Update .pre-commit-config.yaml (#2019)

* Update .pre-commit-config.yaml

* Update __init__.py

* Update .pre-commit-config.yaml

* Precommit updates

utils/flask_rest_api/example_request.py

@@ -7,13 +7,13 @@ import pprint
 
 import requests
 
-DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-IMAGE = "zidane.jpg"
+DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s'
+IMAGE = 'zidane.jpg'
 
 # Read image
-with open(IMAGE, "rb") as f:
+with open(IMAGE, 'rb') as f:
     image_data = f.read()
 
-response = requests.post(DETECTION_URL, files={"image": image_data}).json()
+response = requests.post(DETECTION_URL, files={'image': image_data}).json()
 
 pprint.pprint(response)

utils/flask_rest_api/restapi.py

@@ -13,36 +13,36 @@ from PIL import Image
 app = Flask(__name__)
 models = {}
 
-DETECTION_URL = "/v1/object-detection/<model>"
+DETECTION_URL = '/v1/object-detection/<model>'
 
 
-@app.route(DETECTION_URL, methods=["POST"])
+@app.route(DETECTION_URL, methods=['POST'])
 def predict(model):
-    if request.method != "POST":
+    if request.method != 'POST':
         return
 
-    if request.files.get("image"):
+    if request.files.get('image'):
         # Method 1
         # with request.files["image"] as f:
         #     im = Image.open(io.BytesIO(f.read()))
 
         # Method 2
-        im_file = request.files["image"]
+        im_file = request.files['image']
         im_bytes = im_file.read()
         im = Image.open(io.BytesIO(im_bytes))
 
         if model in models:
             results = models[model](im, size=640)  # reduce size=320 for faster inference
-            return results.pandas().xyxy[0].to_json(orient="records")
+            return results.pandas().xyxy[0].to_json(orient='records')
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
-    parser.add_argument("--port", default=5000, type=int, help="port number")
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model')
+    parser.add_argument('--port', default=5000, type=int, help='port number')
     parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
     opt = parser.parse_args()
 
     for m in opt.model:
-        models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True)
+        models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True)
 
-    app.run(host="0.0.0.0", port=opt.port)  # debug=True causes Restarting with stat
+    app.run(host='0.0.0.0', port=opt.port)  # debug=True causes Restarting with stat