app.py

import gradio as gr
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
import os
from ResNet_for_CC import CC_model  # Import the 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"
model = CC_model(num_classes1=14)

# Load model weights
state_dict = torch.load(model_path, map_location=device)
model.load_state_dict(state_dict, strict=False)
model.to(device)
model.eval()

# Clothing1M Class Labels
class_labels = [
    "T-Shirt", "Shirt", "Knitwear", "Chiffon", "Sweater", "Hoodie",
    "Windbreaker", "Jacket", "Downcoat", "Suit", "Shawl", "Dress",
    "Vest", "Underwear"
]

# **Predefined Default Images**
default_images = {
    "Shawl": "tshirt.jpg",
    "Jacket": "jacket.jpg",
    "Sweater": "sweater.webp",
    "Vest": "dress.jpg"
}

# **Image Preprocessing Function**
def preprocess_image(image):
    """Applies necessary transformations to the input image."""
    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])
    ])
    return transform(image).unsqueeze(0).to(device)

# **Classification Function**
def classify_image(selected_default, uploaded_image):
    """Processes either a default or uploaded image and returns the predicted clothing category."""

    print("\n[INFO] Image selection process started.")
    
    try:
        # Use the uploaded image if provided; otherwise, use the selected default image
        if uploaded_image is not None:
            print("[INFO] Using uploaded image.")
            image = Image.fromarray(uploaded_image)  # Ensure conversion to PIL format
        else:
            print(f"[INFO] Using default image: {selected_default}")
            image_path = default_images[selected_default]
            image = Image.open(image_path)  # Load the selected default image

        image = preprocess_image(image)  # Apply transformations
        print("[INFO] Image transformed and moved to device.")

        with torch.no_grad():
            output = model(image)

            # Ensure output is a tensor (handle tuple case)
            if isinstance(output, tuple):
                output = output[1]  # Extract the actual output tensor

            print(f"[DEBUG] Model output shape: {output.shape}")
            print(f"[DEBUG] Model output values: {output}")

            if output.shape[1] != 14:
                return f"[ERROR] Model output mismatch! Expected 14 but got {output.shape[1]}."

            # Convert logits to probabilities
            probabilities = F.softmax(output, dim=1)
            print(f"[DEBUG] Softmax probabilities: {probabilities}")

            # Get predicted class index
            predicted_class = torch.argmax(probabilities, dim=1).item()
            print(f"[INFO] Predicted class index: {predicted_class} (Class: {class_labels[predicted_class]})")

            # Validate and return the prediction
            if 0 <= predicted_class < len(class_labels):
                predicted_label = class_labels[predicted_class]
                confidence = probabilities[0][predicted_class].item() * 100
                return f"Predicted Class: {predicted_label} (Confidence: {confidence:.2f}%)"
            else:
                return "[ERROR] Model returned an invalid class index."

    except Exception as e:
        print(f"[ERROR] Exception during classification: {e}")
        return "Error in classification. Check console for details."

# **Gradio Interface**
with gr.Blocks() as interface:
    gr.Markdown("# Clothing1M Image Classifier")
    gr.Markdown("Upload a clothing image or select from the predefined images below.")

    # Default Image Selection
    default_selector = gr.Radio(
        choices=list(default_images.keys()),
        label="Select a Default Image",
        value="T-Shirt"
    )

    # File Upload Option
    image_upload = gr.Image(type="numpy", label="Or Upload Your Own Image")

    # Output Text
    output_text = gr.Textbox(label="Classification Result")

    # Classify Button
    classify_button = gr.Button("Classify Image")

    # Define Action
    classify_button.click(
        fn=classify_image,
        inputs=[default_selector, image_upload],
        outputs=output_text
    )

# **Run the Interface**
if __name__ == "__main__":
    print("[INFO] Launching Gradio interface...")
    interface.launch()