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Update appStore/vulnerability_analysis.py
Browse files
appStore/vulnerability_analysis.py
CHANGED
@@ -74,4 +74,70 @@ def app():
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st.session_state.key1 = df
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st.session_state.key1 = df
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def vulnerability_display():
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# Assign dataframe a name
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df_vul = st.session_state['key0']
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st.write(df_vul)
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col1, col2 = st.columns([1,1])
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with col1:
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# Header
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st.subheader("Explore references to vulnerable groups:")
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# Text
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num_paragraphs = len(df_vul['Vulnerability Label'])
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num_references = df_vul['Vulnerability Label'].apply(lambda x: 'Other' not in x).sum()
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st.markdown(f"""<div style="text-align: justify;"> The document contains a
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total of <span style="color: red;">{num_paragraphs}</span> paragraphs.
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We identified <span style="color: red;">{num_references}</span>
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references to vulnerable groups.</div>
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<br>
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In the pie chart on the right you can see the distribution of the different
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groups defined. For a more detailed view in the text, see the paragraphs and
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their respective labels in the table below.</div>""", unsafe_allow_html=True)
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with col2:
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### Bar chart
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# # Create a df that stores all the labels
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df_labels = pd.DataFrame(list(label_dict.items()), columns=['Label ID', 'Label'])
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# Count how often each label appears in the "Vulnerability Labels" column
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group_counts = {}
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# Iterate through each sublist
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for index, row in df_vul.iterrows():
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# Iterate through each group in the sublist
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for sublist in row['Vulnerability Label']:
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# Update the count in the dictionary
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group_counts[sublist] = group_counts.get(sublist, 0) + 1
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# Create a new dataframe from group_counts
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df_label_count = pd.DataFrame(list(group_counts.items()), columns=['Label', 'Count'])
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# Merge the label counts with the df_label DataFrame
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df_label_count = df_labels.merge(df_label_count, on='Label', how='left')
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st.write("df_label_count")
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# # Configure graph
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# fig = px.pie(df_labels,
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# names="Label",
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# values="Count",
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# title='Label Counts',
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# hover_name="Count",
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# color_discrete_sequence=px.colors.qualitative.Plotly
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# )
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# #Show plot
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# st.plotly_chart(fig, use_container_width=True)
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# ### Table
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st.table(df_vul[df_vul['Vulnerability Label'] != 'Other'])
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