Upload a local File to JupyterLite-Pyodide without copying it into memory

jupyter-file-upload.ipynb

{
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "import IPython\nfrom IPython.display import HTML\nsession = IPython.get_ipython().kernel._parent_header['header']['session']\nsession",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": [
        {
          "execution_count": 1,
          "output_type": "execute_result",
          "data": {
            "text/plain": "'1ab81c31-4548-42fe-aa10-53f6b8c87fd7'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "from pathlib import Path\n\nimport js\nimport pyodide\nimport pyodide_js\n\ndef files_upload_callback(event):\n    if not getattr(event, \"data\", None) or not getattr(event.data, \"files\", None) or not getattr(event.data, \"uuid\", None):\n        return\n    \n    upload_path = Path(\"/uploads\") / event.data.uuid\n    upload_path.mkdir(parents=True, exist_ok=False)\n    \n    pyodide_js.FS.mount(\n        pyodide_js.FS.filesystems.WORKERFS,\n        pyodide.ffi.to_js({ \"files\": event.data.files }, dict_converter=js.Object.fromEntries, create_pyproxies=False),\n        str(upload_path),\n    )\n    \n    print(\", \".join(str(path) for path in upload_path.iterdir()))\n\njupyterLiteFileUpload = js.Reflect.construct(\n    js.BroadcastChannel,\n    pyodide.ffi.to_js([f\"jupyter-lite-file-upload-{session}\"]),\n)\njupyterLiteFileUpload.onmessage = files_upload_callback",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "display(HTML(fr'''\n<input type=\"file\" onchange=\"new BroadcastChannel('jupyter-lite-file-upload-{session}').postMessage({{ files: this.files, uuid: crypto.randomUUID() }})\"></input>\n'''))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n<input type=\"file\" onchange=\"new BroadcastChannel('jupyter-lite-file-upload-1ab81c31-4548-42fe-aa10-53f6b8c87fd7').postMessage({ files: this.files, uuid: crypto.randomUUID() })\"></input>\n"
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "/uploads/d8efac67-4ef0-48e9-8f53-03978220140e/large.file\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    }
  ]
}