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app.py

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+import torch
+from torchvision import transforms
+from torchvision import models
+from PIL import Image
+import gradio as gr
+import os
+
+# Use CPU
+device = torch.device('cpu')
+
+# Load the model ResNet-50 model architecture
+model = models.resnet50(pretrained=False)
+
+# Load model's weight to CPU
+model = torch.load('resnet50_model_weights.pth', map_location=device)
+model.eval()
+
+# Define the image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Define the class names
+class_names = ['Abyssinian', 'American Bulldog', 'American Pit Bull Terrier', 'Basset Hound', 'Beagle', 'Bengal', 'Birman', 'Bombay', 
+               'Boxer', 'British Shorthair', 'Chihuahua', 'Egyptian Mau', 'English Cocker Spaniel', 'English Setter', 'German Shorthaired', 
+               'Great Pyrenees', 'Havanese', 'Japanese Chin', 'Keeshond', 'Leonberger', 'Maine Coon', 'Miniature Pinscher', 'Newfoundland', 
+               'Persian', 'Pomeranian', 'Pug', 'Ragdoll', 'Russian Blue', 'Saint Bernard', 'Samoyed', 'Scottish Terrier', 'Shiba Inu', 
+               'Siamese', 'Sphynx', 'Staffordshire Bull Terrier', 'Wheaten Terrier', 'Yorkshire Terrier']
+
+# Define the predict function
+def classify_image(image):
+    image = transform(image).unsqueeze(0).to(device)  # Ensure image data is processed on CPU
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs, 1)
+        return class_names[predicted.item()]
+
+# Custom Gradio interface title, description, and article
+title = 'Oxford Pet 🐈🐕'
+description = 'A ResNet50-based computer vision model for classifying images of pets from the Oxford-IIIT Pet Dataset. The model can recognize 37 different pet breeds, including cats and dogs.'
+article = 'https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/The%20Oxford-IIIT%20Pet%20Project'
+
+# Gradio interface
+examples = [["examples/" + img] for img in os.listdir('examples')]
+
+demo = gr.Interface(fn=classify_image,  # Map input to output function
+                    inputs=gr.Image(type="pil"),  # Image input
+                    outputs=[gr.Label(num_top_classes=1, label="Predictions")],  # Predicted label
+                    examples=examples,  # Example images
+                    title=title,  
+                    description=description,  
+                    article=article)  
+
+# Launch the demo
+demo.launch()

requirements.txt

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+torch==2.3.0
+torchvision==0.18.0
+gradio==4.42.0
+Pillow==9.2.0  

resnet50_model_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa799a82adff86b3fe384f7b86e925d75e8f434255cff9464b2ceeeec1cd5b69
+size 94657902