Update appStore/vulnerability_analysis.py

appStore/vulnerability_analysis.py

@@ -74,4 +74,70 @@ def app():
                 st.session_state.key1 = df
 
 
+def vulnerability_display(): 
+    
+    # Assign dataframe a name
+    df_vul = st.session_state['key0']
+    st.write(df_vul)
+    
+    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 = df_vul['Vulnerability Label'].apply(lambda x: 'Other' not in x).sum()
+       
+        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:
+        
+        ### Bar 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
+        group_counts = {}
+
+        # Iterate through each sublist
+        for index, row in df_vul.iterrows():
+            
+            # Iterate through each group in the sublist
+            for sublist in row['Vulnerability Label']:
+                
+                # Update the count in the dictionary
+                group_counts[sublist] = group_counts.get(sublist, 0) + 1
+
+        # Create a new dataframe from group_counts
+        df_label_count = pd.DataFrame(list(group_counts.items()), columns=['Label', 'Count'])
+
+        # Merge the label counts with the df_label DataFrame
+        df_label_count = df_labels.merge(df_label_count, on='Label', how='left')
+        st.write("df_label_count")
+        
+    #     # 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'])