Spaces:
Runtime error
Runtime error
File size: 1,330 Bytes
ae1dc47 281af70 ae1dc47 281af70 edda5b8 281af70 f9db121 281af70 b40d807 ae1dc47 4dcb436 b40d807 ae1dc47 c40b81d ae1dc47 c4f4d6f 42bc81f c4f4d6f 42bc81f ae1dc47 c4f4d6f ae1dc47 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
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()
|