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bdd8447 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | import streamlit as st
from transformers import pipeline
# Initialize the sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")
# Streamlit app
st.title("Sentiment Analysis with Hugging Face")
# Text input
user_input = st.text_area("Enter text for sentiment analysis:", "I love this product!")
if st.button("Analyze Sentiment"):
# Perform sentiment analysis
result = sentiment_pipeline(user_input)[0]
# Display results
st.write(f"Sentiment: {result['label']}")
st.write(f"Confidence: {result['score']:.4f}")
# Visualize the sentiment
if result['label'] == 'POSITIVE':
st.success(f"Positive Sentiment (Score: {result['score']:.4f})")
else:
st.error(f"Negative Sentiment (Score: {result['score']:.4f})")
# Add some information about the model
st.info("This app uses the default sentiment analysis model from Hugging Face Transformers.")
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