app.py

import gradio as gr
from transformers import pipeline

# Specify the model and revision explicitly
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
revision = "af0f99b"

# Load the sentiment analysis pipeline with the specified model and revision
sentiment_pipeline = pipeline("sentiment-analysis", model=model_name, revision=revision)

def predict_sentiment(text):
    """
    Predicts the sentiment of the input text.
    Returns the label (POSITIVE/NEGATIVE) and the confidence score.
    """
    result = sentiment_pipeline(text)[0]
    label = result['label']
    confidence = round(result['score'], 4)
    return f"Sentiment: {label}, Confidence: {confidence}"

# Create a Gradio interface
interface = gr.Interface(fn=predict_sentiment,
                         inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."),
                         outputs="text",
                         title="Simple Text Sentiment Analysis",
                         description="A simple text sentiment analysis tool using Hugging Face's transformers.")

# Launch the application
interface.launch()