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import os | |
import gradio as gr | |
import torch | |
import numpy as np | |
from transformers import pipeline | |
name_list = ['microsoft/biogpt', 'stanford-crfm/BioMedLM'] | |
examples = [['COVID-19 is'],['A 65-year-old female patient with a past medical history of']] | |
import torch | |
print(f"Is CUDA available: {torch.cuda.is_available()}") | |
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
pipe_biogpt = pipeline("text-generation", model="microsoft/biogpt") | |
pipe_biomedlm = pipeline("text-generation", model="stanford-crfm/BioMedLM", device="cuda:0") | |
title = "Compare generative biomedical LLMs!" | |
description = "This demo compares [BioGPT](https://huggingface.co/microsoft/biogpt) and [BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM). **Disclaimer:** this demo was made for research purposes only and should not be used for medical purposes." | |
def inference(text): | |
output_biogpt = pipe_biogpt(text, max_length=100)[0]["generated_text"] | |
output_biomedlm = pipe_biomedlm(text, max_length=100)[0]["generated_text"] | |
return [ | |
output_biogpt, | |
output_biomedlm | |
] | |
io = gr.Interface( | |
inference, | |
gr.Textbox(lines=3), | |
outputs=[ | |
gr.Textbox(lines=3, label="BioGPT"), | |
gr.Textbox(lines=3, label="BioMedLM") | |
], | |
title=title, | |
description=description, | |
examples=examples | |
) | |
io.launch() | |