_allow_html=True)
     st.write("This app predicts whether a student is likely to experience depression based on various factors.")
 
-    # Load dataset
     df = load_data()
-
-    # Preprocess data
     df, label_encoders = preprocess_data(df)
 
-    # Split features (X) and target variable (y)
-    X = df.drop(columns=['Depression'])  # Features
-    y = df['Depression']  # Target (Depression)
+    X = df.drop(columns=['Depression'])  
+    y = df['Depression']  
 
-    # Train/Test split
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
-
-    # Train model
     model = train_model(X_train, y_train)
     y_pred = model.predict(X_test)
 
-    # Model evaluation
     accuracy = accuracy_score(y_test, y_pred)
     classification_rep = classification_report(y_test, y_pred)
 
-    # Train Model Button
     if st.button("🚀 Train Model"):
         st.subheader("✅ Model Trained Successfully!")
         st.write(f"**Accuracy:** {accuracy:.2f}")
         st.text("Classification Report:")
         st.text(classification_rep)
 
-    # Feature Importance
     feature_importances = model.feature_importances_
-    feature_importance_df = pd.DataFrame({
-        'Feature': X.columns,
-        'Importance': feature_importances
-    }).sort_values(by='Importance', ascending=False)
+    feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances}).sort_values(by='Importance', ascending=False)
 
     st.subheader("🔍 Feature Importance")
     fig, ax = plt.subplots()
@@ -92,14 +77,13 @@ def main():
 
     # User Input for Prediction
     st.subheader("📊 Predict Depression Risk")
-
     input_data = {}
     
     for col in X.columns:
-        if col in label_encoders:  # Categorical columns
+        if col in label_encoders:  
             input_data[col] = st.selectbox(f"Select {col}", label_encoders[col].classes_)
             input_data[col] = label_encoders[col].transform([input_data[col]])[0]
-        else:  # Numeric columns
+        else:  
             input_data[col] = st.number_input(f"Enter {col}", min_value=int(X[col].min()), max_value=int(X[col].max()), value=int(X[col].mean()))
 
     # Prediction Button
@@ -108,9 +92,25 @@ def main():
         prediction = model.predict(input_df)[0]
         
         if prediction == 1:
-            st.success("😨 The student is **at risk of depression**.")
+            st.error("😨 **The student is at risk of depression.**")
+            st.subheader("📌 Recommended Actions:")
+            st.markdown("""
+            1. **Seek Professional Help** 👩‍⚕️ - Consult a therapist or counselor.
+            2. **Stay Connected with Loved Ones** 🏡 - Talk to trusted friends or family.
+            3. **Engage in Physical Activities** 🏃‍♂️ - Exercise regularly to improve mood.
+            4. **Practice Mindfulness & Relaxation** 🧘‍♂️ - Try meditation, yoga, or deep breathing.
+            5. **Manage Academic Stress** 📚 - Break tasks into smaller steps and seek support.
+            """)
         else:
-            st.info("🥳 The student is **not at risk of depression**.")
+            st.success("🥳 **The student is not at risk of depression.**")
+            st.subheader("✅ Wellness Tips:")
+            st.markdown("""
+            1. **Maintain a Balanced Lifestyle** ⚖️ - Manage studies, social life, and self-care.
+            2. **Get Enough Sleep** 😴 - Ensure 7–9 hours of quality sleep for better mental health.
+            3. **Engage in Hobbies & Creative Activities** 🎨 - Pursue music, reading, or painting.
+            4. **Stay Active & Exercise** 🚴‍♂️ - Physical activity boosts energy and reduces anxiety.
+            5. **Support Others & Build Strong Friendships** 🤝 - Encourage open discussions and check on friends.
+            """)
 
 # Run the Streamlit app
 if __name__ == '__main__':