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| import streamlit as st | |
| from utils.uploadAndExample import add_upload | |
| from utils.config import model_dict | |
| from utils.vulnerability_classifier import label_dict | |
| import appStore.doc_processing as processing | |
| import appStore.vulnerability_analysis as vulnerability_analysis | |
| import appStore.target as target_analysis | |
| 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) | |
| # Create a list of options for the dropdown | |
| model_options = ['Llama3.1-8B','Llama3.1-70B','Llama3.1-405B','Zephyr 7B β','Mistral-7B','Mixtral-8x7B'] | |
| # Dropdown selectbox: model | |
| model_sel = st.selectbox('Select a model:', model_options) | |
| model_sel_name = model_dict[model_sel] | |
| st.session_state['model_sel_name'] = model_sel_name | |
| with st.container(): | |
| st.markdown("<h2 style='text-align: center;'> Vulnerability Analysis 3.1 </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 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. | |
| For more understanding on Methodology [Click Here](https://vulnerability-analysis.streamlit.app/) | |
| """) | |
| 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_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: | |
| vulnerability_analysis.vulnerability_display() | |
| target_analysis.target_display(model_sel_name=model_sel_name) |