import gradio as gr
import cv2
import requests
import os

from ultralytics import YOLO

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",
	cache_examples=False,
)

def predict_label(img_path	):

    results = model(img_path)
    return map[results[0].probs.data.argmax().item()]

model = YOLO('best.pt')
path  = [['image_0.jpg'], ['image_1.jpg']]