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Update appStore/vulnerability_analysis.py
Browse files
appStore/vulnerability_analysis.py
CHANGED
@@ -97,13 +97,22 @@ def vulnerability_display():
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num_paragraphs = len(df['Vulnerability Label'])
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num_references = len(df_filtered['Group(s)'])
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st.markdown(f"""<div style="text-align: justify;">
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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 groups in vulnerable situations.</div>
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<br>
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their respective labels in the table below.</div>""", unsafe_allow_html=True)
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@@ -132,7 +141,7 @@ def vulnerability_display():
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# Exclude the "Other" group and all groups that do not have a label
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df_bar_chart = df_label_count[df_label_count['Label'] != 'Other']
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df_bar_chart =
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# Bar chart
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fig = go.Figure()
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num_paragraphs = len(df['Vulnerability Label'])
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num_references = len(df_filtered['Group(s)'])
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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 groups in vulnerable situations.</div>
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<br>
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<div style="text-align: justify;">We are searching for references related
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to the following groups: (1) Agricultural communities, (2) Children, (3)
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Ethnic, racial and other minorities, (4) Fishery communities, (5) Informal sector
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workers, (6) Members of indigenous and local communities, (7) Migrants and
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displaced persons, (8) Older persons, (9) Persons living in poverty, (10)
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Persons living with disabilities, (11) Persons with pre-existing health conditions,
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(12) Residents of drought-prone regions, (13) Rural populations, (14) Sexual
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minorities (LGBTQI+), (15) Urban populations, (16) Women and other genders.</div>
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<br>
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<div style="text-align: justify;">In the chart on the right you can see how often
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each group has been referenced. 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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# Exclude the "Other" group and all groups that do not have a label
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df_bar_chart = df_label_count[df_label_count['Label'] != 'Other']
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df_bar_chart = df_bar_chart[df_bar_chart['Count'] != 0]
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# Bar chart
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fig = go.Figure()
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