Update src/streamlit_app.py

src/streamlit_app.py

@@ -311,8 +311,8 @@ def fig_to_image_buffer(fig):
         img_buffer = BytesIO(img_bytes)
         return img_buffer
     except Exception as e:
-        # Print error to console/logs, as Streamlit elements cannot be used here
-        print(f"Error converting Plotly figure to image (Check Kaleido installation/permissions): {e}")
+        # Changed the error message to be more explicit about the Kaleido dependency issue
+        print(f"Plotly image export failed (Kaleido dependency error): {e}. This means the PPTX will contain placeholder slides where charts should be.")
         return None
 
 # --- PPTX GENERATION FUNCTION ---
@@ -408,7 +408,7 @@ def generate_pptx_report(df, text_input, elapsed_time, df_topic_data, reverse_ca
         slide.shapes.title.text = "Entity Distribution Treemap (Chart Failed)"
         # FIX: Safety check for placeholder index 1
         if len(slide.placeholders) > 1:
-            slide.placeholders[1].text = "Chart generation failed. Check app logs for Kaleido errors."
+            slide.placeholders[1].text = "Chart generation failed, likely due to a missing 'kaleido' dependency for static image export."
 
 
     # 5. Entity Count Bar Chart Slide (Visualization)
@@ -434,7 +434,7 @@ def generate_pptx_report(df, text_input, elapsed_time, df_topic_data, reverse_ca
         slide.shapes.title.text = "Total Entities per Category (Chart Failed)"
         # FIX: Safety check for placeholder index 1
         if len(slide.placeholders) > 1:
-            slide.placeholders[1].text = "Chart generation failed. Check app logs for Kaleido errors."
+            slide.placeholders[1].text = "Chart generation failed, likely due to a missing 'kaleido' dependency for static image export."
 
 
     # 6. Topic Modeling Bubble Chart Slide
@@ -452,7 +452,7 @@ def generate_pptx_report(df, text_input, elapsed_time, df_topic_data, reverse_ca
             slide.shapes.title.text = "Topic Word Weights (Chart Failed)"
             # FIX: Safety check for placeholder index 1
             if len(slide.placeholders) > 1:
-                slide.placeholders[1].text = "Chart generation failed. Check app logs for Kaleido errors."
+                slide.placeholders[1].text = "Chart generation failed, likely due to a missing 'kaleido' dependency for static image export."
 
     else:
         # Placeholder slide if topic modeling is not available
@@ -495,7 +495,7 @@ def generate_html_report(df, text_input, elapsed_time, df_topic_data):
         path=[px.Constant("All Entities"), 'category', 'label', 'text'],
         values='score',
         color='category',
-        
+        title="Entity Distribution by Category and Label",
         color_discrete_sequence=px.colors.sequential.Greys # Monochrome palette
     )
     fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25))
@@ -529,7 +529,7 @@ def generate_html_report(df, text_input, elapsed_time, df_topic_data):
     network_html = network_fig.to_html(full_html=False, include_plotlyjs='cdn')
 
     # 1f. Topic Charts HTML
-    topic_charts_html = '<h3>Bubble size = word weight</h3>'
+    topic_charts_html = '<h3>Topic Word Weights (Bubble Chart)</h3>'
     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:
@@ -588,13 +588,13 @@ def generate_html_report(df, text_input, elapsed_time, df_topic_data):
         <h2>3. Data Visualizations</h2>
         <h3>3.1 Entity Distribution Treemap</h3>
         <div class="chart-box">{treemap_html}</div>
-        <h3>3.2 Comparative Charts</h3>
+        <h3>3.2 Comparative Charts (Pie, Category Count, Frequency) - *Stacked Vertically*</h3>
         <div class="chart-box">{pie_html}</div>
         <div class="chart-box">{bar_category_html}</div>
         <div class="chart-box">{bar_freq_html}</div>
-        <h3>3.3 Entity Relationship Map</h3>
+        <h3>3.3 Entity Co-occurrence Network (Edges = Same Sentence)</h3>
         <div class="chart-box">{network_html}</div>
-        <h2>4. Topic Modelling</h2>
+        <h2>4. Topic Modeling (LDA on Entities)</h2>
         {topic_charts_html}
     </div></body></html>
     """
@@ -644,7 +644,7 @@ st.subheader("NER and Topic Analysis Report Generator", divider="gray") # Divide
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="secondary")
 expander = st.expander("**Important notes**")
 expander.write(f"""**Named Entities:** This app predicts fifteen (15) labels: {', '.join(entity_color_map.keys())}.
-**Dependencies:** Note that **PPTX** and **image export** require the Python libraries `python-pptx`, `plotly`, and `kaleido`. If charts in the PPTX are blank, please check your environment's $\text{kaleido}$ installation/permissions.
+**Dependencies:** Note that **PPTX** and **image export** require the Python libraries `python-pptx`, `plotly`, and **`kaleido`**. If charts in the PPTX are blank, please check your environment's $\text{kaleido}$ installation/permissions.
 **Results:** Results are compiled into a single, comprehensive **HTML report**, a **PowerPoint (.pptx) file**, and a **CSV file** for easy download and sharing.
 **How to Use:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract entities and generate the report.""")
 st.markdown("For any errors or inquiries, please contact us at [info@nlpblogs.com](mailto:info@nlpblogs.com)")