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
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"
]

# ✅ **Updated 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(image):
    """Processes the input image and returns the predicted clothing category."""
    print("\n[INFO] Received image for classification.")

    try:
        image = Image.fromarray(image)  # Ensure conversion to PIL format
        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**
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),
    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__":
    print("[INFO] Launching Gradio interface...")
    interface.launch()