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import streamlit as st
from setfit import SetFitModel
# Load the model
model = SetFitModel.from_pretrained("leavoigt/vulnerable-groups")
# 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]
# 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)
with col1:
input_text = st.text_area('enter your text here')
preds = model(["input_text"])
with col2:
st.text(preds)
st.write("Example: To promote gender diversity, several new policies are being implemented") |