| import gradio as gr | |
| from transformers import AutoTokenizer, T5ForConditionalGeneration | |
| tokenizer = AutoTokenizer.from_pretrained("CodeTed/CGEDit") | |
| model = T5ForConditionalGeneration.from_pretrained("CodeTed/CGEDit") | |
| def cged_correction(sentence, function): | |
| input_ids = tokenizer('糾正句子裡的錯字：' + sentence, return_tensors="pt").input_ids | |
| outputs = model.generate(input_ids, max_length=200) | |
| edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return edited_text | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # 中文錯別字校正 - Chinese Spelling Correction | |
| ### Find Spelling Error and get the correction！ | |
| Start typing below to see the correction. | |
| """ | |
| ) | |
| funt = gr.Radio(["add", "subtract", "multiply", "divide"]) | |
| #設定輸入元件 | |
| sent = gr.Textbox(label="Sentence", placeholder="input the sentence") | |
| # 設定輸出元件 | |
| output = gr.Textbox(label="Result", placeholder="correction") | |
| #設定按鈕 | |
| greet_btn = gr.Button("Correction") | |
| #設定按鈕點選事件 | |
| greet_btn.click(fn=cged_correction, inputs=[sent, funt], outputs=output) | |
| demo.launch() |