Update app.py

app.py

@@ -1,6 +1,8 @@
 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
@@ -10,48 +12,70 @@ def load_data(file):
     return pd.read_csv(file)
 
 def fetch_health_articles(query):
-    # Fetch mental health articles related to the 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]  # Return the top 5 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 = []
-    
-    # Use user data to fetch relevant health advice based on depression, anxiety, stress, etc.
     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():
-    # Set page config for a professional look
     st.set_page_config(
         page_title="Student Well-being Advisor",
         page_icon="📊",
@@ -59,7 +83,6 @@ def main():
         initial_sidebar_state="expanded",
     )
 
-    # Sidebar
     st.sidebar.title("Navigation")
     st.sidebar.write("Use the sidebar to navigate through the app.")
     st.sidebar.markdown("### 📂 Upload Data")
@@ -67,16 +90,12 @@ def main():
     st.sidebar.markdown("### 📊 Analysis & Advice")
     st.sidebar.write("Get detailed insights and personalized advice.")
 
-    # Main Content
     st.title("🎓 Student Well-being Advisor")
     st.subheader("Analyze data and provide professional mental health recommendations.")
-    st.write(
-        """
+    st.write("""
         This app helps identify areas of concern in students' well-being and provides personalized advice based on their responses.
-        """
-    )
+    """)
 
-    # File Upload
     st.markdown("## 📂 Upload Your Dataset")
     uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
     if uploaded_file:
@@ -85,13 +104,12 @@ def main():
         st.write("### Dataset Preview:")
         st.dataframe(df.head())
 
-        # Validate dataset columns
         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', 
+            '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]
@@ -99,21 +117,17 @@ def main():
         if missing_columns:
             st.error(f"The uploaded dataset is missing the following required columns: {', '.join(missing_columns)}")
         else:
-            # Handle missing values in the dataset
             if df.isnull().values.any():
                 st.warning("The dataset contains missing values. Rows with missing values will be skipped.")
                 df = df.dropna()
 
-            # Tabs for better organization
             tab1, tab2, tab3 = st.tabs(["🏠 Home", "📊 Analysis", "📰 Resources"])
 
             with tab1:
                 st.write("### Welcome to the Well-being Advisor!")
-                st.write(
-                    """
+                st.write("""
                     Use the tabs to explore data, generate advice, and access mental health resources.
-                    """
-                )
+                """)
 
             with tab2:
                 st.markdown("### 📊 Select a Row for Analysis")
@@ -122,15 +136,10 @@ def main():
                     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']})",
                 )
-
-                # Extract data for the selected row
                 row_data = df.loc[selected_row].to_dict()
-
-                # Show extracted details
                 st.write("### Selected User Details:")
                 st.json(row_data)
 
-                # Fetch and display health advice from articles
                 st.subheader("🔔 Health Advice Based on Articles")
                 advice = provide_advice_from_articles(row_data)
                 if advice:
@@ -139,8 +148,10 @@ def main():
                 else:
                     st.warning("No specific advice available based on this user's data.")
 
+                # Include graphs in analysis tab
+                plot_graphs(df)
+
             with tab3:
-                # Fetch and display mental health articles
                 st.subheader("📰 Mental Health Resources")
                 articles = fetch_health_articles("mental health")
                 if articles: