Create app.py

app.py

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+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.")
+
+    # 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()