app.py

import gradio as gr
import numpy as np
from transformers import pipeline

unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")

example = 'A crustless sandwich made from two slices of baked bread'

def unmask(text):
    res = unmasker(text)
    out = {item["token_str"]: item["score"] for item in res}
    return out


textbox = gr.Textbox(label="Type language here", lines=5)
import gradio as gr
from transformers import pipeline

unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")


def add_mask(text, size=3):
    split_text = text.split()
    idx = np.random.randint(len(split_text), size=size)
    for i in idx:
        split_text[i] = '[MASK]'
    return ' '.join(split_text)


def unmask(text):
    text = add_mask(text)
    res = unmasker(text)
    out = {item["token_str"]: item["score"] for item in res}
    return out


textbox = gr.Textbox(label="Type language here", lines=5)

demo = gr.Interface(
    fn=unmask,
    inputs=textbox,
    outputs="label",
    examples=[

    ],
)

demo.launch()
demo = gr.Interface(
    fn=unmask,
    inputs=textbox,
    outputs="label",
    examples=[
    ],
)

demo.launch()