app.py

import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO

file_urls = [
    'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
    'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
]

def download_file(url, save_name):
    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('best.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 det in 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.Image(type="filepath", label="Input Image")
outputs_image = gr.Image(label="Output Image")

def show_preds_video(video_path):
    output_path = "processed_video.mp4"
    cap = cv2.VideoCapture(video_path)
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, cap.get(cv2.CAP_PROP_FPS),
                          (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        outputs = model.predict(source=frame)
        results = outputs[0].cpu().numpy()
        for det in results.boxes.xyxy:
            cv2.rectangle(
                frame,
                (int(det[0]), int(det[1])),
                (int(det[2]), int(det[3])),
                color=(0, 0, 255),
                thickness=2,
                lineType=cv2.LINE_AA
            )
        out.write(frame)

    cap.release()
    out.release()
    return output_path

inputs_video = gr.Video(label="Input Video")
outputs_video = gr.Video(label="Output Video")

interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Pothole Detector App (Image)",
    examples=path,
    cache_examples=False,
)

interface_video = gr.Interface(
    fn=show_preds_video,
    inputs=inputs_video,
    outputs=outputs_video,
    title="Pothole Detector App (Video)",
    examples=video_path,
    cache_examples=False,
)

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