Add Alphav1 application file

app.py

@@ -0,0 +1,33 @@
+# Are you wearing a mask?
+import gradio as gr
+import torch
+import torchvision
+import numpy as np
+from PIL import Image
+
+# Face masks
+model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/face_masks_full.pt")
+
+# Animals
+# model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/datasets_1000_41class.pt",force_reload=True)
+
+
+
+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 = "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>"
+
+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(cache_examples=True,enable_queue=True)