Initialize

app.py

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+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()

requirements.txt

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+gradio
+transformers
+torch