fix

app.py

@@ -3,9 +3,44 @@ import gradio as gr
 import os
 import torch
 
-from model import create_effnetb2_model
 from timeit import default_timer as timer
 from typing import Tuple, Dict
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes: int = 1,
+                          seed: int = 42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+    # Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.AlexNet_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.alexnet(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.2,),
+        nn.Linear(in_features=9216, out_features=1),
+    )
+
+    return model, transforms
+
 
 # Setup class names
 class_names = ["Normal", "Pneumonia"]

model.py

@@ -1,36 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes: int = 1,
-                          seed: int = 42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.AlexNet_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.alexNet(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.2,),
-        nn.Linear(in_features=9216, out_features=1),
-    )
-
-    return model, transforms