app.py

import streamlit as st
from transformers import pipeline

# Load the sentiment analysis pipeline
pipe = pipeline("text-classification", model="finiteautomata/bertweet-base-sentiment-analysis")

# Define a function to perform sentiment analysis
def analyze_sentiment(text):
    try:
        results = pipe(text)
        sentiment = results[0]['label']
        confidence = results[0]['score']
        return sentiment, confidence
    except Exception as e:
        return "ERROR", 0.0  # Handle errors gracefully

# Create a Streamlit app
st.title("Sentiment Analysis App")

# Get the user input
text = st.text_input("Enter text for sentiment analysis")

if text:
    # Perform sentiment analysis
    sentiment, confidence = analyze_sentiment(text)

    # Set a confidence threshold
    confidence_threshold = 0.5

    # Display the output based on the sentiment and confidence
    if confidence >= confidence_threshold:
        if sentiment == "POSITIVE":
            st.success(f"Sentiment: Positive, Confidence: {confidence:.2f}")
        elif sentiment == "NEGATIVE":
            st.error(f"Sentiment: Negative, Confidence: {confidence:.2f}")
        else:
            st.info(f"Sentiment: Neutral, Confidence: {confidence:.2f}")
    else:
        st.warning("Low confidence. Sentiment result may not be reliable.")