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import streamlit as st | |
from transformers import pipeline | |
# Load the sentiment analysis pipeline | |
sentiment_pipeline = pipeline("sentiment-analysis") | |
# Streamlit UI | |
st.title("Sentiment Analysis") | |
st.write("Enter text below to analyse the sentiment.") | |
st.write("You can copy and paste a comment from social media for example.") | |
st.write("Usage idea: Companies can now quickly gather customers on mass who leave unhappy comments on social media to get in first before they leave a bad review.") | |
st.write("Making that crucial early contact to be proactive, rather than reactive, keeps your review ratings higher than your competitors.") | |
# Text input | |
user_input = st.text_area("Text input") | |
# Button to perform sentiment analysis | |
if st.button("Analyse Sentiment"): | |
if user_input: | |
# Perform sentiment analysis | |
results = sentiment_pipeline(user_input) | |
# Display the results | |
sentiment = results[0]['label'] | |
score = results[0]['score'] | |
confidence_percentage = score * 100 | |
st.write(f"Sentiment: {sentiment} (Confidence: {confidence_percentage:.2f}%)") | |
else: | |
st.write("Please enter some text to proceed.") | |
#Credit | |
st.write(" ") | |
st.write("Click [here](https://ai-solutions.ai) to visit AI Solutions.") | |