example.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and model from Hugging Face model hub
model_name = "dejanseo/Intent-XS"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()  # Set the model to evaluation mode

# Human-readable labels
label_map = {
    1: 'Commercial',
    2: 'Non-Commercial',
    3: 'Branded',
    4: 'Non-Branded',
    5: 'Informational',
    6: 'Navigational',
    7: 'Transactional',
    8: 'Commercial Investigation',
    9: 'Local',
    10: 'Entertainment'
}

# Function to perform inference
def get_predictions(text):
    inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
    probabilities = torch.sigmoid(logits).squeeze()
    predictions = (probabilities > 0.5).int()
    return probabilities.numpy(), predictions.numpy()

# Streamlit user interface
st.title('Multi-label Classification with Intent-XS')
query = st.text_input("Enter your query:")

if st.button('Submit'):
    if query:
        probabilities, predictions = get_predictions(query)
        result = {label_map[i+1]: f"Probability: {prob:.2f}" for i, prob in enumerate(probabilities) if predictions[i] == 1}
        if result:
            st.write("Predicted Categories:")
            for label, prob in result.items():
                st.write(f"{label}: {prob}")
        else:
            st.write("No relevant categories predicted.")
    else:
        st.write("Please enter a query to get predictions.")