app.py

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("<h2 style='text-align: center; color: black;'> Vulnerability Analysis 2.0 </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. 
        """)
    
    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]

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"""<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)