Update app.py

app.py

@@ -79,35 +79,40 @@ def analyze_sentiment(comment):
 
 def main():
     st.title("YouTube Comments Sentiment Analysis")
+    
+    # Create sidebar section for app description and links
+    st.sidebar.title("App Information")
+    st.sidebar.write("Welcome to the YouTube Comments Sentiment Analysis App.")
+    st.sidebar.write("This app extracts comments from a YouTube video and analyzes their sentiment.")
+    st.sidebar.write("Feel free to check out our other apps:")
+    
+    # Dropdown menu for other app links
+    app_links = {
+        "App 1": "https://your-app-1-url.com",
+        "App 2": "https://your-app-2-url.com"
+    }
+    selected_app = st.sidebar.selectbox("Select an App", list(app_links.keys()))
+    if st.sidebar.button("Go to App"):
+        st.sidebar.write(f"You are now redirected to {selected_app}")
+        st.sidebar.write(f"Link: {app_links[selected_app]}")
+        st.sidebar.success("Redirected successfully!")
+    
     st.write("Enter a YouTube video link below:")
-
     video_url = st.text_input("YouTube Video URL:")
+    
     if st.button("Extract Comments and Analyze"):
         video_id = extract_video_id(video_url)
         if video_id:
             comments_df = fetch_comments(video_id)
-            # Comments is a dataframe of just the comments text
-            # st.write("Top 100 Comments extracted\n", comments_df)
             comments_df['sentiment'] = comments_df['comment'].apply(lambda x: analyze_sentiment(x[:512]))
             sentiment_counts = comments_df['sentiment'].value_counts()
-            positive_count = comments_df['sentiment'].value_counts().get('Positive', 0)
-            negative_count = comments_df['sentiment'].value_counts().get('Negative', 0)
-            neutral_count = comments_df['sentiment'].value_counts().get('Neutral', 0)
-
-                # Create pie chart in col2 with custom colors
-            fig_pie = px.pie(values=[positive_count, negative_count, neutral_count],
-                            names=['Positive', 'Negative', 'Neutral'],
-                            title='Pie chart representations',
-                            color=sentiment_counts.index,  # Use sentiment categories as colors
-                            color_discrete_map={'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue'})
+            
+            # Create pie chart
+            fig_pie = px.pie(values=sentiment_counts.values, names=sentiment_counts.index, title='Sentiment Distribution')
             st.plotly_chart(fig_pie, use_container_width=True)
 
-            # Create bar chart below the pie chart with custom colors
-            fig_bar = px.bar(x=sentiment_counts.index, y=sentiment_counts.values,
-                            labels={'x': 'Sentiment', 'y': 'Count'},
-                            title='Bar plot representations',
-                            color=sentiment_counts.index,  # Use sentiment categories as colors
-                            color_discrete_map={'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue'})
+            # Create bar chart
+            fig_bar = px.bar(x=sentiment_counts.index, y=sentiment_counts.values, labels={'x': 'Sentiment', 'y': 'Count'}, title='Sentiment Counts')
             st.plotly_chart(fig_bar)