Update app.py

app.py

@@ -138,67 +138,71 @@ if st.button("Analyze Document"):
 
 # If there is data stored
 if 'key0' in st.session_state:
-    with st.sidebar:
-        topic = st.radio(
-                        "Which category you want to explore?",
-                        (['Vulnerability', 'Concrete targets/actions']))
-    
-    if topic == 'Vulnerability':
-
-        # Assign dataframe a name
-        df_vul = st.session_state['key0']
-
-        col1, col2 = st.columns([1,1])
-
-        with col1:
-            # Header
-            st.subheader("Explore 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 contains a
-                    total of <span style="color: red;">{num_paragraphs}</span> paragraphs.
-                    We identified <span style="color: red;">{num_references}</span>
-                    references to vulnerable groups.</div>
-                    <br>
-                    In the pie chart on the right you can see the distribution of the different 
-                    groups defined. For a more detailed view in the text, see the paragraphs and 
-                    their respective labels in the table below.</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)
+
+    ###################################################################
+       
+    #with st.sidebar:
+     #  topic = st.radio(
+      #                 "Which category you want to explore?",
+       #                (['Vulnerability', 'Concrete targets/actions/measures']))
     
-        ### Table 
-        st.table(df_vul[df_vul['Vulnerability Label'] != 'Other'])
-
-       # vulnerability_analysis.vulnerability_display()
-    # elif topic == 'Action':
-    #     policyaction.action_display()
-    # else: 
-    #     policyaction.policy_display()
-    #st.write(st.session_state.key0)
+    #if topic == 'Vulnerability':
+
+    # Assign dataframe a name
+    df_vul = st.session_state['key0']
+
+    col1, col2 = st.columns([1,1])
+
+    with col1:
+        
+        # Header
+        st.subheader("Explore 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 contains a
+                total of <span style="color: red;">{num_paragraphs}</span> paragraphs.
+                We identified <span style="color: red;">{num_references}</span>
+                references to vulnerable groups.</div>
+                <br>
+                In the pie chart on the right you can see the distribution of the different 
+                groups defined. For a more detailed view in the text, see the paragraphs and 
+                their respective labels in the table below.</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'])
+
+   # vulnerability_analysis.vulnerability_display()
+# elif topic == 'Action':
+#     policyaction.action_display()
+# else: 
+#     policyaction.policy_display()
+#st.write(st.session_state.key0)