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#Import streamlit and pipeline
import streamlit as st
from transformers import pipeline
pipe_sent = pipeline("text-classification", model="ProsusAI/finbert")
pipe_tran = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
# Streamlit application title
st.title("FinSentinel-Portuguese Financial Sentiment Analysis")
st.write("Classification for 3 attitudes: positive, netural, negative")
# 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"):
# Perform text classification on the input text
translated_text = pipe_tran(text)[0]['translation_text']
results = pipe_sent(translated_text)[0]
# Display the classification result
max_score = float('-inf')
max_label = ''
max_score = round(results['score'], 4)
max_label = results['label']
st.write("Text:", text)
st.write("Label:", max_label)
st.write("Score:", max_score) |