Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load a pretrained fill-mask model
+unmasker = pipeline("fill-mask", model="bert-base-uncased")
+
+def fill_mask_fn(text, top_k):
+    mask_token = unmasker.tokenizer.mask_token
+    if mask_token not in text:
+        return f"Please include the mask token '{mask_token}' in your input."
+    results = unmasker(text, top_k=top_k)
+    output_lines = []
+    for r in results:
+        token = r["token_str"]
+        score = r["score"]
+        sequence = r["sequence"]
+        output_lines.append(f"{sequence}\n→ Prediction: {token} (score: {score:.4f})\n")
+    return "\n".join(output_lines)
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # 🧩 Fill-Mask Demo
+    Type a sentence with a **[MASK]** token to see model predictions.
+    
+    Example:  
+    `Paris is the [MASK] of France.`
+    """)
+
+    text_input = gr.Textbox(label="Input Text", value="The quick brown [MASK] jumps over the lazy dog.")
+    top_k_slider = gr.Slider(label="Top-K Predictions", minimum=1, maximum=10, step=1, value=5)
+    output = gr.Textbox(label="Model Output", lines=12)
+    btn = gr.Button("Generate Predictions")
+
+    btn.click(fill_mask_fn, inputs=[text_input, top_k_slider], outputs=output)
+
+demo.launch()