app.py

from transformers import pipeline, AutoModelForMaskedLM, AutoTokenizer
import gradio as gr

model_name = "bert-base-uncased
model = AutoModelForMaskedLM.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)

def predict_masked_text(text):
    results = nlp(text)
    return [result['sequence'] for result in results]

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_masked_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter text with a [MASK] token..."),
    outputs=gr.Textbox(),
    title="Masked Language Modeling",
    description="Fill in the masked token in the input text."
)

# Launch the interface
iface.launch()