Update app.py

app.py

@@ -47,23 +47,16 @@ st.sidebar.header("Sentiment Analysis Controls")
 st.sidebar.subheader("Input")
 user_input = st.sidebar.text_input('Enter text for sentiment analysis')
 
-# Display explanation and detailed sentiment scores
+# Display sentiment analysis results
 def display_sentiment_analysis(vader_score, bert_result):
     st.subheader("Sentiment Analysis Results:")
     st.write(f"**VADER Sentiment Score**: {vader_score:.2f}")
     st.write(f"**BERT Sentiment**: {bert_result['label']} ({bert_result['score']:.2f})")
     
-    # Display sentiment breakdown in a bar chart
     sentiment_data = {'Positive': max(0, vader_score), 'Negative': min(0, vader_score), 'Neutral': 1 - abs(vader_score)}
     sentiment_df = pd.DataFrame(list(sentiment_data.items()), columns=["Sentiment", "Score"])
+    st.bar_chart(sentiment_df.set_index("Sentiment"))
     
-    # Set color dynamically based on sentiment
-    bar_colors = ['green' if sentiment == 'Positive' else 'red' if sentiment == 'Negative' else 'gray' for sentiment in sentiment_df['Sentiment']]
-    
-    # Display the bar chart
-    st.bar_chart(sentiment_df.set_index("Sentiment"), color=bar_colors)
-
-    # Generate and display the word cloud
     wordcloud_img = f'data:image/png;base64,{generate_wordcloud(user_input)}'
     st.image(wordcloud_img, use_column_width=True)
 
@@ -71,57 +64,57 @@ def display_sentiment_analysis(vader_score, bert_result):
 if st.sidebar.button('Analyze Sentiment'):
     if user_input:
         with st.spinner('Analyzing text...'):
-            # VADER sentiment analysis
             vader_score = vader_analyzer.polarity_scores(user_input)['compound']
-            
-            # BERT sentiment analysis
             bert_result = bert_sentiment(user_input)[0]
-            
-            # Display sentiment analysis details
             display_sentiment_analysis(vader_score, bert_result)
     else:
         st.warning("Please enter some text for analysis.")
 
-# Section for past sentiment trends
+# Past sentiment trends
 st.subheader("Past Sentiment Trends (Last 14 Days)")
 fig1 = px.line(df, x='Date', y='Sentiment Score', title='Past Sentiment Trends', markers=True, line_shape='spline')
 st.plotly_chart(fig1)
 
-# Section for future sentiment predictions
+# Future sentiment predictions
 st.subheader("Sentiment Prediction for Next 7 Days")
 fig2 = px.line(future_df, x='Date', y='Predicted Sentiment', title='Sentiment Prediction for Next 7 Days', markers=True, line_shape='spline')
 st.plotly_chart(fig2)
 
-# Sentiment Distribution Pie Chart
+# Sentiment distribution pie chart
 st.subheader("Sentiment Distribution")
 fig3 = px.pie(values=[sum(df['Sentiment Score'] > 0), sum(df['Sentiment Score'] <= 0)], 
               names=['Positive', 'Negative'], title='Sentiment Distribution', hole=0.3)
 st.plotly_chart(fig3)
 
-# Histogram of Sentiment Scores (Distribution)
+# Histogram of Sentiment Scores
 st.subheader("Sentiment Score Distribution (Past 14 Days)")
 fig4 = px.histogram(df, x='Sentiment Score', nbins=20, title="Sentiment Score Distribution")
 st.plotly_chart(fig4)
 
-# Time-Series Heatmap for Sentiment Over Time
+# Sentiment heatmap (fixed version)
 st.subheader("Sentiment Heatmap (Past 14 Days)")
-df['Day'] = df['Date'].dt.dayofweek
-df['Hour'] = df['Date'].dt.hour
-heatmap_data = df.pivot('Day', 'Hour', 'Sentiment Score')
-fig5 = px.imshow(heatmap_data, title="Heatmap of Sentiment Over Time", labels={'x': 'Hour of Day', 'y': 'Day of Week'})
+df['Day'] = df['Date'].dt.dayofweek  # Monday=0, Sunday=6
+heatmap_data = df.groupby('Day')['Sentiment Score'].mean().reset_index()
+heatmap_data = heatmap_data.pivot(index='Day', columns=None, values='Sentiment Score')
+fig5 = px.imshow(
+    [heatmap_data.values], 
+    title="Heatmap of Sentiment Over Time", 
+    labels={'x': 'Day of Week', 'y': 'Sentiment Score'}, 
+    color_continuous_scale='RdBu'
+)
 st.plotly_chart(fig5)
 
-# Scatter plot of Sentiment over Time
+# Sentiment scatter plot
 st.subheader("Sentiment Scatter Plot (Past 14 Days)")
 fig6 = px.scatter(df, x='Date', y='Sentiment Score', title='Sentiment Over Time')
 st.plotly_chart(fig6)
 
-# Rolling average of Sentiment (7-day window)
+# Rolling average sentiment
 st.subheader("Rolling Average of Sentiment (7-Day Window)")
 df['Rolling Avg Sentiment'] = df['Sentiment Score'].rolling(window=7).mean()
 fig7 = px.line(df, x='Date', y='Rolling Avg Sentiment', title="Rolling Average of Sentiment (7-Day Window)")
 st.plotly_chart(fig7)
 
-# Reset button to clear inputs and visualizations
+# Reset button
 if st.sidebar.button('Reset Analysis'):
     st.experimental_rerun()