Update app.py

app.py

@@ -105,44 +105,48 @@ if 'key0' in st.session_state:
 
         # Assign dataframe a name
         df_vul = st.session_state['key0']
-        
-        # Header
-        st.subheader("Explore the references to vulnerable groups:")
 
-        # Text 
-        num_paragraphs = len(df_vul['Vulnerability Label'])
-        num_references = len(df_vul[df_vul['Vulnerability Label'] != 'Other'])
-        
-        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>
-                paragraphs that contain a reference to a vulnerable group.
-                </div>""", unsafe_allow_html=True)
-    
-           
-        ### Pie chart
-                    
-        # Create a df that stores all the labels
-        df_labels = pd.DataFrame(list(label_dict.items()), columns=['Label ID', 'Label'])
+        col1, col2 = st.columns([1,1])
 
-        # Count how often each label appears in the "Vulnerability Labels" column
-        label_counts = df_vul['Vulnerability Label'].value_counts().reset_index()
-        label_counts.columns = ['Label', 'Count']
+        with col1:
+            # Header
+            st.subheader("Explore references to vulnerable groups:")
 
-        # 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)
+            # Text 
+            num_paragraphs = len(df_vul['Vulnerability Label'])
+            num_references = len(df_vul[df_vul['Vulnerability Label'] != 'Other'])
+            
+            st.markdown(f"""<div style="text-align: justify;"> The document contains a
+                    total of <span style="color: red;">{num_paragraphs}</span> paragraphs.
+                    We found <span style="color: red;">{num_references}</span>
+                    references to vulnerable group. The chart on the right shows the distribution
+                    of those references. In the table below, you can find the text that has been 
+                    identified.</div>""", unsafe_allow_html=True)
+    
+        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 = df_vul['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(df_vul[df_vul['Vulnerability Label'] != 'Other'])