app.py

import streamlit as st
from transformers import pipeline

st.title("Text Sentiment Analysis App")
st.write("Analyze whether a given text is positive or negative using a Hugging Face model.")

user_input = st.text_area("Enter your text:", placeholder="For example: I love this product!")

# Initialize the model
@st.cache_resource
def load_sentiment_model():
    return pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

# Load the model
model = load_sentiment_model()

# Perform analysis if input is provided
if st.button("Analyze"):
    if user_input.strip():
        with st.spinner("Analyzing sentiment..."):
            result = model(user_input)
        st.success("Analysis complete!")
        
        # Extracting the label and score
        label = result[0]['label']
        score = result[0]['score']

        # Display the result
        st.write(f"**Label:** {label}")
        st.write(f"**Confidence Score:** {score:.2f}")

    else:
        st.error("Please enter some text!")