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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()
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