| 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, |
| |
| do_sample=True, |
| top_k=100, |
| top_p=0.8, |
| |
| 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, |
| |
| do_sample=True, |
| top_k=100, |
| top_p=0.8, |
| |
| 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?"] |
|
|
| |
| 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) |
