Spaces:
Sleeping
Sleeping
File size: 1,516 Bytes
f25443b 7f19196 f25443b 7f19196 |
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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
import streamlit as st
from transformers import pipeline
print("Loading the model...")
# Title and Description
st.title("Sentiment Analysis Web App")
st.write("""
### Powered by Hugging Face and Streamlit
This app uses a pre-trained NLP model from Hugging Face to analyze the sentiment of the text you enter.
Try entering a sentence to see if it's positive, negative, or neutral!
""")
# Initialize Hugging Face Sentiment Analysis Pipeline
@st.cache_resource
def load_model():
print("before load model")
return pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
sentiment_analyzer = load_model()
# Input Text from User
user_input = st.text_area("Enter some text to analyze:", "Streamlit and Hugging Face make NLP fun!")
# Analyze Sentiment
if st.button("Analyze Sentiment"):
print("button click")
if user_input.strip():
result = sentiment_analyzer(user_input)[0]
sentiment = result['label']
score = result['score']
# Display the Result
st.subheader("Sentiment Analysis Result")
st.write(f"**Sentiment:** {sentiment}")
st.write(f"**Confidence Score:** {score:.2f}")
else:
st.warning("Please enter some text to analyze!")
# Sidebar with About Information
st.sidebar.title("About")
st.sidebar.info("""
This app demonstrates the use of Hugging Face's NLP models with Streamlit.
It uses the `distilbert-base-uncased-finetuned-sst-2-english` model for sentiment analysis.
""")
print("after")
|