Update app.py
Browse files
app.py
CHANGED
@@ -34,7 +34,7 @@ def install_packages():
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install_packages()
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import appStore.vulnerability_analysis as vulnerability_analysis
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import appStore.target as
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import appStore.doc_processing as processing
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from utils.uploadAndExample import add_upload
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from utils.vulnerability_classifier import label_dict
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@@ -95,79 +95,82 @@ if st.button("Analyze Document"):
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# If there is data stored
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if 'key0' in st.session_state:
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#with st.sidebar:
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#if topic == 'Vulnerability':
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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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with col2:
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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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# vulnerability_analysis.vulnerability_display()
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# elif topic == 'Action':
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install_packages()
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import appStore.vulnerability_analysis as vulnerability_analysis
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import appStore.target as target_analysis
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import appStore.doc_processing as processing
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from utils.uploadAndExample import add_upload
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from utils.vulnerability_classifier import label_dict
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# If there is data stored
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if 'key0' in st.session_state:
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vulnerability_analysis.vulnerability_display()
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target_analysis.target_display()
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# ###################################################################
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# #with st.sidebar:
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# # topic = st.radio(
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# # "Which category you want to explore?",
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# # (['Vulnerability', 'Concrete targets/actions/measures']))
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# #if topic == 'Vulnerability':
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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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# vulnerability_analysis.vulnerability_display()
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# elif topic == 'Action':
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