Upload app.py with huggingface_hub

app.py

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+# import torch
+# import gradio as gr
+# import torchvision.transforms as transforms
+# from PIL import Image
+# from huggingface_hub import hf_hub_download
+# from C2D.models.resnet import SupCEResNet
+
+# # Define class labels
+# class_labels = [
+#     "T-shirt", "Shirt", "Knitwear", "Chiffon", "Sweater",
+#     "Hoodie", "Windbreaker", "Jacket", "Down Coat", "Suit",
+#     "Shawl", "Dress", "Vest", "Underwear"
+# ]
+
+# # Load model from Hugging Face Hub
+# def load_model_from_huggingface(repo_id="tfarhan10/Clothing1M-Pretrained-ResNet50", filename="model.pth"):
+#     try:
+#         print("Downloading model from Hugging Face...")
+#         checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
+#         # Load checkpoint
+#         checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'),weights_only=False)
+
+#         # Extract state_dict if stored in a dictionary
+#         if isinstance(checkpoint, dict) and "model" in checkpoint:
+#             state_dict = checkpoint["model"]
+#         else:
+#             state_dict = checkpoint
+
+#         # Fix "module." prefix issue
+#         new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+
+#         # Initialize model
+#         model = SupCEResNet(name='resnet50', num_classes=14, pool=True)
+
+#         # Load weights
+#         model.load_state_dict(new_state_dict, strict=False)  # `strict=False` allows minor mismatches
+#         model.eval()  # Set model to evaluation mode
+
+#         print("✅ Model loaded successfully from Hugging Face!")
+#         return model
+
+#     except Exception as e:
+#         print(f"❌ Error loading model: {e}")
+#         return None
+
+
+# # Load the model
+# model = load_model_from_huggingface()
+
+# def classify_image(image):
+#     """Process and classify an uploaded PIL image accurately."""
+    
+#     # Convert image to RGB to avoid grayscale or RGBA issues
+#     if image.mode != "RGB":
+#         image = image.convert("RGB")
+
+#     # Define the same preprocessing pipeline as training
+#     transform_test = transforms.Compose([
+#         transforms.Resize(256),  # Resize the shorter side to 256
+#         transforms.CenterCrop(224),  # Center crop to 224x224 (expected input size)
+#         transforms.ToTensor(),  # Convert to Tensor
+#         transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),  # Normalize
+#     ])
+
+#     # Apply transformations
+#     image_tensor = transform_test(image).unsqueeze(0)  # Add batch dimension
+
+#     # Ensure tensor is on the same device as model
+#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#     model.to(device)
+#     image_tensor = image_tensor.to(device)
+
+#     # Run inference
+#     with torch.no_grad():
+#         output = model(image_tensor)
+#         _, pred = torch.max(output, 1)  # Get predicted class index
+
+#     # Map predicted class index to label
+#     predicted_label = class_labels[pred.item()]
+#     print(pred.item())  
+#     return f"Predicted Category: {predicted_label}"
+
+
+# # Create Gradio Interface
+# example = "https://huggingface.co/tfarhan10/Clothing1M-Pretrained-ResNet50/blob/main/content/drive/MyDrive/CS5930/download.jpeg"
+# interface = gr.Interface(
+#     fn=classify_image,
+#     inputs=gr.Image(type="pil"),  # Accept image input
+#     outputs="text",
+#     title="Clothing Image Classifier",
+#     description="Upload an image and the model will classify it into one of 14 clothing categories.",
+#     allow_flagging="never",  # Disable flagging feature
+#     examples = [[example]]
+# )
+
+# # Launch the app
+# if __name__ == "__main__":
+#     interface.launch()
+
+
+import torch
+import gradio as gr
+import torchvision.transforms as transforms
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import requests
+from io import BytesIO
+from C2D.models.resnet import SupCEResNet
+
+# Define class labels
+class_labels = [
+    "T-shirt", "Shirt", "Knitwear", "Chiffon", "Sweater",
+    "Hoodie", "Windbreaker", "Jacket", "Down Coat", "Suit",
+    "Shawl", "Dress", "Vest", "Underwear"
+]
+
+# Load model from Hugging Face Hub
+def load_model_from_huggingface(repo_id="tfarhan10/Clothing1M-Pretrained-ResNet50", filename="model.pth"):
+    try:
+        print("Downloading model from Hugging Face...")
+        checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
+        # Load checkpoint
+        checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'),weights_only=False)
+
+        # Extract state_dict if stored in a dictionary
+        if isinstance(checkpoint, dict) and "model" in checkpoint:
+            state_dict = checkpoint["model"]
+        else:
+            state_dict = checkpoint
+
+        # Fix "module." prefix issue
+        new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+
+        # Initialize model
+        model = SupCEResNet(name='resnet50', num_classes=14, pool=True)
+
+        # Load weights
+        model.load_state_dict(new_state_dict, strict=False)  # `strict=False` allows minor mismatches
+        model.eval()  # Set model to evaluation mode
+
+        print("✅ Model loaded successfully from Hugging Face!")
+        return model
+
+    except Exception as e:
+        print(f"❌ Error loading model: {e}")
+        return None
+
+# Load the model
+model = load_model_from_huggingface()
+
+def classify_image(image):
+    """Process and classify an uploaded PIL image accurately."""
+
+    # Ensure image is in RGB format
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    # Define preprocessing transformations (same as training)
+    transform_test = transforms.Compose([
+        transforms.Resize(256),  # Resize the shorter side to 256
+        transforms.CenterCrop(224),  # Center crop to 224x224 (expected input size)
+        transforms.ToTensor(),  # Convert to Tensor
+        transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),  # Normalize
+    ])
+
+    # Apply transformations
+    image_tensor = transform_test(image).unsqueeze(0)  # Add batch dimension
+
+    # Ensure tensor is on the same device as model
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    image_tensor = image_tensor.to(device)
+
+    # Run inference
+    with torch.no_grad():
+        output = model(image_tensor)
+        _, pred = torch.max(output, 1)  # Get predicted class index
+
+    # Map predicted class index to label
+    predicted_label = class_labels[pred.item()]
+    return f"Predicted Category: {predicted_label}"
+
+# Load example image from Hugging Face repository
+example_url = "https://huggingface.co/tfarhan10/Clothing1M-Pretrained-ResNet50/resolve/main/content/drive/MyDrive/CS5930/download.jpeg"
+
+def load_example_image():
+    """Download and return an example image from Hugging Face"""
+    try:
+        response = requests.get(example_url)
+        if response.status_code == 200:
+            return Image.open(BytesIO(response.content)).convert("RGB")
+        else:
+            print("⚠️ Failed to fetch example image.")
+            return None
+    except Exception as e:
+        print(f"⚠️ Error loading example image: {e}")
+        return None
+
+# Example image
+example_image = load_example_image()
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),  # Accept image input
+    outputs="text",
+    title="Clothing Image Classifier",
+    description="Upload an image or use the example below. The model will classify it into one of 14 clothing categories.",
+    allow_flagging="never",  # Disable flagging feature
+    examples=[[example_image]] if example_image else None  # Use example image if available
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()