from typing import Tuple, Union

import gradio as gr
import matplotlib.pyplot as plt
import torch
from PIL import Image

import bcos.models.pretrained as pretrained
from bcos.data.categories import IMAGENET_CATEGORIES

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def get_model(model_name):
    model = getattr(pretrained, model_name)(pretrained=True)
    model = model.to(device)
    model.eval()
    return model


MODEL_NAMES = pretrained.list_available()


class NormalizationMode:
    # this is normalization for the explanations!
    INDIVIDUAL = "individual"
    WRT_PREDICTION = "wrt prediction's confidence"
    INDIVIDUAL_X_CONFIDENCE = "individual×confidence"

    @classmethod
    def all(cls):
        return [cls.WRT_PREDICTION, cls.INDIVIDUAL_X_CONFIDENCE, cls.INDIVIDUAL]


def freeze(model):
    for param in model.parameters():
        param.requires_grad = False


def run(
    model_name: str,
    input_image: Image,
    do_resize: bool = True,
    do_center_crop: bool = False,
    normalization_mode: str = NormalizationMode.WRT_PREDICTION,
    smooth: int = 15,
    alpha_percentile: Union[int, float] = 99.99,
    plot_dpi: int = 120,
    topk: int = 3,
) -> Tuple[dict, plt.Figure]:
    # cleanup previous stuff
    plt.close("all")
    torch.cuda.empty_cache()

    # preprocess - get model and transform input image
    model = get_model(model_name)
    freeze(model)
    x = model.transform.transform_with_options(
        input_image,
        center_crop=do_center_crop,
        resize=do_resize,
    )
    x = x.unsqueeze(0).to(device).requires_grad_()

    # predict and explain
    with model.explanation_mode():
        out = model(x)

        topk_values, topk_preds = torch.topk(out, topk, dim=1)
        topk_values, topk_preds = topk_values[0], topk_preds[0]

        dynamic_weights = []  # list of grad tensors of shape (C, H, W)
        for i in range(topk):
            topk_values[i].backward(inputs=[x], retain_graph=i < topk - 1)
            dynamic_weights.append(
                x.grad.detach().cpu()[0],
            )
            x.grad = None  # reset

    # prepare output labels+confidences
    topk_probabilities = (
        model.to_probabilities(out.detach()).topk(topk, dim=1).values[0].cpu()
    )
    confidences = {
        IMAGENET_CATEGORIES[i]: v.item() for i, v in zip(topk_preds, topk_probabilities)
    }

    # output plot of images
    output_fig, axs = plt.subplots(
        1, topk + 1, dpi=plot_dpi, figsize=((topk + 1) * 2.1, 2)
    )

    # visualize input image
    x = x.detach().cpu()[0]
    axs[0].imshow(x[:3].permute(1, 2, 0).numpy())
    axs[0].set_xlabel("Input Image")

    # visualize explanations
    pred_confidence = topk_probabilities[0]  # first one is pred
    for i, ax in enumerate(axs[1:]):
        expl = model.gradient_to_image(
            x,
            dynamic_weights[i],
            smooth=smooth,
            alpha_percentile=alpha_percentile,
        )

        if normalization_mode == NormalizationMode.INDIVIDUAL_X_CONFIDENCE:
            expl[:, :, -1] *= topk_probabilities[i].item()
        elif normalization_mode == NormalizationMode.WRT_PREDICTION and i > 0:
            expl[:, :, -1] *= (topk_probabilities[i] / pred_confidence).item()
        else:  # NormalizationMode.INDIVIDUAL
            pass

        ax.imshow(expl)
        ax.set_xlabel(IMAGENET_CATEGORIES[topk_preds[i]])

    for ax in axs:
        ax.set_xticks([])
        ax.set_yticks([])

    output_fig.tight_layout()

    return confidences, output_fig


with gr.Blocks() as demo:
    # basic info
    gr.Markdown(
        """# B-cos Explanation Generation Demo
        This demo generates explanations for images using the B-cos models.
        
        GitHub: [link](https://github.com/B-cos/B-cos-v2/)
        
        ***Scroll to the end to try out some example images!***
        """
    )

    with gr.Row():
        selected_model = gr.Dropdown(
            MODEL_NAMES, value="densenet121_long", label="Select model"
        )

        with gr.Accordion("Options", open=False):
            do_resize = gr.Checkbox(
                label="Resize input image's shorter side to 256", value=True
            )
            do_center_crop = gr.Checkbox(
                label="Center crop input image to 224x224", value=False
            )
            normalization_mode = gr.Radio(
                NormalizationMode.all(),
                value=NormalizationMode.WRT_PREDICTION,
                label="Explanation Normalization Mode",
            )

            smooth = gr.Slider(1, 51, value=15, step=2, label="Smoothing kernel size")
            alpha_percentile = gr.Number(value=99.99, label="Percentile")
            plot_dpi = gr.Number(value=100, label="Plot DPI")

    input_image = gr.Image(type="pil", label="Image")
    run_button = gr.Button("Predict and Explain", variant="primary")

    # will contain all outputs in a plot
    output = gr.Plot(label="Explanations")
    # labels
    output_labels = gr.Label(label="Top-5 Predictions")

    run_button.click(
        fn=run,
        inputs=[
            selected_model,
            input_image,
            do_resize,
            do_center_crop,
            normalization_mode,
            smooth,
            alpha_percentile,
            plot_dpi,
        ],
        outputs=[output_labels, output],
        scroll_to_output=True,
    )

    gr.Examples(
        fn=run,
        examples=[
            [
                "resnet50",
                "./examples/polizeifahrzeug-zebra.png",
                True,
                False,
                NormalizationMode.WRT_PREDICTION,
                15,
                99.99,
                120,
            ],
            [
                "resnet50",
                "./examples/cat-dog.png",
                True,
                False,
                NormalizationMode.INDIVIDUAL,
                15,
                99.99,
                120,
            ]
        ],
        inputs=[
            selected_model,
            input_image,
            do_resize,
            do_center_crop,
            normalization_mode,
            smooth,
            alpha_percentile,
            plot_dpi,
        ],
        outputs=[output_labels, output],
        cache_examples=True,
    )


demo.launch()