Create app.py

app.py

@@ -0,0 +1,84 @@
+import gradio as gr
+import PIL.Image as Image
+
+from ultralytics import ASSETS, YOLO
+
+model_egg = YOLO("/content/drive/MyDrive/EPR_Detection/weights/egg_best.pt")
+model_nest = YOLO("/content/drive/MyDrive/EPR_Detection/weights/nest_best.pt")
+
+def predict_egg_image(img, conf_threshold, iou_threshold):
+    """Predicts objects in an image using a YOLO model with adjustable confidence and IOU thresholds."""
+    results = model_egg.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    return im
+
+
+def predict_nest_image(img, conf_threshold, iou_threshold):
+    """Predicts objects in an image using a YOLO model with adjustable confidence and IOU thresholds."""
+    results = model_nest.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    return i
+
+iface_egg = gr.Interface(
+    fn=predict_egg_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="EPR Egg Detection",
+    description="Upload images for egg detection inference.",
+    examples=[
+        ["/content/drive/MyDrive/EPR_Detection/Examples/Egg/egg1.jpg", 0.25, 0.45],
+        ["/content/drive/MyDrive/EPR_Detection/Examples/Egg/egg2.jpg", 0.25, 0.45],
+        ["/content/drive/MyDrive/EPR_Detection/Examples/Egg/egg3.jpg", 0.25, 0.45]
+    ],
+)
+
+iface_nest = gr.Interface(
+    fn=predict_nest_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="EPR Nest Detection",
+    description="Upload images for nest detection inference.",
+    examples=[
+        ["/content/drive/MyDrive/EPR_Detection/Examples/Nest/nest1.jpg", 0.25, 0.45],
+        ["/content/drive/MyDrive/EPR_Detection/Examples/Nest/nest2.jpg", 0.25, 0.45],
+        ["/content/drive/MyDrive/EPR_Detection/Examples/Nest/nest3.jpg", 0.25, 0.45]
+    ],
+)
+
+iface = gr.TabbedInterface(
+    [iface_egg, iface_nest],
+    ["EPR Egg", "EPR Nest"]
+)
+
+if __name__ == "__main__":
+    iface.launch()