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| from transformers import pipeline | |
| from multilingual_translation import text_to_text_generation | |
| from utils import lang_ids | |
| import gradio as gr | |
| paper_id = "kadirnar/biogpt_paper" | |
| biogpt_model_list = [ | |
| "microsoft/biogpt", | |
| "microsoft/BioGPT-Large", | |
| "microsoft/BioGPT-Large-PubMedQA" | |
| ] | |
| lang_model_list = [ | |
| "facebook/m2m100_1.2B", | |
| "facebook/m2m100_418M" | |
| ] | |
| whisper_model_list = [ | |
| "openai/whisper-small", | |
| "openai/whisper-medium", | |
| "openai/whisper-tiny", | |
| "openai/whisper-large" | |
| ] | |
| lang_list = list(lang_ids.keys()) | |
| def whisper_demo(input_audio, model_id): | |
| pipe = pipeline(task="automatic-speech-recognition",model=model_id, device='cuda:0') | |
| pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language='en', task="transcribe") | |
| output_text = pipe(input_audio)['text'] | |
| return output_text | |
| def translate_to_english(prompt, lang_model_id, base_lang): | |
| if base_lang == "English": | |
| return prompt | |
| else: | |
| output_text = text_to_text_generation( | |
| prompt=prompt, | |
| model_id=lang_model_id, | |
| device='cuda:0', | |
| target_lang='en' | |
| ) | |
| return output_text[0] | |
| def biogpt_text( | |
| prompt: str, | |
| biogpt_model_id: str, | |
| lang_model_id: str, | |
| base_lang: str, | |
| ): | |
| en_prompt = translate_to_english(prompt, lang_model_id, base_lang) | |
| generator = pipeline("text-generation", model=biogpt_model_id, device="cuda:0") | |
| output = generator(en_prompt, max_length=250, num_return_sequences=1, do_sample=True) | |
| output = output[0]['generated_text'] | |
| if base_lang == "English": | |
| output_text = output | |
| else: | |
| output_text = text_to_text_generation( | |
| prompt=output, | |
| model_id=lang_model_id, | |
| device='cuda:0', | |
| target_lang=lang_ids[base_lang] | |
| ) | |
| return en_prompt, output, output_text | |
| def biogpt_audio( | |
| input_audio: str, | |
| biogpt_model_id: str, | |
| whisper_model_id: str, | |
| base_lang: str, | |
| lang_model_id: str, | |
| ): | |
| en_prompt = whisper_demo(input_audio=input_audio, model_id=whisper_model_id) | |
| generator = pipeline("text-generation", model=biogpt_model_id, device="cuda:0") | |
| output = generator(en_prompt, max_length=250, num_return_sequences=1, do_sample=True) | |
| output = output[0]['generated_text'] | |
| if base_lang == "English": | |
| output_text = output | |
| else: | |
| output_text = text_to_text_generation( | |
| prompt=output, | |
| model_id=lang_model_id, | |
| device='cuda:0', | |
| target_lang=lang_ids[base_lang] | |
| ) | |
| return en_prompt, output, output_text | |
| question_example = "Can 'high-risk' human papillomaviruses (HPVs) be detected in human breast milk? context: Using polymerase chain reaction techniques, we evaluated the presence of HPV infection in human breast milk collected from 21 HPV-positive and 11 HPV-negative mothers. Of the 32 studied human milk specimens, no 'high-risk' HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 or 58 DNA was detected. answer: This preliminary case-control study indicates the absence of mucosal 'high-risk' HPV types in human breast milk." | |
| examples = [ | |
| ["COVID-19 is", biogpt_model_list[0], lang_model_list[1], "English"], | |
| [question_example, biogpt_model_list[2], lang_model_list[1], "English"] | |
| ] | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# **<h4 align='center'>Whisper + M2M100 + BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining<h4>**") | |
| gr.Markdown( | |
| """ | |
| <h5 style='text-align: center'> | |
| Follow me for more! | |
| <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | | |
| </h5> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Tab("Text"): | |
| input_text = gr.Textbox(lines=3, value="COVID-19 is", label="Text") | |
| text_biogpt = gr.Dropdown(choices=biogpt_model_list, value=biogpt_model_list[0], label='BioGpt Model') | |
| text_m2m100 = gr.Dropdown(choices=lang_model_list, value=lang_model_list[1], label='Language Model') | |
| text_lang = gr.Dropdown(lang_list, value="English", label="Base Language") | |
| text_button = gr.Button(value="Predict") | |
| with gr.Tab("Audio"): | |
| input_audio = gr.Audio(source="microphone", type="filepath", label='Audio') | |
| audio_biogpt = gr.Dropdown(choices=biogpt_model_list, value=biogpt_model_list[0], label='BioGpt Model') | |
| audio_whisper = gr.Dropdown(choices=whisper_model_list, value=whisper_model_list[0], label='Audio Model') | |
| audio_lang = gr.Dropdown(lang_list, value="English", label="Base Language") | |
| audio_m2m100 = gr.Dropdown(choices=lang_model_list, value=lang_model_list[1], label='Language Model') | |
| audio_button = gr.Button(value="Predict") | |
| with gr.Tab("Output"): | |
| with gr.Column(): | |
| prompt_text = gr.Textbox(lines=3, label="Prompt") | |
| output_text = gr.Textbox(lines=3, label="BioGpt Text") | |
| translated_text = gr.Textbox(lines=3,label="Translated Text") | |
| gr.Examples(examples, inputs=[input_text, text_biogpt, text_m2m100,text_lang], outputs=[prompt_text, output_text, translated_text], fn=biogpt_text, cache_examples=False) | |
| text_button.click(biogpt_text, inputs=[input_text, text_biogpt, text_m2m100 ,text_lang], outputs=[prompt_text, output_text, translated_text]) | |
| audio_button.click(biogpt_audio, inputs=[input_audio, audio_biogpt, audio_whisper, audio_lang, audio_m2m100], outputs=[prompt_text, output_text, translated_text]) | |
| app.launch() |