app.py

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'}

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

# App 
st.title("Identify references to vulnerable groups.")

st.write("""Vulnerable groups encompass various communities and individuals who are disproportionately affected by the impacts of climate change
due to their socioeconomic status, geographical location, or inherent characteristics. By incorporating the needs and perspectives of these groups 
into national climate policies, governments can ensure equitable outcomes, promote social justice, and strive to build resilience within the most marginalized populations, 
fostering a more sustainable and inclusive society as we navigate the challenges posed by climate change.This app allows you to identify whether a text contains any 
references to vulnerable groups, for example when talking about policy documents.""")

#col1, col2 = st.columns(2)

# Create text input box
input_text = st.text_area(label='Please enter your text here', height=350, value="Example")

st.write('Prediction:', run_sentiment_analysis(txt))

# Create the output box
#output=""
#st.text_area(label="Prediction:", height=350)

# Make predictions
#preds = model(input_text)

#modelresponse = model_function(input)
#st.text_area(label ="",value=preds, height =100)

# Select lab
#def get_label(prediction_tensor):
 #   print(prediction_tensor.index("1"))
    #key = prediction_tensor.index(1)
    #return group_dict[key]
    
#st.write(preds)
#st.text(get_label(preds))