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| import streamlit as st | |
| import os | |
| import pkg_resources | |
| # Using this wacky hack to get around the massively ridicolous managed env loading order | |
| def is_installed(package_name, version): | |
| try: | |
| pkg = pkg_resources.get_distribution(package_name) | |
| return pkg.version == version | |
| except pkg_resources.DistributionNotFound: | |
| return False | |
| # shifted from below - this must be the first streamlit call; otherwise: problems | |
| st.set_page_config(page_title = 'Vulnerability Analysis', | |
| initial_sidebar_state='expanded', layout="wide") | |
| # cache the function so it's not called every time app.py is triggered | |
| def install_packages(): | |
| install_commands = [] | |
| if not is_installed("spaces", "0.12.0"): | |
| install_commands.append("pip install spaces==0.17.0") | |
| if not is_installed("pydantic", "1.8.2"): | |
| install_commands.append("pip install pydantic==1.8.2") | |
| if not is_installed("typer", "0.4.0"): | |
| install_commands.append("pip install typer==0.4.0") | |
| if install_commands: | |
| os.system(" && ".join(install_commands)) | |
| # install packages if necessary | |
| install_packages() | |
| # everything else... | |
| #!!!! NOTE: shift st.set_page_config up script - see notes above | |
| import appStore.vulnerability_analysis as vulnerability_analysis | |
| import appStore.doc_processing as processing | |
| from utils.uploadAndExample import add_upload | |
| from utils.vulnerability_classifier import label_dict | |
| import pandas as pd | |
| import plotly.express as px | |
| #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': | |
| # Assign dataframe a name | |
| df_vul = st.session_state['key0'] | |
| 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 = len(df_vul[df_vul['Vulnerability Label'] != 'Other']) | |
| 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: | |
| ### Pie 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 | |
| label_counts = df_vul['Vulnerability Label'].value_counts().reset_index() | |
| label_counts.columns = ['Label', 'Count'] | |
| # Merge the label counts with the df_label DataFrame | |
| df_labels = df_labels.merge(label_counts, on='Label', how='left') | |
| # 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']) | |
| # vulnerability_analysis.vulnerability_display() | |
| # elif topic == 'Action': | |
| # policyaction.action_display() | |
| # else: | |
| # policyaction.policy_display() | |
| #st.write(st.session_state.key0) |