Blame for app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report


def load_data():
    df = pd.read_csv(r"C:\Users\pavilion\Desktop\INNOMATICS_PROJECTS(SL)\Student_Depresion_Prediction(Machine_Learning)\student_depression_dataset.csv")
    
    df.drop(columns=['id', 'City', 'Work Pressure', 'Job Satisfaction'], inplace=True, errors='ignore')

    if 'Profession' in df.columns:
        df = df[df['Profession'].str.lower() == 'student']
        df.drop(columns=['Profession'], inplace=True, errors='ignore')

    return df


def preprocess_data(df):
    label_encoders = {}
    categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
    
    for col in categorical_cols:
        le = LabelEncoder()
        df[col] = le.fit_transform(df[col])  
        label_encoders[col] = le  

    return df, label_encoders


def train_model(X_train, y_train):
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)  
    return model


def main():
    st.set_page_config(page_title="Student Depression Prediction", layout="wide")
    
    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.")

    df = load_data()
    df, label_encoders = preprocess_data(df)

    X = df.drop(columns=['Depression'])  
    y = df['Depression']  

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
    model = train_model(X_train, y_train)
    y_pred = model.predict(X_test)

    accuracy = accuracy_score(y_test, y_pred)
    classification_rep = classification_report(y_test, y_pred)

    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_importances = model.feature_importances_
    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()
    ax.barh(feature_importance_df['Feature'], feature_importance_df['Importance'], color='skyblue')
    ax.set_xlabel("Importance")
    ax.set_ylabel("Feature")
    ax.set_title("Feature Importance in Random Forest")
    st.pyplot(fig)

   
    st.subheader("📊 Predict Depression Risk")
    input_data = {}
    
    for col in X.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:  
            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()))

    
    if st.button("🔮 Predict"):
        input_df = pd.DataFrame([input_data])
        prediction = model.predict(input_df)[0]
        
        if prediction == 1:
            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.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.
            """)


if __name__ == '__main__':
    main()