app.py

import gradio as gr
from transformers import pipeline

# Load the sentiment analysis model
sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")

# Function to analyze sentiment
def analyze_sentiment(text):
    if len(text.strip()) == 0:
        return "Please enter some text for sentiment analysis."

    result = sentiment_analyzer(text)[0]
    
    # Extract numerical rating from the label (e.g., "5 stars" → "5")
    sentiment_label = result['label'].split()[0]  # Extract only the number (1-5)
    confidence = round(result['score'] * 100, 2)  # Convert to percentage
    
    return f"⭐ Sentiment: {sentiment_label} Stars (Confidence: {confidence}%)"

# Create the Gradio interface
iface = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(lines=3, placeholder="Enter a sentence or paragraph...", label="Input Text"),
    outputs=gr.Textbox(label="Sentiment Analysis Result"),
    title="Sentiment Analysis with BERT",
    description="Enter a sentence or paragraph to analyze its sentiment using a pre-trained BERT model.",
    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."]
    ],
    allow_flagging="never"
)

# Launch the app
iface.launch()