Create app.py

app.py

@@ -0,0 +1,56 @@
+import torch
+import gradio as gr
+from transformers import BioGptTokenizer, BioGptForCausalLM
+
+model_names = [
+    "BioGPT",
+    "BioGPT-Large",
+    "BioGPT-QA-PubMedQA-BioGPT",
+    "BioGPT-QA-PubMEDQA-BioGPT-Large",
+    "BioGPT-RE-BC5CDR",
+    "BioGPT-RE-DDI",
+    "BioGPT-RE-DTI",
+    "BioGPT-DC-HoC"
+]
+
+def load_model(model_name):
+  model_name_map = {
+      "BioGPT":"microsoft/biogpt",
+      "BioGPT-QA-PubMedQA-BioGPT":"microsoft/BioGPT-Large-PubMedQA"
+  }
+
+  tokenizer = BioGptTokenizer.from_pretrained(model_name_map[model_name])
+  model = BioGptForCausalLM.from_pretrained(model_name_map[model_name])
+  return tokenizer, model
+
+
+def get_beam_output(sentence, selected_model, min_len,max_len, n_beams):
+    tokenizer, model = load_model(selected_model)
+    inputs = tokenizer(sentence, return_tensors="pt")
+    with torch.no_grad():
+        beam_output = model.generate(**inputs,
+                                    min_length=100,
+                                    max_length=1024,
+                                    num_beams=n_beams,
+                                    early_stopping=True
+                                    )
+    output=tokenizer.decode(beam_output[0], skip_special_tokens=True)
+    return output
+
+inputs = [
+    gr.inputs.Textbox(label="prompt", lines=5, default="Bicalutamide"),
+    gr.Dropdown(model_names, value="microsoft/biogpt", label="selected_model"),
+    gr.inputs.Slider(1, 500, 1, default=100, label="min_len"),
+    gr.inputs.Slider(1, 2048, 1, default=1024, label="max_len"),
+    gr.inputs.Slider(1, 10, 1, default=5, label="num_beams")
+    ]
+outputs = gr.outputs.Textbox(label="output")
+
+iface = gr.Interface(
+  fn=get_beam_output, 
+  inputs=inputs,
+  outputs=outputs,
+  examples=[["Bicalutamide"], ["Janus kinase 3 (JAK-3)"], ["Apricitabine"], ["Xylazine"], ["Psoralen"], ["CP-673451"]]
+)
+
+iface.launch(debug=True, enable_queue=True)