Update src/streamlit_app.py

src/streamlit_app.py

@@ -174,7 +174,7 @@ def create_topic_word_bubbles(df_topic_data):
         # Set text to the word
         text='word', 
         hover_name='word', 
-        size_max=80,
+        size_max=40,
         title='Topic Word Weights (Bubble Chart)',
         color_discrete_sequence=px.colors.qualitative.Bold,
         labels={
@@ -370,7 +370,8 @@ def generate_html_report(df, text_input, elapsed_time, df_topic_data):
     if df_topic_data is not None and not df_topic_data.empty:
         bubble_figure = create_topic_word_bubbles(df_topic_data)
         if bubble_figure:
-            topic_charts_html += f'<div class="chart-box">{bubble_figure.to_html(full_html=False, include_plotlyjs="cdn")}</div>'
+            
+            topic_charts_html += f'<div class="chart-box">{bubble_figure.to_html(full_html=False, include_plotlyjs="cdn", config={"responsive": True})}</div>'
         else:
             topic_charts_html += '<p style="color: red;">Error: Topic modeling data was available but visualization failed.</p>'
     else:
@@ -495,7 +496,9 @@ with tab2:
     **Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"
     
     **Results:** Results are compiled into a single, comprehensive **HTML report** and a **CSV file** for easy download and sharing.
+    
     **How to Use:** Type or paste your text into the text area below, press Ctrl + Enter, and then click the 'Results' button.
+    
     **Technical issues:** If your connection times out, please refresh the page or reopen the app's URL.
     """)