app.py

import gradio as gr
from transformers import pipeline

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

# Function to analyze sentiment and convert it to star rating (1-5)
def analyze_sentiment(text):
    result = sentiment_analyzer(text)[0]
    sentiment_score = result['label']
    
    # Convert sentiment score to numeric star rating (1-5 stars)
    if sentiment_score == '1 star':
        return 1
    elif sentiment_score == '2 stars':
        return 2
    elif sentiment_score == '3 stars':
        return 3
    elif sentiment_score == '4 stars':
        return 4
    else:
        return 5

# Define example sentences for easy 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."]
]

# Build the Gradio interface
iface = gr.Interface(
    fn=analyze_sentiment,  # Function to call for sentiment analysis
    inputs=[
        gr.Textbox(label="Enter Text", placeholder="Type or paste a sentence or paragraph here...", lines=5),
        gr.Button("Analyze Sentiment")  # Button to trigger analysis
    ],
    outputs=gr.Textbox(label="Sentiment Rating (1 to 5 stars)"),  # Display sentiment rating
    live=False,  # Disable live preview while typing
    examples=examples,  # Predefined examples
    description="Sentiment analysis using BERT-based model for multilingual sentiment prediction."
)

# Launch the Gradio interface
iface.launch()