Spaces:
Sleeping
Sleeping
File size: 6,506 Bytes
3936853 31b5a19 1e5a262 c48611a 614088e eab471f 31b5a19 74a942d 31b5a19 afff22e 31b5a19 57455f3 31b5a19 6f96de4 31b5a19 6f96de4 31b5a19 a12fa3b adac0c9 31b5a19 a12fa3b f57ce49 31b5a19 8541d11 31b5a19 a12fa3b 51f0454 c48611a ccc6bed c48611a 77cdf42 c48611a 77cdf42 c48611a 51f0454 c48611a 51f0454 a12fa3b 0919f9a 31b5a19 0919f9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
import appStore.vulnerability_analysis as vulnerability_analysis
import appStore.doc_processing as processing
from utils.uploadAndExample import add_upload
import streamlit as st
from utils.vulnerability_classifier import label_dict
import pandas as pd
st.set_page_config(page_title = 'Vulnerability Analysis',
initial_sidebar_state='expanded', layout="wide")
with st.sidebar:
# upload and example doc
choice = st.sidebar.radio(label = 'Select the Document',
help = 'You can upload the document \
or else you can try a example document',
options = ('Upload Document', 'Try Example'),
horizontal = True)
add_upload(choice)
with st.container():
st.markdown("<h2 style='text-align: center; color: black;'> Vulnerability Analysis </h2>", unsafe_allow_html=True)
st.write(' ')
with st.expander("ℹ️ - About this app", expanded=False):
st.write(
"""
The Vulnerability Analysis App is an open-source\
digital tool which aims to assist policy analysts and \
other users in extracting and filtering references \
to different vulnerable groups from public documents.
""")
# st.write('**Definitions**')
# st.caption("""
# - **Target**: Targets are an intention to achieve a specific result, \
# for example, to reduce GHG emissions to a specific level \
# (a GHG target) or increase energy efficiency or renewable \
# energy to a specific level (a non-GHG target), typically by \
# a certain date.
# - **Economy-wide Target**: Certain Target are applicable \
# not at specific Sector level but are applicable at economic \
# wide scale.
# - **Netzero**: Identifies if its Netzero Target or not.
# - 'NET-ZERO': target_labels = ['T_Netzero','T_Netzero_C']
# - 'Non Netzero Target': target_labels_neg = ['T_Economy_C',
# 'T_Economy_Unc','T_Adaptation_C','T_Adaptation_Unc','T_Transport_C',
# 'T_Transport_O_C','T_Transport_O_Unc','T_Transport_Unc']
# - 'Others': Other Targets beside covered above
# - **GHG Target**: GHG targets refer to contributions framed as targeted \
# outcomes in GHG terms.
# - 'GHG': target_labels_ghg_yes = ['T_Transport_Unc','T_Transport_C']
# - 'NON GHG TRANSPORT TARGET': target_labels_ghg_no = ['T_Adaptation_Unc',\
# 'T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C']
# - 'OTHERS': Other Targets beside covered above.
# - **Conditionality**: An “unconditional contribution” is what countries \
# could implement without any conditions and based on their own \
# resources and capabilities. A “conditional contribution” is one \
# that countries would undertake if international means of support \
# are provided, or other conditions are met.
# - **Action**: Actions are an intention to implement specific means of \
# achieving GHG reductions, usually in forms of concrete projects.
# - **Policies and Plans**: Policies are domestic planning documents \
# such as policies, regulations or guidlines, and Plans are broader \
# than specific policies or actions, such as a general intention \
# to ‘improve efficiency’, ‘develop renewable energy’, etc. \
# The terms come from the World Bank's NDC platform and WRI's publication.
# """)
#c1, c2, c3 = st.columns([12,1,10])
#with c1:
# image = Image.open('docStore/img/flow.jpg')
# st.image(image)
#with c3:
st.write("""
What Happens in background?
- Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
In this step the document is broken into smaller paragraphs \
(based on word/sentence count).
- Step 2: The paragraphs are then fed to the **Vulnerability Classifier** which detects if
the paragraph contains any references to vulnerable groups.
""")
st.write("")
# Define the apps used
apps = [processing.app, vulnerability_analysis.app]
multiplier_val =1/len(apps)
if st.button("Analyze Document"):
prg = st.progress(0.0)
for i,func in enumerate(apps):
func()
prg.progress((i+1)*multiplier_val)
# If there is data stored
if 'key0' in st.session_state:
with st.sidebar:
topic = st.radio(
"Which category you want to explore?",
(['Vulnerability']))
if topic == 'Vulnerability':
# Display charts
col1, col2 = st.columns([1,1])
# Pie chart
with col1:
st.write(type(st.session_state['key0']))
st.write(st.session_state['key0'])
# Create a df that stores how often the labels appear
df_count = pd.DataFrame(list(label_dict.items()), columns=['Label ID', 'Label'])
# # Count how often each label appears in the "Vulnerability Labels" column
# label_counts = st.session_state['key0']['Vulnerability Labels'].value_counts().reset_index()
# label_counts.columns = ['Label', 'Count']
# # Merge the label counts with the df_label DataFrame
# df_label = df_label.merge(label_counts, on='Label', how='left')
# st.write(df_label)
#bar_data = st.session_state['key0']
# fig = px.bar(st.session_state['key0'],
# x="Year",
# y="Value",
# color='Country',
# title='Chart 3 - Total Population',
# hover_name="Value",
# color_discrete_sequence=px.colors.qualitative.Plotly
# )
# Bar cart
# Display the table
st.table(st.session_state['key0'])
# vulnerability_analysis.vulnerability_display()
# elif topic == 'Action':
# policyaction.action_display()
# else:
# policyaction.policy_display()
#st.write(st.session_state.key0) |