yolov8 accident detector

app.py

@@ -1,7 +1,9 @@
 import gradio as gr
-import torch
+# import torch
 from ultralyticsplus import YOLO, render_result
 
+classes: ['car', 'bike', 'person', 'car_car_accident', 'car_bike_accident', 'car_person_accident', 'bike_bike_accidnet', 'bike_person_accident', 'car_object_accident', 'bike_object_accident']
+
 def yolov8_func(image):
                 #image_size: gr.inputs.Slider = 640,
                 #conf_threshold: gr.inputs.Slider = 0.4,
@@ -18,12 +20,12 @@ def yolov8_func(image):
     box = results[0].boxes
 
     print("Object type: ", box.cls)
-    print("Coordinates: ", box.xyxy)
-    print("Probability: ", box.conf)
+    # print("Coordinates: ", box.xyxy)
+    # print("Probability: ", box.conf)
 
     render = render_result(model=model, image=image, result=results[0])
 
-    return render
+    return render, box.cls
 
 # inputs = [
 #     gr.inputs.Image(type="filepath", label="Input Image"),
@@ -51,6 +53,7 @@ with gr.Blocks(title="YOLOS Object Detection - ClassCat", css=".gradio-container
     with gr.Row():
         input_image = gr.Image(label="Input image", type="pil")
         output_image = gr.Image(label="Output image", type="pil")
+        output_label = gr.Text(label="output label")
     gr.HTML("<br>")
     #gr.HTML("<h4>object centre detection threshold means the object centre will be considered a new object if it's value is above threshold</h4>")
     #gr.HTML("<p>less means more objects</p>")
@@ -67,7 +70,7 @@ with gr.Blocks(title="YOLOS Object Detection - ClassCat", css=".gradio-container
     gr.HTML("<br>")
     #gr.Examples(['./samples/out_1.jpg'], inputs=input_image)
 
-    send_btn.click(fn=yolov8_func, inputs=[input_image], outputs=[output_image])
+    send_btn.click(fn=yolov8_func, inputs=[input_image], outputs=[output_image, output_label])
 
 
 demo.launch(debug=True)