Upload app.py

app.py

@@ -6,19 +6,24 @@ import gradio as gr
 from glycowork.ml.processing import dataset_to_dataloader
 import numpy as np
 import torch
-from glycowork.glycan_data.loader import lib
 
 
-def fn(class_list):
-  def f(glycan, model):
+class_list=['Amoebozoa', 'Animalia', 'Bacteria', 'Bamfordvirae', 'Chromista', 'Euryarchaeota', 'Excavata', 'Fungi', 'Heunggongvirae', 
+            'Orthornavirae', 'Pararnavirae', 'Plantae', 'Proteoarchaeota', 'Protista', 'Riboviria']
+
+model1 = torch.load("model1.pt", map_location=torch.device('cpu'))
+model2 = torch.load("model2.pt", map_location=torch.device('cpu'))
+model3 = torch.load("model3.pt", map_location=torch.device('cpu'))
+
+def fn(glycan, model):
     if model == "No data augmentation":
-      model = torch.load("model1.pt", map_location=torch.device('cpu'))
+      model = model1
       model.eval()
     elif model == "Ensemble":
-      model = torch.load("model3.pt", map_location=torch.device('cpu'))
+      model = model3
       model.eval()
     else:
-      model = torch.load("model2.pt", map_location=torch.device('cpu'))
+      model = model2
       model.eval()
     glycan = [glycan]
     label = [0]
@@ -37,10 +42,9 @@ def fn(class_list):
     pred = [float(x) for x in pred]
     pred = {class_list[i]:pred[i] for i in range(15)}
     return pred
-  return f
 
-class_list=['Amoebozoa', 'Animalia', 'Bacteria', 'Bamfordvirae', 'Chromista', 'Euryarchaeota', 'Excavata', 'Fungi', 'Heunggongvirae', 
-            'Orthornavirae', 'Pararnavirae', 'Plantae', 'Proteoarchaeota', 'Protista', 'Riboviria']
+
+
 
 f = fn(class_list)