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app.py

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+
+import gradio as gr
+from transformers import pipeline
+
+# Load both models
+distilbert = pipeline(
+    "text-classification",
+    model="Nav772/distilbert-amazon-reviews-5star"
+)
+
+roberta = pipeline(
+    "text-classification",
+    model="Nav772/roberta-amazon-reviews-5star"
+)
+
+def compare_models(text):
+    if not text.strip():
+        return "Please enter a review.", "Please enter a review."
+    
+    # Get predictions from both models
+    distilbert_result = distilbert(text)[0]
+    roberta_result = roberta(text)[0]
+    
+    # Format outputs
+    distilbert_stars = "⭐" * int(distilbert_result["label"][0])
+    roberta_stars = "⭐" * int(roberta_result["label"][0])
+    
+    distilbert_output = f"{distilbert_stars}\n{distilbert_result['label']}\nConfidence: {distilbert_result['score']:.2%}"
+    roberta_output = f"{roberta_stars}\n{roberta_result['label']}\nConfidence: {roberta_result['score']:.2%}"
+    
+    return distilbert_output, roberta_output
+
+demo = gr.Interface(
+    fn=compare_models,
+    inputs=gr.Textbox(
+        label="Enter a product review",
+        placeholder="Type your review here...",
+        lines=4
+    ),
+    outputs=[
+        gr.Textbox(label="DistilBERT (67M params, faster)"),
+        gr.Textbox(label="RoBERTa (125M params, more accurate)")
+    ],
+    title="🔬 Model Comparison: DistilBERT vs RoBERTa",
+    description="Compare two transformer models on the same review. Both were fine-tuned on Amazon product reviews for 5-star rating prediction.",
+    examples=[
+        ["This product exceeded all my expectations! Incredible quality and fast shipping."],
+        ["Meh. It works I guess. Nothing special about it."],
+        ["DO NOT BUY. Arrived broken and customer service was unhelpful."],
+        ["Pretty good for the price. Some minor issues but overall satisfied."]
+    ],
+    theme="soft"
+)
+
+demo.launch()