Create app.py

app.py

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+import gradio as gr
+import numpy as np
+
+from ultralyticsplus import YOLO, render_result
+import cv2
+from PIL import Image
+
+from cv2 import imshow
+from cv2 import imwrite
+
+#image=[gr.Image(label="Input Image", source="webcam")
+
+
+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()
+
+    return render
+
+
+#demo = gr.Interface(
+#    PPE, 
+#    gr.Image(source="webcam", streaming=True), 
+#    "image",
+#    live=True
+#)
+#demo.launch()
+
+
+
+#def snap(image):
+#    return np.flipud(image)
+
+
+#iface = gr.Interface(PPE, gr.inputs.Image(source="webcam", tool=None), "image")
+#iface.launch()
+
+
+#demo = gr.Interface(
+#    fn=PPE,
+#    inputs=gr.inputs.Image(source="webcam", tool=None),
+#    outputs="image",
+#)
+#demo.launch()
+
+demo = gr.Interface(
+    fn=PPE,
+    inputs="image",
+    outputs="image",
+)
+demo.launch()
+