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import gradio as gr | |
from transformers import pipeline | |
examples = [ | |
'Alisher Navoiy – ulug‘ o‘zbek va boshqa turkiy xalqlarning <mask>, mutafakkiri va davlat arbobi bo‘lgan.', | |
'Oʻzbekistonning poytaxti <mask> shahri boʻlib, davlat tili oʻzbek tili hisoblanadi.', | |
'Registon maydoni - tarixda shaharning ilm-fan, siyosat va <mask> markazi boʻlgan.', | |
'Venera - Quyosh tizimidagi o‘z o‘qi atrofida soat sohasi farqli ravishda aylanadigan yagona <mask>.', | |
'Kuchli yomg‘irlar tufayli bir qator <mask> kuchli sel oqishi kuzatildi.', | |
'Oʻzbekiston iqtisodiyoti bozor <mask> bosqichma-bosqich oʻtadi, tashqi savdo siyosati import oʻrnini bosishga asoslangan.' | |
] | |
models = [ | |
"sinonimayzer/UzRoBERTa-v1", | |
"sinonimayzer/UzRoBERTa-v2", | |
"sinonimayzer/UzRoBERTa-v2", | |
"rifkat/uztext-3Gb-BPE-Roberta", | |
"tahrirchi/tahrirchi-bert-base", | |
] | |
def df(arr): | |
d = {} | |
for val in arr: | |
d[val['token_str']] = val['score'] | |
return d | |
def fn(text): | |
arr = [] | |
for model in models: | |
arr.append(df(pipeline("fill-mask", model=model)(text))) | |
return arr[0], arr[1], arr[2], arr[3], arr[4] | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(): | |
output0 = gr.Label(label=models[0]) | |
with gr.Column(): | |
output1 = gr.Label(label=models[1]) | |
with gr.Column(): | |
output2 = gr.Label(label=models[2]) | |
with gr.Row(): | |
with gr.Column(): | |
output3 = gr.Label(label=models[3]) | |
with gr.Column(): | |
output4 = gr.Label(label=models[4]) | |
with gr.Column(): | |
input = gr.Textbox(label="Input", value=examples[0], lines=8, max_lines=8) | |
btn = gr.Button("Check") | |
gr.Examples(examples, fn=fn, inputs=[input], outputs=[output0, output1, output2, output3, output4], cache_examples=True, batch=True) | |
btn.click(fn, inputs=[input], outputs=[output0, output1, output2, output3, output4]) | |
if __name__ == "__main__": | |
demo.queue().launch() | |