Update src/streamlit_app.py

src/streamlit_app.py

@@ -688,36 +688,90 @@ if st.session_state.show_results:
         else:
             unique_categories = list(FIXED_CATEGORY_MAPPING.keys())
         # --- Section 2a: Detailed Tables by Category/Label ---
-        with tab_category_details:
-            st.markdown("#### Detailed Entities Table (Grouped by Category)")
-            if st.session_state.is_custom_mode:
-                # In custom mode, group by the actual label since the category is just "User Defined Entities"
-                tabs_list = df['label'].unique().tolist()
-                tabs_category = st.tabs(tabs_list)
-                for label, tab in zip(tabs_list, tabs_category):
-                    df_label = df[df['label'] == label][['text', 'label', 'score', 'start', 'end']].sort_values(by='score', ascending=False)
-                    with tab:
-                        st.markdown(f"##### {label.capitalize()} Entities ({len(df_label)} total)")
-                        st.dataframe(
-                            df_label,
-                            use_container_width=True,
-                            column_config={'score': st.column_config.NumberColumn(format="%.4f")}
-                        )
-            else:
-                # In fixed mode, group by the category defined in FIXED_CATEGORY_MAPPING
-                tabs_category = st.tabs(unique_categories)
-                for category, tab in zip(unique_categories, tabs_category):
-                    df_category = df[df['category'] == category][['text', 'label', 'score', 'start', 'end']].sort_values(by='score', ascending=False)
-                    with tab:
-                        st.markdown(f"##### {category} Entities ({len(df_category)} total)")
-                        if not df_category.empty:
-                            st.dataframe(
-                                df_category,
-                                use_container_width=True,
-                                column_config={'score': st.column_config.NumberColumn(format="%.4f")}
-                            )
-                        else:
-                            st.info(f"No entities of category **{category}** were found in the text.")
+
+        
+
+
+# --- Function to Apply Conditional Coloring to Scores ---
+def color_score_gradient(df):
+    """
+    Applies a color gradient to the 'score' column using Pandas Styler.
+    High scores (closer to 1.0) will be darker/more saturated.
+    """
+    # Use 'YlGnBu' (Yellow-Green-Blue) gradient.
+    # We apply the gradient only to the 'score' column subset.
+    return df.style.background_gradient(
+        cmap='YlGnBu',
+        subset=['score']
+    ).format(
+        {'score': '{:.4f}'} # Re-apply the four decimal place format
+    )
+
+# --- Your Main Tab Detail Logic ---
+
+# Note: This code assumes 'df', 'st.session_state.is_custom_mode', and 'unique_categories'
+# are already defined earlier in your Streamlit application.
+
+tab_category_details:
+    st.markdown("#### Detailed Entities Table (Grouped by Category)")
+    
+    if st.session_state.is_custom_mode:
+        # In custom mode, group by the actual label since the category is just "User Defined Entities"
+        tabs_list = df['label'].unique().tolist()
+        tabs_category = st.tabs(tabs_list)
+        
+        for label, tab in zip(tabs_list, tabs_category):
+            # Prepare the DataFrame for the current label
+            df_label = df[df['label'] == label][['text', 'label', 'score', 'start', 'end']].sort_values(by='score', ascending=False)
+            
+            # Apply the coloring function
+            styled_df_label = color_score_gradient(df_label)
+
+            with tab:
+                st.markdown(f"##### {label.capitalize()} Entities ({len(df_label)} total)")
+                st.dataframe(
+                    # Pass the STYLED DataFrame object to Streamlit
+                    styled_df_label,
+                    use_container_width=True,
+                    # NOTE: st.column_config for 'score' is removed because Pandas Styler handles formatting and coloring
+                )
+    else:
+        # In fixed mode, group by the category defined in FIXED_CATEGORY_MAPPING
+        tabs_category = st.tabs(unique_categories)
+        
+        for category, tab in zip(unique_categories, tabs_category):
+            # Prepare the DataFrame for the current category
+            df_category = df[df['category'] == category][['text', 'label', 'score', 'start', 'end']].sort_values(by='score', ascending=False)
+            
+            # Apply the coloring function
+            styled_df_category = color_score_gradient(df_category)
+
+            with tab:
+                st.markdown(f"##### {category} Entities ({len(df_category)} total)")
+                if not df_category.empty:
+                    st.dataframe(
+                        # Pass the STYLED DataFrame object to Streamlit
+                        styled_df_category,
+                        use_container_width=True,
+                        # NOTE: st.column_config for 'score' is removed
+                    )
+                else:
+                    st.info(f"No entities of category **{category}** were found in the text.")
+            
+
+
+
+
+
+
+
+
+
+        
+
+
+
+                            
             # --- INSERTED GLOSSARY HERE ---
             with st.expander("See Glossary of tags"):
                 st.write('''- **text**: ['entity extracted from your text data']- **label**: ['label (tag) assigned to a given extracted entity (custom or fixed)']- **category**: ['the grouping category (e.g., "Locations" or "User Defined Entities")']- **score**: ['accuracy score; how accurately a tag has been assigned to a given entity']- **start**: ['index of the start of the corresponding entity']- **end**: ['index of the end of the corresponding entity']''')