Update app.py

app.py

@@ -431,7 +431,7 @@ except ET.ParseError as e:
 # -------------------------------
 # Tabs for Different Views
 # -------------------------------
-tabs = st.tabs(["Raw XML", "DataFrame", "Diplomatic Edition", "Editor Edition", "Visualization"])
+tabs = st.tabs(["Raw XML", "DataFrame", "Diplomatic Edition", "Editor Edition", "Visualization", "Authority Connections"])
 
 # -------------------------------
 # Raw XML Tab
@@ -709,6 +709,162 @@ with tabs[4]:
             place_desc = places_dict.get(row['Origin_ID'], {}).get('Description', "No description available.")
             st.markdown(f"**Place Description**: {place_desc}")
 
+with tabs[5]:
+    st.subheader("Authority Connections")
+    
+    # Define Authority Types
+    authority_types = ["Material", "Place"]  # Extend this list if you include Titles in the future
+    
+    # Select Authority Type
+    selected_authority_type = st.selectbox("Select Authority Type", authority_types)
+    
+    # Based on selection, provide the corresponding options
+    if selected_authority_type == "Material":
+        # List all materials from materials_dict
+        material_names = [material['Name_EN'] for material in materials_dict.values()]
+        selected_material = st.selectbox("Select Material", sorted(material_names))
+        
+        # Find the material ID based on the selected name
+        material_id = None
+        for id_, material in materials_dict.items():
+            if material['Name_EN'] == selected_material:
+                material_id = id_
+                break
+        
+        if material_id:
+            # Filter inscriptions that reference this material
+            connected_inscriptions = df[df['Material_ID'] == material_id]
+            
+            st.markdown(f"### Inscriptions using **{selected_material}**")
+            st.write(f"**Total Inscriptions:** {len(connected_inscriptions)}")
+            
+            if not connected_inscriptions.empty:
+                # Display inscriptions in a table
+                st.dataframe(connected_inscriptions[['Number', 'Publisher', 'Origin', 'Language', 'Dating', 'Encoder']])
+                
+                # Optional: Visualization - Number of Inscriptions per Year (Dating)
+                st.markdown("#### Inscriptions Over Time")
+                # Assuming 'Dating' is in a format that can be processed (e.g., single year or range)
+                # For simplicity, we'll extract the starting year
+                def extract_start_year(dating):
+                    if isinstance(dating, str):
+                        parts = dating.split('to')
+                        try:
+                            return int(parts[0].strip())
+                        except:
+                            return None
+                    return None
+                
+                connected_inscriptions['Start_Year'] = connected_inscriptions['Dating'].apply(extract_start_year)
+                year_counts = connected_inscriptions['Start_Year'].dropna().astype(int).value_counts().sort_index()
+                
+                fig, ax = plt.subplots()
+                ax.bar(year_counts.index, year_counts.values, color='skyblue')
+                ax.set_xlabel('Year')
+                ax.set_ylabel('Number of Inscriptions')
+                ax.set_title(f'Number of Inscriptions Using {selected_material} Over Time')
+                st.pyplot(fig)
+                
+                # Optional: Network Graph
+                st.markdown("#### Network Graph of Inscriptions and Materials")
+                G = nx.Graph()
+
+                # Add nodes
+                G.add_node(selected_material, type='Material')
+                for _, row in connected_inscriptions.iterrows():
+                    inscription_node = f"Inscription {row['Number']}"
+                    G.add_node(inscription_node, type='Inscription')
+                    G.add_edge(selected_material, inscription_node)
+
+                # Define node colors based on type
+                color_map = []
+                for node in G:
+                    if G.nodes[node]['type'] == 'Material':
+                        color_map.append('lightblue')
+                    else:
+                        color_map.append('lightgreen')
+
+                # Draw the graph
+                plt.figure(figsize=(8, 6))
+                nx.draw(G, with_labels=True, node_color=color_map, node_size=1500, font_size=10, font_weight='bold')
+                st.pyplot(plt)
+                plt.close()
+                
+            else:
+                st.info("No inscriptions found for the selected material.")
+    
+    elif selected_authority_type == "Place":
+        # List all places from places_dict
+        place_names = [place['Name'] for place in places_dict.values()]
+        selected_place = st.selectbox("Select Place", sorted(place_names))
+        
+        # Find the place ID based on the selected name
+        place_id = None
+        for id_, place in places_dict.items():
+            if place['Name'] == selected_place:
+                place_id = id_
+                break
+        
+        if place_id:
+            # Filter inscriptions that originate from this place
+            connected_inscriptions = df[df['Origin_ID'] == place_id]
+            
+            st.markdown(f"### Inscriptions from **{selected_place}**")
+            st.write(f"**Total Inscriptions:** {len(connected_inscriptions)}")
+            
+            if not connected_inscriptions.empty:
+                # Display inscriptions in a table
+                st.dataframe(connected_inscriptions[['Number', 'Publisher', 'Material', 'Language', 'Dating', 'Encoder']])
+                
+                # Optional: Visualization - Inscriptions Geographical Distribution
+                st.markdown("#### Geographical Distribution of Inscriptions")
+                map_df = connected_inscriptions[['Latitude', 'Longitude', 'Number']]
+                map_df = map_df.dropna(subset=['Latitude', 'Longitude'])
+                
+                if not map_df.empty:
+                    # Create a Folium map centered on the selected place
+                    selected_place_coords = [places_dict[place_id]['Latitude'], places_dict[place_id]['Longitude']]
+                    folium_map = folium.Map(location=selected_place_coords, zoom_start=8)
+                    
+                    for _, row in map_df.iterrows():
+                        folium.Marker(
+                            location=[row['Latitude'], row['Longitude']],
+                            popup=f"Inscription {row['Number']}"
+                        ).add_to(folium_map)
+                    
+                    st_folium(folium_map, width=700, height=500)
+                else:
+                    st.info("No geographical data available for these inscriptions.")
+                
+                # Optional: Network Graph
+                st.markdown("#### Network Graph of Inscriptions and Places")
+                G = nx.Graph()
+
+                # Add nodes
+                G.add_node(selected_place, type='Place')
+                for _, row in connected_inscriptions.iterrows():
+                    inscription_node = f"Inscription {row['Number']}"
+                    G.add_node(inscription_node, type='Inscription')
+                    G.add_edge(selected_place, inscription_node)
+
+                # Define node colors based on type
+                color_map = []
+                for node in G:
+                    if G.nodes[node]['type'] == 'Place':
+                        color_map.append('salmon')
+                    else:
+                        color_map.append('lightgreen')
+
+                # Draw the graph
+                plt.figure(figsize=(8, 6))
+                nx.draw(G, with_labels=True, node_color=color_map, node_size=1500, font_size=10, font_weight='bold')
+                st.pyplot(plt)
+                plt.close()
+                
+            else:
+                st.info("No inscriptions found for the selected place.")
+
+
 # -------------------------------
 # Footer
 # -------------------------------