Upload 2 files

app.py

@@ -0,0 +1,70 @@
+# 🔍 Masked Word Predictor | CPU-only HF Space
+
+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+from transformers.pipelines.base import PipelineException
+
+# 1. Load the fill-mask pipeline once
+fill_mask = pipeline("fill-mask", model="distilroberta-base", device=-1)
+
+def predict_mask(sentence: str, top_k: int):
+    # 2. Get the actual mask token (e.g. "<mask>")
+    mask = fill_mask.tokenizer.mask_token
+
+    # 3. Allow users to type [MASK]
+    sentence = sentence.replace("[MASK]", mask)
+
+    # 4. Validate presence of mask
+    if mask not in sentence:
+        return pd.DataFrame(
+            [["Error: please include `[MASK]` in your sentence.", 0.0]],
+            columns=["Sequence", "Score"]
+        )
+
+    # 5. Run the pipeline safely
+    try:
+        preds = fill_mask(sentence, top_k=top_k)
+    except PipelineException as e:
+        return pd.DataFrame([[f"Error: {str(e)}", 0.0]],
+                            columns=["Sequence", "Score"])
+
+    # 6. Build a DataFrame from list-of-lists
+    rows = [[p["sequence"], round(p["score"], 3)] for p in preds]
+    return pd.DataFrame(rows, columns=["Sequence", "Score"])
+
+with gr.Blocks(title="🔍 Masked Word Predictor") as demo:
+    gr.Markdown(
+        "# 🔍 Masked Word Predictor\n"
+        "Enter a sentence with one `[MASK]` token and see the top-K completions."
+    )
+
+    with gr.Row():
+        sentence = gr.Textbox(
+            lines=2,
+            placeholder="e.g. The salon’s new color treatment is [MASK].",
+            label="Input Sentence"
+        )
+        top_k = gr.Slider(
+            minimum=1, maximum=10, step=1, value=5,
+            label="Top K Predictions"
+        )
+
+    predict_btn = gr.Button("Predict 🔍", variant="primary")
+
+    results_df = gr.Dataframe(
+        headers=["Sequence", "Score"],
+        datatype=["str", "number"],
+        wrap=True,
+        interactive=False,
+        label="Predictions"
+    )
+
+    predict_btn.click(
+        fn=predict_mask,
+        inputs=[sentence, top_k],
+        outputs=results_df
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0")

requirements.txt

@@ -0,0 +1,3 @@
+gradio==5.31.0
+transformers>=4.30.0
+torch>=2.0.0