Add app.py and examples

app.py

@@ -0,0 +1,61 @@
+import torch
+import numpy as np
+import gradio as gr
+from pathlib import Path
+from PIL import Image
+from torchvision import transforms
+from huggingface_hub import hf_hub_download
+from ResNet_for_CC import CC_model
+
+# Define the Clothing1M class labels
+CLOTHING1M_CLASSES = [
+    "T-Shirt", "Shirt", "Knitwear", "Chiffon", "Sweater",
+    "Hoodie", "Windbreaker", "Jacket", "Downcoat",
+    "Suit", "Shawl", "Dress", "Vest", "Underwear"
+]
+
+# Initialize the model
+model = CC_model()
+model_path = hf_hub_download(repo_id="mohamdlog/CC", filename="CC_net.pt")
+model.load_state_dict(torch.load(model_path, map_location='cpu'))
+model.eval()
+
+# Define preprocessing pipeline
+def preprocess_image(image):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+    return transform(image).unsqueeze(0)
+
+# Define classification function
+def classify_image(image):
+    input_tensor = preprocess_image(image)
+    with torch.no_grad():
+        output = model(input_tensor)
+    
+    # Get predicted class and confidence
+    probabilities = torch.nn.functional.softmax(output, dim=1)
+    predicted_class_idx = output.argmax(dim=1).item()
+    predicted_class = CLOTHING1M_CLASSES[predicted_class_idx]
+    confidence = probabilities[0][predicted_class_idx].item()
+    
+    return f"Category: {predicted_class}\nConfidence: {confidence:.2f}"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_image, 
+    inputs=gr.Image(label="Uploaded Image"), 
+    outputs=gr.Text(label="Predicted Clothing"), 
+    title="Clothing Category Classifier",
+    description="Upload an image of clothing, and the model will predict its category.",
+    examples = [[str(file)] for file in Path("examples").glob("*")],
+    flagging_mode="never",
+    theme="soft"
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()