Update app.py

app.py

@@ -1,7 +1,46 @@
 import streamlit as st
-from transformers import pipeline
+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]
 
-pipe = pipeline('sentiment-analysis')
 text = st.text_area('enter your text here')
 
 x = st.slider('Select a value')