Update app.py

app.py

@@ -25,9 +25,9 @@ def yolo(im, size=640):
 inputs = gr.inputs.Image(type='pil', label="Original Image")
 outputs = gr.outputs.Image(type="pil", label="Output Image")
 
-title = "Detecting masked and unmasked faces with YOLOv5"
-description = "YOLOv5 Gradio demo for finding faces with and without masks, using object detection. Upload an image or click an example image to use."
-article = "<p style='text-align: center'>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>"
+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/batman.png']]
 gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True)