import gradio as gr
import cv2
import requests
import os

import torch
import ultralytics
 
file_urls = [
    'https://www.dropbox.com/s/bc9r8n7919cbc77/test-image.jpg?dl=0',
    'https://www.dropbox.com/s/fkmzgdm6okdzxdk/test-image-2.jpg?dl=0',
]
 
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):
    download_file(
        file_urls[i],
        f"image_{i}.jpg"
    )

model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5_0.65map_exp7_best.pt",
                       force_reload=False) 

path  = [['image_0.jpg'], ['image_1.jpg']]

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="Pothole detector",
    examples=path,
    cache_examples=False,
)

interface_image.launch()