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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', 'facebook/galactica-1.3b']
examples = [['COVID-19 is'],['A 65-year-old female patient with a past medical history of']]
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-Large", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
pipe_biomedlm = pipeline("text-generation", model="stanford-crfm/BioMedLM", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
pipe_galactica = pipeline("text-generation", model="facebook/galactica-1.3b", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
title = "Compare generative biomedical LLMs!"
description = "**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"]
output_galactica = pipe_galactica(text, max_length=100)[0]["generated_text"]
return [
output_biogpt,
output_biomedlm,
output_galactica
]
io = gr.Interface(
inference,
gr.Textbox(lines=3),
outputs=[
gr.Textbox(lines=3, label="BioGPT-Large"),
gr.Textbox(lines=3, label="BioMedLM (fka PubmedGPT)"),
gr.Textbox(lines=3, label="Galactica 1.3B"),
],
title=title,
description=description,
examples=examples
)
io.launch()
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