#!/usr/bin/env python
"""
Application for ResNet50 trained on ImageNet-1K.
"""
# Standard Library Imports
import gradio as gr

# Third Party Imports
import torch
from torchvision import models

# Local Imports
from inference import inference


def load_model(model_path: str):
    """
    Load the model.
    """
    # Check if CUDA is available and set device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Using device: {device}")
    
    # Load the pre-trained ResNet50 model
    model = models.resnet50(weights=None)
    model = model.to(device)

    # Load custom weights from a .pth file
    state_dict = torch.load(model_path, map_location=device)

    # Filter out unexpected keys
    filtered_state_dict = {k: v for k, v in state_dict['model_state_dict'].items() if k in model.state_dict()}

    # Load the filtered state dictionary into the model
    model.load_state_dict(filtered_state_dict, strict=False)
    model.eval()
    return model


def load_classes():
    """
    Load the classes.
    """
    # Get ImageNet class names from ResNet50 weights
    weights = models.ResNet50_Weights.IMAGENET1K_V2
    classes = weights.meta["categories"]
    return classes


def inference_wrapper(image, alpha, top_k, target_layer):
    """
    Wrapper function for inference with error handling
    """
    try:
        if image is None:
            return {"No image provided": 1.0}, None
            
        results = inference(
            image, 
            alpha, 
            top_k, 
            target_layer, 
            model=model, 
            classes=classes
        )
        
        if results is None:
            return {"Processing failed": 1.0}, None
            
        return results
        
    except Exception as e:
        error_msg = str(e)
        print(f"Error in inference: {error_msg}")
        
        # Handle GPU quota error specifically
        if "GPU quota" in error_msg:
            return {"GPU quota exceeded - Please try again later": 1.0}, None
        
        # Handle other errors
        return {"Error: " + error_msg: 1.0}, None


def main():
    """
    Main function for the application.
    """
    global model, classes
    
    try:
        print(f"Gradio version: {gr.__version__}")
        
        # Load the model at startup
        model = load_model("resnet50_imagenet1k.pth")
        
        # Load the classes at startup
        classes = load_classes()

        with gr.Blocks() as demo:
            gr.Markdown(
                """
                # ImageNet-1K trained on ResNet50v2
                """
            )

            with gr.Tab("GradCam"):
                gr.Markdown(
                    """
                    Visualize Class Activations Maps generated by the model's layer for the predicted class.
                    """
                )
                
                # Define inputs
                with gr.Row():
                    img_input = gr.Image(
                        label="Input Image",
                        type="numpy",
                        height=224,
                        width=224
                    )
                    with gr.Column():
                        label_output = gr.Label(label="Predictions")
                        gradcam_output = gr.Image(
                            label="GradCAM Output",
                            height=224,
                            width=224
                        )

                with gr.Row():
                    alpha_slider = gr.Slider(
                        minimum=0,
                        maximum=1,
                        value=0.5,
                        step=0.1,
                        label="Activation Map Transparency"
                    )
                    top_k_slider = gr.Slider(
                        minimum=1,
                        maximum=10,
                        value=3,
                        step=1,
                        label="Number of Top Predictions"
                    )
                    target_layer_slider = gr.Slider(
                        minimum=1,
                        maximum=6,
                        value=4,
                        step=1,
                        label="Target Layer Number"
                    )

                gradcam_button = gr.Button("Generate GradCAM")
                
                # Set up the click event
                gradcam_button.click(
                    fn=inference_wrapper,
                    inputs=[
                        img_input,
                        alpha_slider,
                        top_k_slider,
                        target_layer_slider
                    ],
                    outputs=[
                        label_output,
                        gradcam_output
                    ]
                )

                # Example section
                gr.Examples(
                    examples=[
                        ["assets/examples/dog.jpg", 0.5, 3, 4],
                        ["assets/examples/cat.jpg", 0.5, 3, 4],
                        ["assets/examples/frog.jpg", 0.5, 3, 4],
                        ["assets/examples/bird.jpg", 0.5, 3, 4],
                        ["assets/examples/shark-plane.jpg", 0.5, 3, 4],
                        ["assets/examples/car.jpg", 0.5, 3, 4],
                        ["assets/examples/truck.jpg", 0.5, 3, 4],
                        ["assets/examples/horse.jpg", 0.5, 3, 4],
                        ["assets/examples/plane.jpg", 0.5, 3, 4],
                        ["assets/examples/ship.png", 0.5, 3, 4]
                    ],
                    inputs=[
                        img_input,
                        alpha_slider,
                        top_k_slider,
                        target_layer_slider
                    ],
                    outputs=[
                        label_output,
                        gradcam_output
                    ],
                    fn=inference_wrapper,
                    cache_examples=True,
                    label="Click on any example to run GradCAM"
                )

            # Configure queue
            demo.queue(concurrency_count=1)  # Limit concurrent processing

            # Launch with compatible parameters
            demo.launch(
                server_name="0.0.0.0",
                server_port=7860,
                share=False
            )
            
    except Exception as e:
        print(f"Error during startup: {str(e)}")
        if torch.cuda.is_available():
            torch.cuda.empty_cache()


if __name__ == "__main__":
    main()