app.py

import streamlit as st
from transformers import pipeline

# Load the text classification model pipeline
classifier = pipeline("text-classification",model='wxrrrrrrr/finetunde_sentiment_analysis', return_all_scores=True)

# Streamlit application title
st.title("Text Sentiment")
st.write("Analysis for 2 emotions: love, anger")

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

# Perform text classification when the user clicks the "Classify" button
if st.button("Analysis"):
    # Perform text classification on the input text
    results = classifier(text)[0]

    # Display the classification result
    max_score = float('-inf')
    max_label = ''

    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)