Update app.py

app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+from ResNet_for_CC import CC_model  # Import updated model
+
+# Set device (CPU/GPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the trained CC_model
+model_path = "CC_net.pt"  # Ensure correct path
+model = CC_model(num_classes1=14)  # Updated model with classification
+model.load_state_dict(torch.load(model_path, map_location=device))
+model.to(device)
+model.eval()
+
+# Define Clothing1M Class Labels
+class_labels = [
+    "T-Shirt", "Shirt", "Knitwear", "Chiffon", "Sweater", "Hoodie",
+    "Windbreaker", "Jacket", "Downcoat", "Suit", "Shawl", "Dress",
+    "Vest", "Underwear"
+]
+
+# Define preprocessing for images
+transform = 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])
+])
+
+# Function for Image Classification
+def classify_image(image):
+    image = transform(image).unsqueeze(0).to(device)  # Preprocess image
+    with torch.no_grad():
+        _, output = model(image)  # Unpack to get only output_mean
+        predicted_class = torch.argmax(output, dim=1).item()  # Get class index
+
+    return f"Predicted Class: {class_labels[predicted_class]}"
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=classify_image, 
+    inputs=gr.Image(type="pil"), 
+    outputs="text",
+    title="Clothing1M Image Classifier",
+    description="Upload a clothing image, and the model will classify it into one of the 14 categories."
+)
+
+# Run the Interface
+if __name__ == "__main__":
+    interface.launch()