app.py

import tensorflow as tf
import cv2
import numpy as np
from glob import glob
# from models import Yolov4
import gradio as gr
# model = Yolov4(weight_path="best.pt", class_name_path='coco_classes.txt')


from ultralytics import YOLO

# Load a model
model = YOLO("best.pt")  # load a custom model

# Predict with the model
# results = model("image.jpg", save = True)  # predict on an image


def gradio_wrapper(img):
    global model
    #print(np.shape(img))
    results = model(img, save = True, show = True)  # predict on an image
    # print(model.names.get(box.cls.item()))
    
    # return cv2.putText(img, str(results[0]),(00, 185), cv2.FONT_HERSHEY_SIMPLEX, 1, 
    #               (0, 0, 255),  2, cv2.LINE_AA, False)
    return results
  
demo = gr.Interface(
    gradio_wrapper,
    #gr.Image(source="webcam", streaming=True, flip=True),
    gr.Image(source="webcam", streaming=True),
    "image",
    live=True
)

demo.launch()