app.py

import gradio as gr
from transformers import pipeline

# Load the Sentiment Analysis pipeline...
classifier = pipeline(
    "sentiment-analysis",
    model="distilbert-base-uncased-finetuned-sst-2-english"
)

# Define the prediction function...
def sentiment_predictor(text):
    if not text:
        return "Please enter some text.", 0.0
    
    result = classifier(text)[0]
    label = result['label']
    score = result['score']

    output_text = f"Predicted Sentiment: **{label}**"
    
    return output_text, score

# Create the Gradio Interface
iface = gr.Interface(
    fn=sentiment_predictor,
    inputs=gr.Textbox(lines=5, placeholder="Type a sentence here...", label="Enter Text for Sentiment Analysis"),
    outputs=[
        gr.Markdown(label="Analysis Result"),
        gr.Number(label="Confidence Score")
    ],
    title="🤗 Simple Sentiment Analyzer on Hugging Face Spaces",
    description="A demonstration of deploying a DistilBERT-based model for sentiment classification using Gradio and Hugging Face Spaces. Type in any sentence and see the prediction!",
    # The allow_flagging argument is now obsolete and removed.
)

# Launch the interface
iface.launch()