load model checkpoint

app.py

@@ -88,6 +88,8 @@ class Network(nn.Module):
 
         return F.log_softmax(x, dim=1)
     
+model = load_checkpoint("flower_inference_model.pth")
+    
 def process_image(img_path):
     """Scales, crops, and normalizes a PIL image for a PyTorch model,
     returns a Numpy array
@@ -198,7 +200,7 @@ def predict(image_path, model=model, category_names=cat_to_name, topk=5):
         # Plot Functionality
 
         image = Image.open(image_path)
-        fig, (ax1, ax2) = plt.subplots(ncols=2)
+        fig, (ax1, ax2) = plt.subplots(figsize=(8, 8), ncols=2)
         ax1.imshow(image)
         ax1.axis("off")
         ax2.barh(np.arange(len(top_labels)), percentages)