Update app.py

app.py

@@ -7,14 +7,14 @@ import os
 # Use CPU
 device = torch.device('cpu')
 
-# define ResNet-50 architecture
+# Define ResNet-50 Architecture
 model = models.resnet50(weights=None)
 
-# change the output to 37 (num_classes)
+# revised full connected layer to 37 (num_classes)
 model.fc = torch.nn.Linear(2048, 37)
 
-# Load model weights
-model.load_state_dict(torch.load('/content/Oxford_Pet_classifier/resnet50_model_weights.pth', map_location=device))
+# Load Model weights
+model.load_state_dict(torch.load('./resnet50_model_weights.pth', map_location=device))
 
 model.eval()
 
@@ -37,25 +37,25 @@ class_names = ['Abyssinian (阿比西尼亞貓)', 'American Bulldog (美國鬥
 
 # predict function
 def classify_image(image):
-    image = transform(image).unsqueeze(0).to(device)  # make sure on the cpu
+    image = transform(image).unsqueeze(0).to(device)  # make sure prediction on cpu
     with torch.no_grad():
         outputs = model(image)
         probabilities, indices = torch.topk(outputs, k=3)  # top 3 predictions
-        probabilities = torch.nn.functional.softmax(probabilities, dim=1)  # turn into probabilities
+        probabilities = torch.nn.functional.softmax(probabilities, dim=1)  
         predictions = [(class_names[idx], prob.item()) for idx, prob in zip(indices[0], probabilities[0])]
         return {class_name: f"{prob * 100:.2f}%" for class_name, prob in predictions}
 
-# define examples_path
-examples_path = '/content/Oxford_Pet_classifier/examples'
 
-# make sure examples is exists
+examples_path = './examples'
+
 if os.path.exists(examples_path):
-    examples = [[examples_path + "/" + img] for img in os.listdir(examples_path)]
+    print(f"[INFO] Found examples folder at {examples_path}")
 else:
     print(f"[ERROR] Examples folder not found at {examples_path}")
-    examples = []  
 
 # Gradio Interface
+examples = [[examples_path + "/" + img] for img in os.listdir(examples_path)]
+
 demo = gr.Interface(fn=classify_image,  
                     inputs=gr.Image(type="pil"),  
                     outputs=[gr.Label(num_top_classes=3, label="Top 3 Predictions")],  
@@ -64,4 +64,4 @@ demo = gr.Interface(fn=classify_image,
                     description='A ResNet50-based model for classifying 37 different pet breeds.',
                     article='https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/The%20Oxford-IIIT%20Pet%20Project')  
 
-demo.launch()
+demo.launch()