Update app.py

app.py

@@ -1,9 +1,42 @@
+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("text2text-generation", model="microsoft/biogpt")
+pipe_biomedlm = pipeline("text2text-generation", model="stanford-crfm/BioMedLM")
+
+title = "Compare generative biomedical LLMs!"
+description = "This demo compares [BioGPT](https://huggingface.co/microsoft/biogpt) and [BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM)."
+
+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()
+
+"""
 def generate_biomedical(text):
     interfaces = [gr.Interface.load(name) for name in name_list]
     return [interface(text) for interface in interfaces]
@@ -28,4 +61,5 @@ with gr.Blocks() as demo:
                 gr.Markdown("Let’s compare!")      
                 btn.click(generate_biomedical, inputs = input_text, outputs = [gr.Textbox(label=name_list[_], lines=4) for _ in range(len(name_list))])
 
-demo.launch(enable_queue=True, debug=True)
+demo.launch(enable_queue=True, debug=True)
+"""