Update app.py

app.py

@@ -103,42 +103,48 @@ if 'key0' in st.session_state:
     
     if topic == 'Vulnerability':
 
+        # Assign dataframe easier name 
+        df_vul = st.session_state['key0']
+        
+        # Header
+        st.subheader("Explore here which references to vulnerable groups were identified:")
 
-        col1, col2 = st.columns([1,1])
-
-        with col1:
-            
-            ### Table 
-            st.table(st.session_state['key0'])
+        # Text 
+        num_paragraphs = len(df_vul['Vulnerability Label'])
+        num_references = df_vul[df_vul['Vulnerability Label'] != 'Other']
         
-        with col2: 
-            
-            ### Pie chart
-                        
-            # Create a df that stores all the labels
-            df_labels = pd.DataFrame(list(label_dict.items()), columns=['Label ID', 'Label'])
-    
-            # Count how often each label appears in the "Vulnerability Labels" column
-            label_counts = st.session_state['key0']['Vulnerability Label'].value_counts().reset_index()
-            label_counts.columns = ['Label', 'Count']
-    
-            # Merge the label counts with the df_label DataFrame
-            df_labels = df_labels.merge(label_counts, on='Label', how='left')
+        st.markdown(f"""<div style="text-align: justify;"> The document has a
+                total of <span style="color: red;">{num_paragraphs}</span> paragraphs.
+                In total, we found <span style="color: red;">{num_references}</span>
+                to vulnerable groups.</div>""", unsafe_allow_html=True)
     
-            # Configure graph
-            fig = px.pie(df_labels,
-                    names="Label", 
-                    values="Count",
-                    title='Label Counts',
-                    hover_name="Count",
-                    color_discrete_sequence=px.colors.qualitative.Plotly
-            )
-            
-            #Show plot
-            st.plotly_chart(fig, use_container_width=True)
+           
+        ### Pie chart
+                    
+        # Create a df that stores all the labels
+        df_labels = pd.DataFrame(list(label_dict.items()), columns=['Label ID', 'Label'])
+
+        # Count how often each label appears in the "Vulnerability Labels" column
+        label_counts = st.session_state['key0']['Vulnerability Label'].value_counts().reset_index()
+        label_counts.columns = ['Label', 'Count']
+
+        # Merge the label counts with the df_label DataFrame
+        df_labels = df_labels.merge(label_counts, on='Label', how='left')
+
+        # Configure graph
+        fig = px.pie(df_labels,
+                names="Label", 
+                values="Count",
+                title='Label Counts',
+                hover_name="Count",
+                color_discrete_sequence=px.colors.qualitative.Plotly
+        )
         
+        #Show plot
+        st.plotly_chart(fig, use_container_width=True)
+    
         ### Table 
-        #st.table(st.session_state['key0'])
+        st.table(st.session_state['key0'])
 
        # vulnerability_analysis.vulnerability_display()
     # elif topic == 'Action':