Upload app.py

app.py

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+import gradio as gr
+from glycowork.ml.processing import dataset_to_dataloader
+import numpy as np
+import torch
+
+def fn(model, class_list):
+  def f(glycan):
+    glycan = [glycan]
+    label = [0]
+    data = next(iter(dataset_to_dataloader(glycan, label, batch_size=1)))
+    device = "cpu"
+    if torch.cuda.is_available():
+      device = "cuda:0"
+    x = data.labels
+    edge_index = data.edge_index
+    batch = data.batch
+    x = x.to(device)
+    edge_index = edge_index.to(device)
+    batch = batch.to(device) 
+    pred = model(x,edge_index, batch).cpu().detach().numpy()
+    pred = np.argmax(pred)
+    pred = class_list[pred]
+    return pred
+  return f
+
+model = torch.load("model.pt")
+model.eval()  
+class_list=['Amoebozoa', 'Animalia', 'Bacteria', 'Bamfordvirae', 'Chromista', 'Euryarchaeota', 'Excavata', 'Fungi', 'Heunggongvirae', 
+            'Orthornavirae', 'Pararnavirae', 'Plantae', 'Proteoarchaeota', 'Protista', 'Riboviria']
+
+f = fn(model, class_list)  
+
+demo = gr.Interface(
+    fn=f,
+    inputs=[gr.Textbox(label="Glycan sequence")],
+    outputs=[gr.Textbox(label="Predicted Class")],
+    allow_flagging=False,
+    title="SweetNet demo",
+    examples=["GlcOSN(a1-4)GlcA(b1-4)GlcOSN(a1-4)GlcAOS(b1-4)GlcOSN(a1-4)GlcOSN",
+    "Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc",
+    "Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GlcNAc(b1-3)Gal(b1-4)Glc-ol"]
+)
+demo.launch(debug=True)