app.py

import gradio as gr
import cv2
import requests
import os

from ultralytics import YOLO

file_urls = [
    'https://lh3.googleusercontent.com/a2iyhpYl4Jgzc0r7MYgXQI1BGwkutp3rKuauNpkEbD3Z_HP-gf29M-wugKebKJQdl8ILtKWN-vOZAS9r1qMsI88=w16383'    
]

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"
        )


path  = [['image_0.jpg']]


def show_preds_image(image_path):
    image = cv2.imread(image_path,0)    
    return image #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="Computer Vision and Deep Learning by Farshid PirahanSiah",
    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(type="filepath", label="Input Video"),

# ]
# outputs_video = [
#     gr.components.Image(type="numpy", label="Output Image"),
# ]
# interface_video = gr.Interface(
#     fn=show_preds_video,
#     inputs=inputs_video,
#     outputs=outputs_video,
#     title="Pothole detector",
#     examples=video_path,
#     cache_examples=False,
# )

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