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| from transformers import pipeline, set_seed | |
| from transformers import BioGptTokenizer, BioGptForCausalLM | |
| from multilingual_translation import translate | |
| from utils import lang_ids | |
| import gradio as gr | |
| model_list = [ | |
| "microsoft/biogpt", | |
| "microsoft/BioGPT-Large-PubMedQA" | |
| ] | |
| lang_list = list(lang_ids.keys()) | |
| def translate_to_english(text, base_lang): | |
| if base_lang == "en": | |
| return text | |
| else: | |
| new_text = translate("facebook/m2m100_418M", text, base_lang, "en") | |
| return new_text | |
| def biogpt( | |
| prompt: str, | |
| model_id: str, | |
| max_length: int = 25, | |
| num_return_sequences: int = 5, | |
| base_lang: str = "en" | |
| ): | |
| en_prompt = translate_to_english(prompt, base_lang) | |
| model = BioGptForCausalLM.from_pretrained(model_id) | |
| tokenizer = BioGptTokenizer.from_pretrained(model_id) | |
| generator = pipeline('text-generation', model=model, tokenizer=tokenizer) | |
| set_seed(42) | |
| output = generator(en_prompt, max_length=max_length, num_return_sequences=num_return_sequences, do_sample=True) | |
| output_dict = { | |
| "1": output[0]['generated_text'], | |
| "2": output[1]['generated_text'], | |
| "3": output[2]['generated_text'], | |
| "4": output[3]['generated_text'], | |
| "5": output[4]['generated_text'] | |
| } | |
| return f'{output_dict["1"]}\n\n{output_dict["2"]}\n\n{output_dict["3"]}\n\n{output_dict["4"]}\n\n{output_dict["5"]}' | |
| inputs = [ | |
| gr.inputs.Textbox(label="Prompt", lines=5, default="COVID-19 is"), | |
| gr.Dropdown(model_list, value="microsoft/biogpt", label="Model ID"), | |
| gr.inputs.Slider(5, 100, 25, default=25, label="Max Length"), | |
| gr.inputs.Slider(1, 10, 5, default=5, label="Num Return Sequences"), | |
| gr.Dropdown(lang_list, value="en", label="Base Language") | |
| ] | |
| outputs = gr.outputs.Textbox(label="Output") | |
| examples = [ | |
| ["COVID-19 is", "microsoft/biogpt", 25, 5, "en"], | |
| ["Kanser", "microsoft/BioGPT-Large-PubMedQA", 25, 5, "tr"], | |
| ["Covid-19 est", "microsoft/biogpt", 25, 5, "fr"], | |
| ] | |
| title = " BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining" | |
| demo_app = gr.Interface( | |
| biogpt, | |
| inputs, | |
| outputs, | |
| title=title, | |
| examples=examples, | |
| cache_examples=True, | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) | |