diff --git a/gpt.py b/gpt.py
new file mode 100644
index 0000000..eaa9091
--- /dev/null
+++ b/gpt.py
@@ -0,0 +1,73 @@
+import pyvista as pv
+import vtk
+from vtk.util import numpy_support
+import numpy as np
+import time
+import kitty
+import asyncio
+from PIL import Image
+from io import BytesIO
+
+
+async def render_to(image_array):
+    """
+    Dummy rendering function that receives the rendered image as a numpy array.
+    Replace this with your actual rendering/display logic.
+    """
+    # print(f"Received image with shape: {image_array.shape} and dtype: {image_array.dtype}")
+    y, x = await kitty.get_position()
+    img = Image.fromarray(image_array)
+    buffer: BytesIO = BytesIO()
+    img.save(buffer, format="PNG")
+    await kitty.draw_to_terminal(buffer)
+    kitty.set_position(y, x)
+
+
+async def main():
+    plotter = pv.Plotter(off_screen=True, window_size=[400, 400])
+    plotter.add_mesh(pv.Sphere(), color="tomato")
+    plotter.add_text("Live capture example", font_size=14)
+
+    # plotter.show(auto_close=False)  # keep window open and interactive
+
+    # Access the vtkRenderWindow
+    vtk_render_window = plotter.ren_win
+
+    # Setup the window-to-image filter to capture the rendered scene
+    w2i_filter = vtk.vtkWindowToImageFilter()
+    w2i_filter.SetInput(vtk_render_window)
+
+    try:
+        while True:
+            # Trigger a render to make sure the content is fresh
+            vtk_render_window.Render()
+
+            # Update the filter to grab the current buffer
+            w2i_filter.Modified()
+            w2i_filter.Update()
+
+            vtk_image = w2i_filter.GetOutput()
+
+            width, height, _ = vtk_image.GetDimensions()
+            vtk_array = vtk_image.GetPointData().GetScalars()
+            arr = numpy_support.vtk_to_numpy(vtk_array)
+
+            # Reshape the array to height x width x channels (probably 3 or 4)
+            arr = arr.reshape(height, width, -1)
+
+            # Flip vertically because VTK's origin is bottom-left
+            arr = np.flip(arr, axis=0)
+
+            # Call the dummy render_to function with captured frame
+            await render_to(arr)
+
+            time.sleep(0.1)  # adjust framerate to your needs, e.g. 10 FPS
+            plotter.camera.Azimuth(1)
+
+    except KeyboardInterrupt:
+        print("Capture stopped by user.")
+        plotter.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())