Update app.py

app.py

@@ -1,91 +1,13 @@
-from ultralyticsplus import YOLO, render_result
-import cv2
-from PIL import Image
-
-from cv2 import imshow
-from cv2 import imwrite
-
-def PPE(image):
-    # load model
-    #model = YOLO('keremberke/yolov8m-protective-equipment-detection')
-    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
-
-
-
-    # perform inference
-    results = model.predict(image)
-
-    # observe results
-    print(results[0].boxes)
-    render = render_result(model=model, image=image, result=results[0])
-    render.show()
-
-def take_photo(image='photo.jpg', quality=0.8):
-
-    # program to capture single image from webcam in python
-  
-    # importing OpenCV library
-    from cv2 import imshow
-    from cv2 import imwrite
-  
-    # initialize the camera
-    # If you have multiple camera connected with 
-    # current device, assign a value in cam_port 
-    # variable according to that
-    cam_port = 0
-    cam = cv2.VideoCapture(cam_port)
-  
-    # reading the input using the camera
-    result, image = cam.read()
-  
-    # If image will detected without any error, 
-    # show result
-    if result:
-  
-    # showing result, it take frame name and image 
-    # output
-        imshow("photo.jpg", image)
-  
-    # saving image in local storage
-        imwrite("photo.jpg", image)
-  
-    # If keyboard interrupt occurs, destroy image 
-    # window
-        cv2.waitKey(0)
-        cv2.destroyAllWindows()
-#        cv2.destroyWindow("photo.jpg")
-
-  
-# If captured image is corrupted, moving to else part
-    else:
-        print("No image detected. Please! try again")
-       
-        
-  #with open(filename, 'wb') as f:
-  #  f.write(binary)
-    return image
-
-
-#take_photo()
-
-
-from PIL import Image
-
-#read the image
-image = Image.open("photo.jpg")
-
-
-
-
-
-
 import gradio as gr
-
-demo = gr.Interface(fn=take_photo, inputs=image, outputs=PPE(), description="Safety Helmet Detection")
-
-demo.launch(share=True)
+import numpy as np
+
+def flip(im):
+    return np.flipud(im)
+
+demo = gr.Interface(
+    flip, 
+    gr.Image(source="webcam", streaming=True), 
+    "image",
+    live=True
+)
+demo.launch()