Update app.py

app.py

@@ -1,56 +1,48 @@
-# 🔍 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)
+fill_mask = pipeline("fill-mask", model="Keyurjotaniya007/bert-large-cased-wikitext-mlm-3.0", 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:
+with gr.Blocks(title="Masked Language Modeling") as demo:
     gr.Markdown(
-        "# 🔍 Masked Word Predictor\n"
+        "# Masked Language Modeling\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].",
+            placeholder="e.g. The Great Wall of [MASK] is visible from space.",
             label="Input Sentence"
         )
         top_k = gr.Slider(
             minimum=1, maximum=10, step=1, value=5,
-            label="Top K Predictions"
+            label="K Predictions[Min=1 & Max=10]"
         )
 
-    predict_btn = gr.Button("Predict 🔍", variant="primary")
+    predict_btn = gr.Button("Evaluate [MASK] Words", variant="primary")
 
     results_df = gr.Dataframe(
         headers=["Sequence", "Score"],
@@ -67,4 +59,4 @@ with gr.Blocks(title="🔍 Masked Word Predictor") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0")
+    demo.launch(server_name="0.0.0.0")