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AIEcosystem commited on Nov 6, 2025

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2783c81

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1 Parent(s): 0c28caf

Update src/streamlit_app.py

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src/streamlit_app.py +22 -20

src/streamlit_app.py CHANGED Viewed

@@ -263,7 +263,8 @@ def generate_entity_csv(df):
 # --- HTML REPORT GENERATION FUNCTION ---
 def generate_html_report(df, text_input, elapsed_time, df_topic_data, entity_color_map, report_title="Entity and Topic Analysis Report", branding_html=""):
     """
-    Generates a full HTML report containing all analysis results and visualizations.
     """
     # 1. Generate Visualizations (Plotly HTML)
@@ -324,10 +325,17 @@ def generate_html_report(df, text_input, elapsed_time, df_topic_data, entity_col
     highlighted_text_html = highlight_entities(text_input, df, entity_color_map).replace("div style", "div class='highlighted-text' style")
     # 3. Entity Tables (Pandas to HTML)
-    entity_table_html = df[['text', 'label', 'score', 'start', 'end', 'category']].to_html(
-        classes='table table-striped',
-        index=False
     )
     # 4. Construct the Final HTML
     html_content = f"""<!DOCTYPE html><html lang="en"><head>
@@ -344,8 +352,9 @@ def generate_html_report(df, text_input, elapsed_time, df_topic_data, entity_col
             .metadata {{ background-color: #e6f0ff; padding: 15px; border-radius: 8px; margin-bottom: 20px; font-size: 0.9em; }}
             .chart-box {{ background-color: #f9f9f9; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); min-width: 0; margin-bottom: 20px; }}
             table {{ width: 100%; border-collapse: collapse; margin-top: 15px; }}
-            table th, table td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
-            table th {{ background-color: #f0f0f0; }}
             .highlighted-text {{ border: 1px solid #888888; padding: 15px; border-radius: 5px; background-color: #ffffff; font-family: monospace; white-space: pre-wrap; margin-bottom: 20px; }}
         </style>
     </head>
@@ -518,7 +527,7 @@ DEFAULT_TEXT = (
     "space capabilities within the **European Union**. The core team, including lead engineer Marcus Davies, will hold "
     "their first collaborative workshop in Berlin, Germany, on August 15th. The community response on social "
     "media platform X (under the username @TechCEO) was overwhelmingly positive, with many major tech "
-    "publications, including Wired Magazine, predicting a major impact on the space technology industry by the "
     "end of the year, further strengthening the technological standing of the **European Union**. The platform is designed to be compatible with both Windows and Linux operating systems. "
     "The initial funding, secured via a Series B round, totaled $50 million. Financial analysts from Morgan Stanley "
     "are closely monitoring the impact on TechSolutions Inc.'s Q3 financial reports, expected to be released to the "
@@ -565,7 +574,9 @@ custom_labels_text = st.text_area(
     "**Optional:** Enter your own comma-separated entity labels here (e.g., `product, symptom, client_id`). Leave blank for default labels.",
     height=60,
     key='custom_labels_input',
-    placeholder="e.g., product, symptom, client_id"
 )
 col_results, col_clear = st.columns([1, 1])
@@ -681,14 +692,7 @@ if st.session_state.show_results:
         st.markdown("### 2. Detailed Entity Analysis")
         tab_category_details, tab_treemap_viz = st.tabs(["📑 Entities Grouped by Category", "🗺️ Treemap Distribution"])
-        if st.session_state.is_custom_mode:
-            unique_categories = ["User Defined Entities"]
-            tabs_to_show = df['label'].unique().tolist()
-            st.markdown(f"**Custom Labels Detected: {', '.join(tabs_to_show)}**")
-        else:
-            unique_categories = list(FIXED_CATEGORY_MAPPING.keys())
-        # --- Function to Apply Conditional Coloring to Scores ---
         def color_score_gradient(df_input):
             """Applies a color gradient to the 'score' column using Pandas Styler."""
             return df_input.style.background_gradient(
@@ -712,6 +716,7 @@ if st.session_state.show_results:
                         st.markdown(f"##### {label.capitalize()} Entities ({len(df_label)} total)")
                         st.dataframe(styled_df_label, use_container_width=True)
             else:
                 tabs_category = st.tabs(unique_categories)
                 for category, tab in zip(unique_categories, tabs_category):
@@ -841,11 +846,9 @@ if st.session_state.show_results:
             df_topic_data = st.session_state.topic_results
-            # --- CRITICAL: This is the conditional block that must have correct indentation ---
             if df_topic_data is not None and not df_topic_data.empty:
                 st.plotly_chart(create_topic_word_bubbles(df_topic_data), use_container_width=True)
                 st.markdown("This chart visualizes the key words driving the identified topics, based on extracted entities.")
-            # END CRITICAL BLOCK
             else:
                 st.info("Topic Modeling requires at least two unique entities with a minimum frequency to perform statistical analysis.")
@@ -900,5 +903,4 @@ if st.session_state.show_results:
                 file_name="ner_topic_full_report.html",
                 mime="text/html",
                 use_container_width=True
-            )

