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import streamlit as st
from setfit import SetFitModel
# Load the model
model = SetFitModel.from_pretrained("peter2000/vulnerable-groups-setfit")
# Define the classes
group_dict = {
1: 'Women and girls',
2: 'Children and youth',
3: 'Landlocked countries',
4: 'Outdoor workers',
5: 'Riverine and flood-prone areas',
6: 'Small-scale farmers',
7: 'Men and boys',
8: 'Small island developing states (SIDS)',
9: 'Fisherfolk and fishing communities',
10: 'Children with disabilities',
11: 'Low-income households',
12: 'Rural communities',
13: 'Pregnant women and new mothers',
14: 'Young adults',
15: 'Urban slums',
16: 'Gender non-conforming individuals',
17: 'Remote communities',
18: 'Older adults and the elderly',
19: 'Elderly population',
20: 'Mountain communities',
21: 'People with disabilities',
22: 'Indigenous peoples',
23: 'Informal settlements and slums',
24: 'Coastal communities',
25: 'Informal sector workers',
26: 'Drought-prone regions',
27: 'People with pre-existing health conditions',
28: 'Small-scale farmers and subsistence agriculture',
29: 'Migrants and displaced populations',
30: 'no vulnerable group mentioned'}
def predict(text):
preds = model([text])[0].item()
return group_dict[preds]
text = st.text_area('enter your text here')
x = st.slider('Select a value')
st.write(x, 'squared is', x * x) |