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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| md_name1 = "vinai/vinai-translate-vi2en-v2" | |
| tokenizer_vi2en = AutoTokenizer.from_pretrained(md_name1, src_lang="vi_VN") | |
| model_vi2en = AutoModelForSeq2SeqLM.from_pretrained(md_name1) | |
| def translate_vi2en(vi_text: str) -> str: | |
| input_ids = tokenizer_vi2en(vi_text, return_tensors="pt").input_ids | |
| output_ids = model_vi2en.generate( | |
| input_ids, | |
| decoder_start_token_id=tokenizer_vi2en.lang_code_to_id["en_XX"], | |
| num_return_sequences=1, | |
| # # With sampling | |
| do_sample=True, | |
| top_k=100, | |
| top_p=0.8, | |
| # With beam search | |
| num_beams=5, | |
| early_stopping=True | |
| ) | |
| en_text = tokenizer_vi2en.batch_decode(output_ids, skip_special_tokens=True) | |
| en_text = " ".join(en_text) | |
| return en_text | |
| md_name2 = "vinai/vinai-translate-en2vi-v2" | |
| tokenizer_en2vi = AutoTokenizer.from_pretrained(md_name2, src_lang="en_XX") | |
| model_en2vi = AutoModelForSeq2SeqLM.from_pretrained(md_name2) | |
| def translate_en2vi(en_text: str) -> str: | |
| input_ids = tokenizer_en2vi(en_text, return_tensors="pt").input_ids | |
| output_ids = model_en2vi.generate( | |
| input_ids, | |
| decoder_start_token_id=tokenizer_en2vi.lang_code_to_id["vi_VN"], | |
| num_return_sequences=1, | |
| # With sampling | |
| do_sample=True, | |
| top_k=100, | |
| top_p=0.8, | |
| # With beam search | |
| num_beams=5, | |
| early_stopping=True | |
| ) | |
| vi_text = tokenizer_en2vi.batch_decode(output_ids, skip_special_tokens=True) | |
| vi_text = " ".join(vi_text) | |
| return vi_text | |
| vi_example_text = ["Xin chào, chúng tôi là nhóm 01, bao gồm 3 thành viên: Minh Trí, Kim Thanh và Hồng Ngọc", | |
| "Chúng ta đang từng bước học cách trở nên tốt đẹp hơn!", | |
| "Bạn có phải là người chăm chỉ?", | |
| "Luận văn thạc sĩ Khoa học Máy tính", | |
| "Hãy sống như những đoá hoa toả ngát hương thơm"] | |
| en_example_text = ["Life is countless days of trying.", | |
| "Always remember, what doesn't kill you makes you stronger", | |
| "What's up man?", | |
| "How could you...?", | |
| "Could you do me a favor?"] | |
| # GIAO DIỆN WEB MACHINE TRANSLATION | |
| with gr.Blocks(theme=gr.themes.Soft(), title="Charmed's One MT") as demo: | |
| with gr.Row(): | |
| test = gr.Text(label="MACHINE TRANSLATION", value="The Application of English-Vietnamese automatic translation was created by The Power of Three: Doan Minh Tri, Che Thi Kim Thanh and Nguyen Thi Hong Ngoc",) | |
| with gr.Tabs(): | |
| with gr.TabItem("VIETNAMESE TO ENGLISH"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| vietnamese = gr.Textbox(label="Vietnamese Text") | |
| gr.ClearButton(vietnamese) | |
| with gr.Column(): | |
| english = gr.Textbox(label="English Text") | |
| translate_to_english = gr.Button(value="Translate To English") | |
| translate_to_english.click(lambda text: translate_vi2en(text), inputs=vietnamese, outputs=english) | |
| gr.Examples(examples=vi_example_text, | |
| inputs=[vietnamese]) | |
| with gr.TabItem("ENGLISH TO VIETNAMESE"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| english = gr.Textbox(label="English Text") | |
| gr.ClearButton(english) | |
| with gr.Column(): | |
| vietnamese = gr.Textbox(label="Vietnamese Text") | |
| translate_to_vietnamese = gr.Button(value="Translate To Vietnamese") | |
| translate_to_vietnamese.click(lambda text: translate_en2vi(text), inputs=english, outputs=vietnamese) | |
| gr.Examples(examples=en_example_text, | |
| inputs=[english]) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) #share=True NẾU MUỐN ONLINE |