import gradio as gr
from typing import List
import cv2
import torch
from torchvision import transforms
import numpy as np
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image
import io
from models import YoloV3Lightning
from utils import load_model_from_checkpoint
import utils
import config as cfg
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from dataset import YOLODataset
from torch.utils.data import Dataset, DataLoader
from grad_cam import YoloGradCAM

device = torch.device('cpu')
dataset_mean, dataset_std = (0.4914, 0.4822, 0.4465), \
                            (0.2470, 0.2435, 0.2616)
model = YoloV3Lightning.YOLOv3LightningModel(num_classes=cfg.NUM_CLASSES, anchors=cfg.ANCHORS, S=cfg.S)
ckpt_file = 'ckpt_light.pth'
checkpoint = load_model_from_checkpoint(device, file_name=ckpt_file)
model.load_state_dict(checkpoint['model'], strict=False)

model.eval()

scaled_anchors = (
        torch.tensor(cfg.ANCHORS)
        * torch.tensor(cfg.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
).to(model.device)

cam = YoloGradCAM(model=model, target_layers=[model.layers[-2]], scaled_anchors=scaled_anchors, use_cuda=False)
'''cfg.IMG_DIR = cfg.DATASET + "/images/"
cfg.LABEL_DIR = cfg.DATASET + "/labels/"
eval_dataset = YOLODataset(
    cfg.DATASET + "/25examples.csv",
    transform=cfg.test_transforms,
    S=[cfg.IMAGE_SIZE // 32, cfg.IMAGE_SIZE // 16, cfg.IMAGE_SIZE // 8],
    img_dir=cfg.IMG_DIR,
    label_dir=cfg.LABEL_DIR,
    anchors=cfg.ANCHORS,
    mosaic=False
)
eval_loader = DataLoader(
    dataset=eval_dataset,
    batch_size=cfg.BATCH_SIZE,
    num_workers=cfg.NUM_WORKERS,
    pin_memory=cfg.PIN_MEMORY,
    shuffle=True,
    drop_last=False,
)

scaled_anchors = (
        torch.tensor(cfg.ANCHORS)
        * torch.tensor(cfg.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
)
scaled_anchors = scaled_anchors.to(cfg.DEVICE)

utils.plot_examples(model, eval_loader, 0.5, 0.6, scaled_anchors)'''

sample_images = [
    ['images/000001.jpg'],
    ['images/000002.jpg'],
    ['images/000003.jpg'],
    ['images/000004.jpg'],
    ['images/000005.jpg'],
    ['images/000006.jpg'],
    ['images/000007.jpg'],
    ['images/000008.jpg'],
    ['images/000009.jpg'],
    ['images/000010.jpg'],
    ['images/000011.jpg'],
    ['images/000012.jpg'],
    ['images/000013.jpg'],
    ['images/000014.jpg'],
    ['images/000015.jpg'],
    ['images/000016.jpg'],
    ['images/000017.jpg'],
    ['images/000018.jpg'],
    ['images/000019.jpg'],
    ['images/000020.jpg'],
    ['images/000021.jpg'],
    ['images/000022.jpg'],
    ['images/000023.jpg'],
    ['images/000024.jpg'],
    ['images/000025.jpg']
]

with gr.Blocks() as app:
    with gr.Row():

        gr.Markdown(
            """
            # YoloV3 App!
            ## Model is trained on PASCAL-VOC data to predict following classes -
            """)

    with gr.Row():
        gr.HTML(
            """
            <table>
                <tr>
                    <th>aeroplane</th>
                    <th>bicycle</th>
                    <th>bird</th>
                    <th>boat</th>
                    <th>bottle</th>
                    <th>bus</th>
                    <th>car</th>
                    <th>cat</th>
                </tr>
                <tr>
                    <th>chair</th>
                    <th>cow</th>
                    <th>diningtable</th>
                    <th>dog</th>
                    <th>horse</th>
                    <th>motorbike</th>
                    <th>person</th>
                    <th>pottedplant</th>
                </tr>
                <tr>
                    <th>sheep</th>
                    <th>sofa</th>
                    <th>train</th>
                    <th>tvmonitor</th>
                </tr>

            </table>
            <p>
                <a href='https://github.com/piygr/yolov3/blob/main/models/YoloV3Lightning.py'>Click to see the model architecture / code </a>

            </p>
            """
        )
    with gr.Row(visible=True) as top_pred_cls_col:
        with gr.Column():
            example_images = gr.Gallery(allow_preview=False, label='Select image ', info='',
                                        value=[img[0] for img in sample_images], columns=5, rows=2)

        with gr.Column():
            top_pred_image = gr.Image(label='Upload Image or Select from the gallery')

            with gr.Row():
                top_class_btn = gr.Button("Submit", variant='primary')
                tc_clear_btn = gr.ClearButton()

    with gr.Row():
        if_show_grad_cam = gr.Checkbox(value=True, label='Show Class Activation Map (What the model sees)?')

