app.py

import streamlit as st
from setfit import SetFitModel

# Load the model
model = SetFitModel.from_pretrained("peter2000/vulnerable-groups-setfit")

# Define the classes
group_dict = {
    0: 'Coastal communities',
    1: 'Small island developing states (SIDS)',
    2: 'Landlocked countries',
    3: 'Low-income households',
    4: 'Informal settlements and slums',
    5: 'Rural communities',
    6: 'Children and youth',
    7: 'Older adults and the elderly',
    8: 'Women and girls',
    9: 'People with pre-existing health conditions',
    10: 'People with disabilities',
    11: 'Small-scale farmers and subsistence agriculture',
    12: 'Fisherfolk and fishing communities',
    13: 'Informal sector workers',
    14: 'Children with disabilities',
    15: 'Remote communities',
    16: 'Young adults',
    17: 'Elderly population',
    18: 'Urban slums',
    19: 'Men and boys',
    20: 'Gender non-conforming individuals',
    21: 'Pregnant women and new mothers',
    22: 'Mountain communities',
    23: 'Riverine and flood-prone areas',
    24: 'Drought-prone regions',
    25: 'Indigenous peoples',
    26: 'Migrants and displaced populations',
    27: 'Outdoor workers',
    28: 'Small-scale farmers',
    29: 'Other'}

def predict(text):
    preds = model([text])[0].item()
    return group_dict[preds]

text = st.text_area('enter your text here')

# App 
st.title("Identify references to vulnerable groups.")
st.write("This app allows you to identify whether a text contains any references to vulnerable groups. This can, for example, be used to analyse policy documents.")

col1, col2 = st.columns(2)

col1.text_area('enter your text here')
col2.text('f"{ group_dict['label'] }: { round(p['score'] * 100, 1)}%"')

st.write("Example: To promote gender diversity, ")