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")