import gradio as gr
import cv2
import requests
import os

from ultralytics import YOLO

file_urls = [
    'https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.researchgate.net%2Ffigure%2Fray-of-baggage-showing-different-objects-with-different-densities-III-ALGORITHEM_fig2_305264591&psig=AOvVaw1s-qOqWplhsbjliHEq4bqo&ust=1699106526616000&source=images&cd=vfe&opi=89978449&ved=0CBIQjRxqFwoTCNCBs5n_p4IDFQAAAAAdAAAAABAJ',
    'https://www.google.com/url?sa=i&url=https%3A%2F%2Fbitrefine.group%2Faboutcompany%2Fnews%2F251-computer-system-has-learned-to-recognize-x-ray-images-and-alerts-if-it-sees-illegal-items&psig=AOvVaw1s-qOqWplhsbjliHEq4bqo&ust=1699106526616000&source=images&cd=vfe&opi=89978449&ved=0CBIQjRxqFwoTCNCBs5n_p4IDFQAAAAAdAAAAABAR'
]

def download_file(url, save_name):
    url = url
    if not os.path.exists(save_name):
        file = requests.get(url)
        open(save_name, 'wb').write(file.content)

for i, url in enumerate(file_urls):
    if 'mp4' in file_urls[i]:
        download_file(
            file_urls[i],
            f"video.mp4"
        )
    else:
        download_file(
            file_urls[i],
            f"image_{i}.jpg"
        )

model = YOLO('airport_scaner.pt')
path  = [['image_0.jpg'], ['image_1.jpg']]
# video_path = [['video.mp4']]

def show_preds_image(image_path):
    image = cv2.imread(image_path)
    outputs = model.predict(source=image_path)
    results = outputs[0].cpu().numpy()
    for i, det in enumerate(results.boxes.xyxy):
        cv2.rectangle(
            image,
            (int(det[0]), int(det[1])),
            (int(det[2]), int(det[3])),
            color=(0, 0, 255),
            thickness=2,
            lineType=cv2.LINE_AA
        )
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Airport Luggage Weapon Detector app",
    examples=path,
    cache_examples=False,
)

def show_preds_video(video_path):
    cap = cv2.VideoCapture(video_path)
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            frame_copy = frame.copy()
            outputs = model.predict(source=frame)
            results = outputs[0].cpu().numpy()
            for i, det in enumerate(results.boxes.xyxy):
                cv2.rectangle(
                    frame_copy,
                    (int(det[0]), int(det[1])),
                    (int(det[2]), int(det[3])),
                    color=(0, 0, 255),
                    thickness=2,
                    lineType=cv2.LINE_AA
                )
            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)

inputs_video = [
    gr.components.Video(),

]
outputs_video = [
    gr.components.Image(),
]
interface_video = gr.Interface(
    fn=show_preds_video,
    inputs=inputs_video,
    outputs=outputs_video,
    title="Airport Luggage Weapon Detector",
    cache_examples=False,
)

gr.TabbedInterface(
    [interface_image, interface_video],
    tab_names=['Image inference', 'Video inference']
).queue().launch()