import torch
import albumentations as A
import cv2

from albumentations.pytorch import ToTensorV2
import numpy as np
import config
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from model import YOLOv3
import gradio as gr
import os
import matplotlib.pyplot as plt
from utils import *

# checkpoint_filename = "object_detection_app/custom_yolo_v3.pt"
# relative_path = os.path.join(os.getcwd(),"hugging_space_app", checkpoint_filename)

model = YOLOv3(num_classes=config.NUM_CLASSES).to(config.DEVICE)
model.load_state_dict(torch.load("custom_yolo_v3.pt", map_location=torch.device('cpu')), strict=False)

IMAGE_SIZE = config.IMAGE_SIZE
test_transforms = A.Compose(
    [
        A.LongestMaxSize(max_size=IMAGE_SIZE),
        A.PadIfNeeded(
            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
        ),
        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
        ToTensorV2(),
    ])

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

def inference(input_img, transparency = 0.5, thresh=0.8, iou_thresh=0.3):

    x = test_transforms(image=input_img)['image'].unsqueeze(0).to(config.DEVICE)
    with torch.no_grad():
        out = model(x)
        bboxes = [[] for _ in range(x.shape[0])]
        for i in range(3):
            batch_size, A, S, _, _ = out[i].shape
            anchor = anchors[i]
            boxes_scale_i = cells_to_bboxes(
                out[i], anchor, S=S, is_preds=True
            )
            for idx, (box) in enumerate(boxes_scale_i):
                bboxes[idx] += box

        model.train()

    nms_boxes = non_max_suppression(
        bboxes[0], iou_threshold=iou_thresh, threshold=thresh, box_format="midpoint",
    )
    visualization = plot_image(x[0].permute(1,2,0).detach().cpu(), nms_boxes)

    return visualization

title = "Object Detection using YOLOv3"
description = "A simple Gradio interface to show object detection on images"
examples = [['./images/006294.jpg'],
 ['./images/005898.jpg'],
 ['./images/003785.jpg'],
 ['./images/001624.jpg'],
 ['./images/006796.jpg'],
 ['./images/003388.jpg'],
 ['./images/002216.jpg'],
 ['./images/000341.jpg'],
 ['./images/006818.jpg'],
 ]
 

demo = gr.Interface(
    inference, 
    inputs = [gr.Image(label="Input Image", type='numpy'), 
              gr.Slider(0, 1, value = 0.75, label="Threshold"), 
              gr.Slider(0, 1, value = 0.75, label="IoU Threshold"), 
              gr.Slider(0, 1, value = 0.8, label="Opacity of GradCAM")],
    outputs = [gr.Image(width=600, height=600,label="Output")],
    title = title,
    description = description,
    examples = examples,
) 
demo.launch()