#!/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.
    """
    # Load the pre-trained ResNet50 model from ImageNet
    model = models.resnet50(pretrained=False)

    # Load custom weights from a .pth file with CPU mapping
    state_dict = torch.load(model_path, map_location=torch.device('cpu'))

    # 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
    classes = models.ResNet50_Weights.IMAGENET1K_V2.meta["categories"]
    return classes


def main():
    """
    Main function for the application.
    """
    # 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
            """
        )

        # #############################################################################
        # ################################ GradCam Tab ################################
        # #############################################################################
        with gr.Tab("GradCam"):
            gr.Markdown(
                """
                Visualize Class Activations Maps generated by the model's layer for the predicted class.
                This is used to see what the model is actually looking at in the image.
                """
            )
            with gr.Row():
                img_input = [gr.Image(label="Input Image", type="numpy", height=224)]
                gradcam_outputs = [
                    gr.Label(label="Predictions"),
                    gr.Image(label="GradCAM Output", height=224)
                ]

            with gr.Row():
                gradcam_inputs = [
                    gr.Slider(0, 1, value=0.5, label="Activation Map Transparency"),
                    gr.Slider(1, 10, value=3, step=1, label="Number of Top Predictions"),
                    gr.Slider(1, 6, value=4, step=1, label="Target Layer Number")
                ]

            gradcam_button = gr.Button("Generate GradCAM")
            
            # Pass model to inference function using partial
            from functools import partial
            inference_fn = partial(inference, model=model, classes=classes)
            gradcam_button.click(inference_fn, inputs=img_input + gradcam_inputs, outputs=gradcam_outputs)

            gr.Markdown("## Examples")
            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 + gradcam_inputs,
                fn=inference_fn,
                outputs=gradcam_outputs
            )

        # Launch the demo (moved inside the Blocks context)
        demo.launch(debug=True)


if __name__ == "__main__":
    main()