Update app.py

app.py

@@ -5,7 +5,8 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import requests
 
-news_api_key = "fe1e6bcbbf384b3e9220a7a1138805e0"  # Replace with your News API key
+# News API Key
+news_api_key = "your_news_api_key"  # Replace with your News API key
 
 @st.cache_data
 def load_data(file):
@@ -21,50 +22,42 @@ def fetch_health_articles(query):
         st.error("Failed to fetch news articles. Please check your API key or try again later.")
         return []
 
-def provide_advice_from_selected_row(data):
+def stress_level_to_string(stress_level):
+    """Convert numerical stress level (0, 1, 2) to a string representation."""
+    if stress_level == 0:
+        return "Low"
+    elif stress_level == 1:
+        return "Moderate"
+    else:
+        return "High"
+
+def provide_advice_from_articles(data):
     advice = []
+    stress_level = stress_level_to_string(data['stress_level'])
     
-    stress_level = data['stress_level']
-    anxiety_level = data['anxiety_level']
-    depression_level = data['depression']
-    
-    # Generate advice based on the stress level of the selected row
-    if stress_level > 7:
-        advice.append("🔴 **High Stress Levels Detected**: It's critical to manage stress effectively. Please explore the following articles to help you manage stress:")
-        articles = fetch_health_articles("high stress management")
+    if stress_level == "High":
+        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']})")
-    elif stress_level > 4:
-        advice.append("🟡 **Moderate Stress Levels**: It's important to manage moderate stress. Here are some helpful articles and tips:")
-        articles = fetch_health_articles("moderate stress management")
+    elif stress_level == "Moderate":
+        advice.append("Searching for articles related to moderate stress...")
+        articles = fetch_health_articles("moderate stress")
         for article in articles:
             advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
     else:
-        advice.append("🟢 **Low Stress Levels**: Great! You're in a good place, but it's still important to maintain healthy stress management. Here are some resources to help:")
-        articles = fetch_health_articles("stress relief techniques")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    
-    # Additional advice based on anxiety and depression levels
-    if anxiety_level > 7:
-        advice.append("\n🔴 **High Anxiety Levels**: Managing anxiety is crucial. Here are some resources to help manage anxiety:")
-        articles = fetch_health_articles("high anxiety management")
+        advice.append("Searching for articles related to low stress...")
+        articles = fetch_health_articles("low stress")
         for article in articles:
             advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
     
-    if depression_level > 7:
-        advice.append("\n🔴 **High Depression Levels**: It's important to seek professional support for depression. Here are some helpful resources:")
-        articles = fetch_health_articles("high depression management")
-        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
+    """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))
@@ -74,8 +67,8 @@ def plot_graphs(data):
 
 def main():
     st.set_page_config(
-        page_title="Stress Management Advisor",
-        page_icon="🧠",
+        page_title="Student Well-being Advisor",
+        page_icon="📊",
         layout="wide",
         initial_sidebar_state="expanded",
     )
@@ -87,10 +80,10 @@ def main():
     st.sidebar.markdown("### 📊 Analysis & Advice")
     st.sidebar.write("Get detailed insights and personalized advice.")
 
-    st.title("🎓 Stress Management Advisor")
-    st.subheader("Analyze stress levels and receive professional stress management recommendations.")
+    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 related to stress in individuals and provides personalized advice based on their responses.
+        This app helps identify areas of concern in students' well-being and provides personalized advice based on their responses.
     """)
 
     st.markdown("## 📂 Upload Your Dataset")
@@ -121,7 +114,7 @@ def main():
             tab1, tab2, tab3 = st.tabs(["🏠 Home", "📊 Analysis", "📰 Resources"])
 
             with tab1:
-                st.write("### Welcome to the Stress Management Advisor!")
+                st.write("### Welcome to the Well-being Advisor!")
                 st.write("""
                     Use the tabs to explore data, generate advice, and access mental health resources.
                 """)
@@ -131,26 +124,26 @@ def main():
                 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']})",
+                    format_func=lambda x: f"Row {x} - Stress Level: {stress_level_to_string(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 Selected Row")
-                advice = provide_advice_from_selected_row(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 stress level.")
+                    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("stress relief")
+                articles = fetch_health_articles("mental health")
                 if articles:
                     for article in articles:
                         st.write(f"**{article['title']}**")