app.py

import gradio as gr
from transformers import pipeline

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

# Define a function for sentiment analysis
def analyze_sentiment(text):
    result = sentiment_pipeline(text)
    label = result[0]['label']  # Get the label, e.g., "1 star", "5 stars"
    score = int(label.split()[0])  # Extract the numerical part of the label (e.g., 1, 2, ..., 5)
    
    # Map the score from 1-5 to -1 to 1:
    # 1 star -> -1.0, 2 stars -> -0.5, 3 stars -> 0.0, 4 stars -> 0.5, 5 stars -> 1.0
    sentiment_score = (score - 3) / 2
    
    return sentiment_score

# Create a list of example sentences for users to select from
examples = [
    ["I love using Hugging Face Transformers!"],
    ["The product is terrible and I will never buy it again."],
    ["This is the best experience I've ever had!"],
    ["The movie was okay, not great but not bad either."],
    ["I am extremely disappointed with the service."]
]

# Create a Gradio interface
interface = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(label="Enter text for sentiment analysis", placeholder="Type your text here..."),
    outputs=gr.Number(label="Sentiment Score (-1 to 1)"),
    examples=examples,
    title="Sentiment Analysis with Hugging Face Transformers",
    description="""
        Enter a sentence to analyze its sentiment. The model will output a sentiment score between -1 and 1:
        - **-1**: Extremely Negative  
        - **0**: Neutral  
        - **+1**: Extremely Positive  
        This uses the `nlptown/bert-base-multilingual-uncased-sentiment` model.
    """
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()