Update app.py

app.py

@@ -6,36 +6,36 @@ 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'}
+    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()
@@ -43,5 +43,13 @@ def predict(text):
 
 text = st.text_area('enter your text here')
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# 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, ")