Update app.py

app.py

@@ -1,21 +1,14 @@
+import gradio as gr
 from transformers import pipeline
 
-# Initialize the fill-mask pipeline with your chosen model
-unmasker = pipeline("fill-mask", model="bert-base-uncased")
-
-def predict(text):
-  """
-  Function to take user input, process it, and return predictions.
-  """
-  # Add any preprocessing steps here if needed (e.g., tokenization)
-  masked_text = f"The sentence is {text}."  # Example with a mask
-
-  # Use the pipeline to get predictions for the masked word
-  predictions = unmasker(masked_text, top_k=3)  # Top 3 most likely words
-  return predictions
-
-# Example usage (replace with your app logic for handling user input)
-user_text = input("Enter a word to fill the mask: ")
-results = predict(user_text)
-for result in results:
-  print(f"Predicted word: {result['token_str']}, Score: {result['score']}")
+def fill_mask(text, mask_token="[MASK]"):
+  fill_mask_pipeline = pipeline("fill-mask")
+  return fill_mask_pipeline(text, mask_token=mask_token)["generated_texts"][0]
+
+interface = gr.Interface(
+  fn=fill_mask,
+  inputs=gr.Textbox(label="Enter text with a mask ([MASK])"),
+  outputs=gr.Textbox(label="Masked text filled"),
+  title="Fill Mask with Transformers",
+  description="Fill the masked word in the sentence using a pre-trained model."
+)