Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+from cvu.detector.yolov5 import Yolov5 as Yolov5Onnx
+
+# Load the model outside of the function so it's not reloaded every time the function is called
+model = Yolov5Onnx(classes="coco", backend="onnx", weight='./jtbz_opt.onnx', device='cpu')
+
+def detect_objects(image: np.ndarray):
+    # Convert the input image to OpenCV format
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+
+    # Perform the object detection
+    preds = model(image)
+
+    # Draw the predictions on the image
+    preds.draw(image)
+
+    # Convert the image back to PIL format and return it
+    return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+# Define the Gradio interface
+iface = gr.Interface(fn=detect_objects, 
+                     inputs=gr.inputs.Image(shape=(416, 416)), 
+                     outputs="image",
+                     title="YOLOv5 Object Detection",
+                     description="This is an interactive demo for YOLOv5 object detection. Upload an image to see the detected objects.")
+
+iface.launch()