app.py

import gradio as gr
from transformers import pipeline

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

# Define the sentiment analysis function
def analyze_sentiment(text):
    # Perform sentiment analysis
    result = sentiment_analyzer(text)[0]
    # Extract label (e.g., "1 star", "2 stars", etc.) and return it
    return f"Predicted Sentiment: {result['label']}"

# Define input and output components with clear labels
input_text = gr.Textbox(lines=5, label="Enter Your Text", placeholder="Type a sentence or paragraph here...")
output_sentiment = gr.Textbox(label="Sentiment Result")

# Define example inputs
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 the Gradio interface
interface = gr.Interface(
    fn=analyze_sentiment,
    inputs=input_text,
    outputs=output_sentiment,
    title="Sentiment Analyzer",
    description="Enter text to analyze its sentiment (1 to 5 stars) using a BERT-based model.",
    examples=examples,
    theme="default"  # Ensures a clean, responsive design
)

# Launch the interface
interface.launch()