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app.py

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+
+import torch
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+import gradio as gr
+
+# 14-class Clothing1M labels
+clothing1m_classes = [
+    "T-shirt", "Shirt", "Knitwear", "Chiffon", "Sweater",
+    "Hoodie", "Windbreaker", "Jacket", "Down Coat", "Suits",
+    "Shawl", "Dress", "Vest", "Underwear"
+]
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model
+model = models.resnet50(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, 14)  # match to 14 classes
+try:
+    model.load_state_dict(torch.load("model.pth", map_location=device))
+    model.to(device)
+    model.eval()
+except Exception as e:
+    print("❌ Model loading failed:", e)
+    model = None
+
+# Preprocessing
+def preprocess_image(image):
+    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])
+    ])
+    return transform(image).unsqueeze(0).to(device)
+
+# Prediction
+def classify_image(image):
+    if model is None:
+        return "Model failed to load. Check logs."
+
+    image_tensor = preprocess_image(image)
+    with torch.no_grad():
+        output = model(image_tensor)
+        predicted_idx = output.argmax(dim=1).item()
+    return clothing1m_classes[predicted_idx] if predicted_idx < len(clothing1m_classes) else "Unknown"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Clothing1M ResNet Classifier",
+    description="Upload an image of clothing and get the predicted category."
+)
+
+if __name__ == "__main__":
+    interface.launch()
+