app.py

import os
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import requests

news_api_key = "fe1e6bcbbf384b3e9220a7a1138805e0"  # Replace with your News API key

@st.cache_data
def load_data(file):
    return pd.read_csv(file)

def fetch_health_articles(query):
    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={news_api_key}"
    response = requests.get(url)
    if response.status_code == 200:
        articles = response.json().get('articles', [])
        return articles[:5]
    else:
        st.error("Failed to fetch news articles. Please check your API key or try again later.")
        return []

def provide_advice_from_articles(data):
    advice = []
    if data['depression'] > 7:
        advice.append("Searching for articles related to high depression...")
        articles = fetch_health_articles("high depression")
        for article in articles:
            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
    elif data['anxiety_level'] > 7:
        advice.append("Searching for articles related to high anxiety...")
        articles = fetch_health_articles("high anxiety")
        for article in articles:
            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
    elif data['stress_level'] > 7:
        advice.append("Searching for articles related to high stress...")
        articles = fetch_health_articles("high stress")
        for article in articles:
            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
    else:
        advice.append("Searching for general health advice articles...")
        articles = fetch_health_articles("mental health")
        for article in articles:
            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
    return advice

def plot_graphs(data):
    # Create subplots for visualization
    st.markdown("### 📊 Data Visualizations")
    st.write("Explore key insights through visualizations.")

    # Histogram for depression
    st.markdown("#### Histogram of Depression Levels")
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(data['depression'], bins=20, color='skyblue', edgecolor='black')
    ax.set_title("Histogram of Depression Levels")
    ax.set_xlabel("Depression Level")
    ax.set_ylabel("Frequency")
    st.pyplot(fig)

    # Scatter plot for anxiety vs. depression
    st.markdown("#### Scatter Plot: Anxiety vs Depression")
    fig, ax = plt.subplots(figsize=(6, 4))
    sns.scatterplot(x=data['anxiety_level'], y=data['depression'], ax=ax, color='blue')
    ax.set_title("Anxiety Level vs Depression")
    ax.set_xlabel("Anxiety Level")
    ax.set_ylabel("Depression")
    st.pyplot(fig)

    # Correlation heatmap
    st.markdown("#### Correlation Heatmap")
    fig, ax = plt.subplots(figsize=(10, 8))
    sns.heatmap(data.corr(), annot=True, cmap="coolwarm", ax=ax)
    ax.set_title("Correlation Heatmap")
    st.pyplot(fig)

def main():
    st.set_page_config(
        page_title="Student Well-being Advisor",
        page_icon="📊",
        layout="wide",
        initial_sidebar_state="expanded",
    )

    st.sidebar.title("Navigation")
    st.sidebar.write("Use the sidebar to navigate through the app.")
    st.sidebar.markdown("### 📂 Upload Data")
    st.sidebar.write("Start by uploading your dataset for analysis.")
    st.sidebar.markdown("### 📊 Analysis & Advice")
    st.sidebar.write("Get detailed insights and personalized advice.")

    st.title("🎓 Student Well-being Advisor")
    st.subheader("Analyze data and provide professional mental health recommendations.")
    st.write("""
        This app helps identify areas of concern in students' well-being and provides personalized advice based on their responses.
    """)

    st.markdown("## 📂 Upload Your Dataset")
    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
    if uploaded_file:
        df = load_data(uploaded_file)
        st.success("Dataset uploaded successfully!")
        st.write("### Dataset Preview:")
        st.dataframe(df.head())

        required_columns = [
            'anxiety_level', 'self_esteem', 'mental_health_history', 'depression',
            'headache', 'blood_pressure', 'sleep_quality', 'breathing_problem',
            'noise_level', 'living_conditions', 'safety', 'basic_needs',
            'academic_performance', 'study_load', 'teacher_student_relationship',
            'future_career_concerns', 'social_support', 'peer_pressure',
            'extracurricular_activities', 'bullying', 'stress_level'
        ]
        missing_columns = [col for col in required_columns if col not in df.columns]
        
        if missing_columns:
            st.error(f"The uploaded dataset is missing the following required columns: {', '.join(missing_columns)}")
        else:
            if df.isnull().values.any():
                st.warning("The dataset contains missing values. Rows with missing values will be skipped.")
                df = df.dropna()

            tab1, tab2, tab3 = st.tabs(["🏠 Home", "📊 Analysis", "📰 Resources"])

            with tab1:
                st.write("### Welcome to the Well-being Advisor!")
                st.write("""
                    Use the tabs to explore data, generate advice, and access mental health resources.
                """)

            with tab2:
                st.markdown("### 📊 Select a Row for Analysis")
                selected_row = st.selectbox(
                    "Select a row (based on index) to analyze:",
                    options=df.index,
                    format_func=lambda x: f"Row {x} - Stress Level: {df.loc[x, 'stress_level']}, Anxiety: {df.loc[x, 'anxiety_level']} (Depression: {df.loc[x, 'depression']})",
                )
                row_data = df.loc[selected_row].to_dict()
                st.write("### Selected User Details:")
                st.json(row_data)

                st.subheader("🔔 Health Advice Based on Articles")
                advice = provide_advice_from_articles(row_data)
                if advice:
                    for i, tip in enumerate(advice, 1):
                        st.write(f"📌 **{i}.** {tip}")
                else:
                    st.warning("No specific advice available based on this user's data.")

                # Include graphs in analysis tab
                plot_graphs(df)

            with tab3:
                st.subheader("📰 Mental Health Resources")
                articles = fetch_health_articles("mental health")
                if articles:
                    for article in articles:
                        st.write(f"**{article['title']}**")
                        st.write(f"{article['description']}")
                        st.write(f"[Read more]({article['url']})")
                else:
                    st.write("No articles available at the moment.")

if __name__ == "__main__":
    main()