app.py

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()