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import streamlit as st
from transformers import pipeline
import time
# Set page configuration
st.set_page_config(page_title="Sentiment Analysis App", page_icon="😊")
@st.cache_resource
def load_sentiment_model():
"""Load the sentiment analysis model."""
return pipeline('sentiment-analysis')
def analyze_sentiment(text, model):
"""Analyze the sentiment of the given text."""
try:
return model(text)
except Exception as e:
st.error(f"An error occurred during sentiment analysis: {str(e)}")
return None
def main():
# Page header
st.title("📊 Sentiment Analysis Tool")
st.write("Enter your text below to analyze its sentiment.")
# Load the model
with st.spinner("Loading sentiment analysis model..."):
sentiment_model = load_sentiment_model()
# Text input
text = st.text_area("Enter some text:", height=150)
if st.button("Analyze Sentiment"):
if not text:
st.warning("Please enter some text to analyze.")
else:
with st.spinner("Analyzing sentiment..."):
result = analyze_sentiment(text, sentiment_model)
if result:
# Display results
sentiment = result[0]['label']
score = result[0]['score']
st.subheader("Analysis Result:")
col1, col2 = st.columns(2)
with col1:
st.metric("Sentiment", sentiment)
with col2:
st.metric("Confidence", f"{score:.2%}")
# Visualize the sentiment
if sentiment == "POSITIVE":
st.success("😃 The text expresses a positive sentiment.")
elif sentiment == "NEGATIVE":
st.error("😞 The text expresses a negative sentiment.")
else:
st.info("😐 The text expresses a neutral sentiment.")
# Display raw JSON output
with st.expander("See raw output"):
st.json(result)
# Add a footer
st.markdown("---")
st.markdown("Created with ❤️ by Ali Nasri")
if __name__ == "__main__":
main()
#pipe = pipeline('sentiment-analysis')
#text = st.text_area('Enter some text')
#if text:
# out = pipe(text)
# st.json(out) |