Update app.py

app.py

@@ -1,6 +1,8 @@
 import cv2 # opencv2 package for python.
 import torch
 from pytube import YouTube
+from ultralyticsplus import YOLO, render_result
+
 #from torch import hub # Hub contains other models like FasterRCNN
 
 URL = "https://www.youtube.com/watch?v=dQw4w9WgXcQ" #URL to parse
@@ -17,7 +19,27 @@ def load():
     stream = cv2.VideoCapture(vid)
     return vid_cap
 
-model = torch.hub.load('ultralytics/yolov5','yolov5s',pretrained=True)
+
+# load model
+model = YOLO('ultralyticsplus/yolov8s')
+
+# 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
+
+# set image
+image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
+
+# perform inference
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+'''
 
 """
 The function below identifies the device which is availabe to make the prediction and uses it to load and infer the frame. Once it has results it will extract the labels and cordinates(Along with scores) for each object detected in the frame.
@@ -84,3 +106,4 @@ def __call__(self):
         ret, frame = player.read() # Read next frame.
 
 
+'''