app.py

import streamlit as st
from transformers import pipeline

# Load the text classification model pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)

# Streamlit application title
st.title("Text Classification")
st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")

# Text input for user to enter the text to classify
text = st.text_area("Enter the text to classify", "")

# Perform text classification when the user clicks the "Classify" button
if st.button("Classify"):
    if text.strip() == "":
        st.warning("Please enter some text to classify.")
    else:
        # Perform text classification on the input text
        results = classifier(text)[0]
        
        # Display the classification result
        max_score = float('-inf')
        max_label = None
        
        for result in results:
            if result['score'] > max_score:
                max_score = result['score']
                max_label = result['label']
                
        st.write("Text:", text)
        st.write("Label:", max_label)
        st.write("Score:", max_score)