Update app.py

app.py

@@ -1,102 +1,4 @@
-# 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