Created App.py

app.py

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+import torch
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+from ResNet_for_CC import CC_model
+
+# Initialize the model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = CC_model()
+
+# Load the pre-trained weights, adjusting for DataParallel if necessary
+model_path = 'CC_net.pt'
+checkpoint = torch.load(model_path, map_location=device)
+if any(key.startswith('module.') for key in checkpoint.keys()):
+    checkpoint = {k.replace('module.', ''): v for k, v in checkpoint.items()}
+model.load_state_dict(checkpoint)
+model.eval()
+model.to(device)
+
+# Image preprocessing
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Define class names from category_names_eng.txt
+class_names = [
+    'T-Shirt', 'Shirt', 'Knitwear', 'Chiffon', 'Sweater', 'Hoodie', 
+    'Windbreaker', 'Jacket', 'Downcoat', 'Suit', 'Shawl', 'Dress', 
+    'Vest', 'Underwear'
+]
+
+def predict(image):
+    # Convert Gradio Image to PIL and preprocess
+    img = Image.fromarray(image.astype('uint8'), 'RGB')
+    img = preprocess(img).unsqueeze(0).to(device)
+    
+    # Generate predictions
+    with torch.no_grad():
+        dr_feature, output_mean = model(img)
+    
+    # Get the predicted class
+    _, predicted = torch.max(output_mean, 1)
+    predicted_class = class_names[predicted.item()]
+    
+    # Format output
+    return f"Predicted class: {predicted_class} (class number: {predicted.item()})"
+
+# Example images from Hugging Face
+examples = [
+    ["example_image(1).JPG"],
+    ["example_image(2).jpg"],
+    ["example_image(3).jpg"],
+    ["example_image(4).webp"],
+    ["example_image(5).webp"],
+    ["example_image(6).webp"]
+]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload Clothing Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Clothing1M Class Predictor",
+    description="This model predicts the class of clothing images using CC_net.",
+    examples=examples
+)
+
+interface.launch()