app.py

import torch
import cv2
import numpy as np
import gradio as gr

# load model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image


def detect(img):

# perform inference
    results = model(img, size=640)

# inference with test time augmentation
    results = model(img, augment=True)
# parse results
    predictions = results.pred[0]
    boxes = predictions[:, :4] # x1, y1, x2, y2
    scores = predictions[:, 4]
    categories = predictions[:, 5]
    return results.show()
# show detection bounding boxes on image

img = gr.inputs.Image(shape=(192, 192))

intf = gr.Interface(fn=detect, inputs=img, outputs='image')
intf.launch(inline=False)