app.py

# Are you wearing a mask?
import gradio as gr
import torch
import torchvision
import numpy as np
from PIL import Image

# Face masks
# TODO: Allow user selectable model?
model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/face_masks_v8.pt")




def yolo(im, size=640):
    g = (size / max(im.size))  # gain
    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize

    results = model(im)  # inference
    results.render()  # updates results.imgs with boxes and labels
    return Image.fromarray(results.imgs[0])


inputs = gr.inputs.Image(type='pil', label="Original Image")
outputs = gr.outputs.Image(type="pil", label="Output Image")

title = "Are you wearing a mask?"
description = "Detecting masked and unmasked faces with YOLOv5. Take a picture, upload an image, or click an example image to use."
article = "<p style='text-align: center'>This app makes predictions using a YOLOv5s model that was fine tuned on a dataset of people with and without masks. All of the code  for training the model is available on <a href='https://github.com/hlydecker/are-you-wearing-a-mask'>GitHub</a>. This app and the model behind it were created by Henry Lydecker, as part of his work at the Sydney Informatics Hub, a Core Research Facility of The University of Sydney. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. Here's the Ultralytics YOLOv5 blurb: YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"

examples = [['data/picard.jpg'], ['data/stockmasks.jpg'],['data/bts.png']]
gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True)