app.py

import streamlit as st
from transformers import pipeline


# Load the text classification model pipeline
classifier = pipeline("text-classification", model='lguoao123/model2', return_all_scores=True)
translate = pipe = pipeline("text2text-generation", model="jieshenai/zh_en_translation")

# Streamlit application title
st.title("Financial News Sentiment Classification")
st.write("Classification")

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

# Perform text classification when the user clicks the "Classify" button
if st.button("Classify"):
    translate_text = translate(text)[0]['generated_text']
    # Perform text classification on the input text
    results = classifier(translate_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:", translate_text)
    st.write("Label:", max_label)
    st.write("Score:", max_score)