 # --- HTML REPORT GENERATION FUNCTION ---
 def generate_html_report(df, text_input, elapsed_time, df_topic_data, entity_color_map, report_title="Entity and Topic Analysis Report", branding_html=""):
     """
+    Generates a full HTML report containing all analysis results and visualizations,
+    including color gradient styling for the score column in the main table.
     """
     # 1. Generate Visualizations (Plotly HTML)
     highlighted_text_html = highlight_entities(text_input, df, entity_color_map).replace("div style", "div class='highlighted-text' style")
     # 3. Entity Tables (Pandas to HTML)
+    # --- FIX: Apply color gradient styling to the DataFrame BEFORE converting to HTML ---
+    styled_df = df[['text', 'label', 'score', 'start', 'end', 'category']].style.background_gradient(
+        cmap='YlGnBu',
+        subset=['score']
+    ).format({'score': '{:.4f}'})
+    entity_table_html = styled_df.to_html(
+        classes='table table-striped',
+        index=False,
     )
+    # --- END FIX ---
     # 4. Construct the Final HTML
     html_content = f"""<!DOCTYPE html><html lang="en"><head>
             .metadata {{ background-color: #e6f0ff; padding: 15px; border-radius: 8px; margin-bottom: 20px; font-size: 0.9em; }}
             .chart-box {{ background-color: #f9f9f9; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); min-width: 0; margin-bottom: 20px; }}
             table {{ width: 100%; border-collapse: collapse; margin-top: 15px; }}
+            /* Target the cells generated by pandas styling */
+            table td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
+            table th {{ border: 1px solid #ddd; padding: 8px; text-align: left; background-color: #f0f0f0; }}
             .highlighted-text {{ border: 1px solid #888888; padding: 15px; border-radius: 5px; background-color: #ffffff; font-family: monospace; white-space: pre-wrap; margin-bottom: 20px; }}
         </style>
     </head>
     "space capabilities within the **European Union**. The core team, including lead engineer Marcus Davies, will hold "
     "their first collaborative workshop in Berlin, Germany, on August 15th. The community response on social "
     "media platform X (under the username @TechCEO) was overwhelmingly positive, with many major tech "
+    "publications, including Wired Magazine, predicting a major impact on the space technology industry by the "
     "end of the year, further strengthening the technological standing of the **European Union**. The platform is designed to be compatible with both Windows and Linux operating systems. "
     "The initial funding, secured via a Series B round, totaled $50 million. Financial analysts from Morgan Stanley "
     "are closely monitoring the impact on TechSolutions Inc.'s Q3 financial reports, expected to be released to the "
     "**Optional:** Enter your own comma-separated entity labels here (e.g., `product, symptom, client_id`). Leave blank for default labels.",
     height=60,
     key='custom_labels_input',
+    # --- UPDATED PLACEHOLDER ENTITIES ---
+    placeholder="e.g., product_name, medical_symptom, customer_id, location_code, internal_project"
+    # --- END UPDATE ---
 )
 col_results, col_clear = st.columns([1, 1])
         st.markdown("### 2. Detailed Entity Analysis")
         tab_category_details, tab_treemap_viz = st.tabs(["📑 Entities Grouped by Category", "🗺️ Treemap Distribution"])
+        # --- Function to Apply Conditional Coloring to Scores (For Streamlit UI only) ---
         def color_score_gradient(df_input):
             """Applies a color gradient to the 'score' column using Pandas Styler."""
             return df_input.style.background_gradient(
                         st.markdown(f"##### {label.capitalize()} Entities ({len(df_label)} total)")
                         st.dataframe(styled_df_label, use_container_width=True)
             else:
+                unique_categories = list(FIXED_CATEGORY_MAPPING.keys())
                 tabs_category = st.tabs(unique_categories)
                 for category, tab in zip(unique_categories, tabs_category):
             df_topic_data = st.session_state.topic_results
             if df_topic_data is not None and not df_topic_data.empty:
                 st.plotly_chart(create_topic_word_bubbles(df_topic_data), use_container_width=True)
                 st.markdown("This chart visualizes the key words driving the identified topics, based on extracted entities.")
             else:
                 st.info("Topic Modeling requires at least two unique entities with a minimum frequency to perform statistical analysis.")
                 file_name="ner_topic_full_report.html",
                 mime="text/html",
                 use_container_width=True
+            )