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") @st.cache_resource # 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() import appStore.vulnerability_analysis as vulnerability_analysis import appStore.target as target_extraction 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("

Vulnerability Analysis 2.0

", 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 groups in vulnerable situations from public documents. \ We use Natural Language Processing (NLP), specifically deep \ learning-based text representations to search context-sensitively \ for mentions of the special needs of groups in vulnerable situations to cluster them thematically. """) #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 or multiple references to vulnerable groups. """) st.write("") # Define the apps used apps = [processing.app, vulnerability_analysis.app, target_extraction.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', 'Concrete targets/actions/measures'])) #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"""
The document contains a total of {num_paragraphs} paragraphs. We identified {num_references} references to vulnerable groups.

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.""", 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)