app.py

import streamlit as st
from transformers import pipeline

# Load the zero-shot classification model
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Define Streamlit app
def main():
    # Set page title and favicon
    st.set_page_config(page_title="Zero-Shot Text Classification", page_icon=":rocket:")

    # App title and description
    st.title("Zero-Shot Text Classification")
    st.markdown("""
    This app performs zero-shot text classification using the Facebook BART-Large-MNLI model.
    Enter a sentence and candidate labels, and the model will predict the most relevant label.
    """)

    # Input text box for the sentence to classify
    sequence_to_classify = st.text_input("Enter the sentence to classify:")

    # Candidate labels input with help text
    st.text("Enter candidate labels separated by commas (e.g., travel, cooking, dancing):")
    candidate_labels = st.text_input("Candidate Labels:")

    # Confidence threshold slider
    confidence_threshold = st.slider("Confidence Threshold:", min_value=0.0, max_value=1.0, value=0.5, step=0.01)

    # Classification button
    if st.button("Classify"):
        if sequence_to_classify and candidate_labels:
            # Split candidate labels into a list
            candidate_labels = [label.strip() for label in candidate_labels.split(",")]

            # Perform classification
            classification_result = classifier(sequence_to_classify, candidate_labels)

            # Display classification results
            st.subheader("Classification Results:")
            for label, score in zip(classification_result["labels"], classification_result["scores"]):
                if score >= confidence_threshold:
                    st.write(f"- {label}: {score}")
                else:
                    st.write(f"- {label}: Below threshold ({score})")

if __name__ == "__main__":
    main()