Create app.py

app.py

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+import torch
+import cv2
+
+from ultralyticsplus import YOLO, render_result
+
+# load model
+model = YOLO('keremberke/yolov8m-hard-hat-detection')
+
+# set model parameters
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45  # NMS IoU threshold
+model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+
+
+from IPython.display import display, Javascript
+from google.colab.output import eval_js
+from base64 import b64decode
+
+def take_photo(filename='photo.jpg', quality=0.8):
+  js = Javascript('''
+    async function takePhoto(quality) {
+      const div = document.createElement('div');
+      const capture = document.createElement('button');
+      capture.textContent = 'Capture';
+      div.appendChild(capture);
+
+      const video = document.createElement('video');
+      video.style.display = 'block';
+      const stream = await navigator.mediaDevices.getUserMedia({video: true});
+
+      document.body.appendChild(div);
+      div.appendChild(video);
+      video.srcObject = stream;
+      await video.play();
+
+      // Resize the output to fit the video element.
+      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);
+
+      // Wait for Capture to be clicked.
+      await new Promise((resolve) => capture.onclick = resolve);
+
+      const canvas = document.createElement('canvas');
+      canvas.width = video.videoWidth;
+      canvas.height = video.videoHeight;
+      canvas.getContext('2d').drawImage(video, 0, 0);
+      stream.getVideoTracks()[0].stop();
+      div.remove();
+      return canvas.toDataURL('image/jpeg', quality);
+    }
+    ''')
+  display(js)
+  data = eval_js('takePhoto({})'.format(quality))
+  binary = b64decode(data.split(',')[1])
+  with open(filename, 'wb') as f:
+    f.write(binary)
+  return filename
+
+
+
+from ultralyticsplus import YOLO, render_result
+
+def safety_helmet():
+
+# perform inference
+#results = model.predict(image_path)
+    results = model.predict(filename)
+
+#    self.assertEqual(array.shape, (1, 224, 224, 3))
+
+# observe results
+    print(results[0].boxes)
+    render = render_result(model=model, image=filename, result=results[0])
+    render.show()
+
+
+import gradio as gr
+
+demo = gr.Interface(fn=safety_helmet, inputs=take_photo(), outputs=safety_helmet(), description="Safety Helmet Detection")
+
+demo.launch(share=True)