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app.py

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+import timm
+import torch
+import torch.nn.functional as nnf
+import gradio as gr
+import numpy as np
+import json
+
+class GELU(torch.nn.Module):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.gelu(input)
+torch.nn.modules.activation.GELU = GELU
+
+model = torch.load("/home/user/app/run45.pkl",map_location=torch.device('cpu'))
+
+with open('/home/user/app/val.json', 'r') as handle:
+    parsed = json.load(handle)
+    
+classes = []
+for i in range(len(parsed["categories"])):
+  if parsed["categories"][i]['supercategory'] == 'Plants':
+    classes.append(parsed["categories"][i]['name'])
+    
+classes = set(classes)
+
+classes = list(classes)
+classes.sort()
+
+labels = classes
+
+def classify_image(inp):
+  print(inp.shape)
+  inp = inp.astype(np.uint8).reshape((-1, 3, 224, 224))
+  print(inp.shape)
+  inp = torch.from_numpy(inp).float()
+  #confidences = model(inp)
+
+  preds = nnf.softmax(model(inp).data[0], dim=0)
+  preds = [pred.cpu() for pred in preds]
+  preds = [pred.detach().numpy() for pred in preds]
+
+  #confidences_dict = {classes[i]: float(confidences.data[0][i]) for i in range(len(confidences.data[0]))}
+  confidences_dict = {classes[i]: float(preds[i]) for i in range(len(preds))}
+
+  return confidences_dict
+
+gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(224, 224)),
+             outputs=gr.Label(num_top_classes=3)).launch(debug = True)