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app.py

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+import gradio as gr
+from transformers import pipeline
+
+clf = pipeline("text-classification", model="your-username/bert-base-uncased-yelp")
+
+def predict(text):
+    pred = clf(text)[0]
+    return f"Label: {pred['label']} (score={pred['score']:.2f})"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=4, placeholder="Paste a customer review here..."),
+    outputs="text",
+    title="Sentiment Analysis Demo",
+    description="Fine‑tuned BERT‑base model on Yelp Polarity dataset"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0
+transformers>=4.40
+torch>=2.0
+datasets
+evaluate
+accelerate