    # with gr.Row(visible=True) as top_class_output:
    with gr.Row(visible=True)  as top_class_output:
        top_class_output_img = gr.Image(interactive=False, label='Prediction Output')
    with gr.Row(visible=True)  as top_class_output:
        grad_cam_out = gr.Image(interactive=False, visible=True, label='CAM Outcome')


    def show_cam_output(input):
        return {
            grad_cam_out: gr.update(visible=input)
        }


    if_show_grad_cam.change(
        show_cam_output,
        if_show_grad_cam,
        grad_cam_out
    )


    def clear_data():
        return {
            top_pred_image: None,
            top_class_output_img: None
        }


    tc_clear_btn.click(clear_data, None, [top_pred_image, top_class_output_img])


    def on_select(evt: gr.SelectData):
        return {
            top_pred_image: sample_images[evt.index][0]
        }


    example_images.select(on_select, None, top_pred_image)


    def plot_image(image, boxes):
        """Plots predicted bounding boxes on the image"""
        cmap = plt.get_cmap("tab20b")
        class_labels = cfg.PASCAL_CLASSES
        colors = [cmap(i) for i in np.linspace(0, 1, len(class_labels))]
        im = np.array(image)
        height, width, _ = im.shape

        # Create figure and axes
        fig, ax = plt.subplots(1)
        # Display the image
        ax.imshow(im)

        # box[0] is x midpoint, box[2] is width
        # box[1] is y midpoint, box[3] is height

        # Create a Rectangle patch
        for box in boxes:
            assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height"
            class_pred = box[0]
            box = box[2:]
            upper_left_x = box[0] - box[2] / 2
            upper_left_y = box[1] - box[3] / 2
            rect = patches.Rectangle(
                (upper_left_x * width, upper_left_y * height),
                box[2] * width,
                box[3] * height,
                linewidth=2,
                edgecolor=colors[int(class_pred)],
                facecolor="none",
            )
            # Add the patch to the Axes
            ax.add_patch(rect)
            plt.text(
                upper_left_x * width,
                upper_left_y * height,
                s=class_labels[int(class_pred)],
                color="white",
                verticalalignment="top",
                bbox={"color": colors[int(class_pred)], "pad": 0},
            )

        plt.savefig('output.png')
        x = plt.show()


    def predict(image: np.ndarray, iou_thresh: float = 0.5, thresh: float = 0.4, show_cam: bool = False,
                transparency: float = 0.5) -> List[np.ndarray]:
        with torch.no_grad():
            transformed_image = cfg.grad_cam_transforms(image=image)["image"].unsqueeze(0)
            output = model(transformed_image)

            bboxes = [[] for _ in range(1)]
            for i in range(3):
                batch_size, A, S, _, _ = output[i].shape
                anchor = scaled_anchors[i]
                boxes_scale_i = utils.cells_to_bboxes(
                    output[i], anchor, S=S, is_preds=True
                )
                for idx, (box) in enumerate(boxes_scale_i):
                    bboxes[idx] += box

        nms_boxes = utils.non_max_suppression(
            bboxes[0], iou_threshold=iou_thresh, threshold=thresh, box_format="midpoint",
        )

        plot_image(image, nms_boxes)
        plotted_img = 'output.png'
        if not show_cam:
            return [plotted_img, None]

        grayscale_cam = cam(transformed_image)[0, :, :]
        img = cv2.resize(image, (416, 416))
        img = np.float32(img) / 255
        cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True, image_weight=transparency)
        return [plotted_img, cam_image]


    def top_class_img_upload(input_img, if_cam):
        if input_img is not None:
            imgs = predict(input_img, show_cam=if_cam)

            return {
                top_class_output_img: imgs[0],
                grad_cam_out: imgs[1]
            }


    top_class_btn.click(
        top_class_img_upload,
        [top_pred_image, if_show_grad_cam],
        [top_class_output_img, grad_cam_out]
    )

'''
Launch the app
'''
app.launch()