app.py

import gradio as gr
from transformers import pipeline
import torch

# Detect GPU availability and set the device
device = 0 if torch.cuda.is_available() else -1
print(f"Using device: {'GPU' if torch.cuda.is_available() else 'CPU'}")

# Load the sentiment analysis pipeline from Hugging Face
model = pipeline("sentiment-analysis", 
                 model="nlptown/bert-base-multilingual-uncased-sentiment",
                 device=device)

# Function to get sentiment prediction
def analyze_sentiment(text):
    result = model(text)

    # Extracting the star rating (1 to 5)
    sentiment = result[0]['label']
    sentiment_score = int(sentiment.split(' ')[-1])  # Extract star rating
    return f"Sentiment: {sentiment_score} Stars"

# Predefined examples for testing
examples = [
    ["I love this product! It's amazing!"],
    ["This was the worst experience I've ever had."],
    ["The movie was okay, not great but not bad either."],
    ["Absolutely fantastic! I would recommend it to everyone."]
]

# Create Gradio interface
interface = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(label="Enter Text", 
                      placeholder="Type a sentence here...", 
                      lines=2),
    outputs=gr.Textbox(label="Sentiment", 
                       placeholder="Predicted sentiment will be displayed here..."),
    examples=examples,
    title="Sentiment Analysis with BERT",
    description="This app performs sentiment analysis on the text you provide, displaying a sentiment score ranging from 1 to 5 stars."
)

# Launch the app
interface.launch()