Merge pull request #1877 from kili-technology/release/2.169.0

chore: release/2.169.0 -> Main

Author: baptiste-olivier

docs/sdk/tutorials/llm_dynamic.md

@@ -42,7 +42,6 @@
    "source": [
     "import os\n",
     "import shutil\n",
-    "import subprocess\n",
     "\n",
     "import requests\n",
     "import tqdm"
@@ -165,7 +164,10 @@
     "for asset_key in sorted_files.keys():\n",
     "    print(asset_key)\n",
     "    im = read_dcm_image(sorted_files[asset_key][20])\n",
-    "    im.save(f\"./recipes/img/frame_dicom_data_{asset_key}.png\")"
+    "    im_directory = os.path.dirname(os.path.abspath(\"\"))\n",
+    "    img_name = f\"frame_dicom_data_{asset_key}.png\"\n",
+    "    save_location = os.path.join(im_directory, \"img\", img_name)\n",
+    "    im.save(img_name)"
    ]
   },
   {
@@ -316,56 +318,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, let's import the volumes using `appendManyToDataset` (see [link](https://staging.cloud.kili-technology.com/docs/python-graphql-api/python-api/#append_many_to_dataset)). The key argument is `json_content_array`, which is a list of list of strings. Each element is the list of urls or paths pointing to images of the volume considered.\n",
-    " - Let's host these images locally to demonstrate how we would do it with cloud URLs for example :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subprocess.Popen(\n",
-    "    f\"python -m http.server 8001 --directory {ASSET_ROOT}\",\n",
-    "    shell=True,\n",
-    "    stdin=None,\n",
-    "    stdout=None,\n",
-    "    stderr=None,\n",
-    "    close_fds=True,\n",
-    ")\n",
-    "ROOT_URL = \"http://localhost:8001/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def files_to_urls(files):\n",
-    "    return list(map(lambda file: ROOT_URL + file.split(\"TCGA-LUAD\")[1], files))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kili.append_many_to_dataset(\n",
-    "    project_id=project_id,\n",
-    "    external_id_array=list(sorted_images.keys()),\n",
-    "    json_content_array=list(map(files_to_urls, sorted_images.values())),\n",
-    ")"
+    "Finally, let's import the volumes using `appendManyToDataset` (see [link](https://staging.cloud.kili-technology.com/docs/python-graphql-api/python-api/#append_many_to_dataset)). The key argument is `json_content_array`, which is a list of list of strings. Each element is the list of urls or paths pointing to images of the volume considered."
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or, as mentionned, you can simply provide the paths to your images, and call the function like below : "
+    "You can simply provide the paths to your images, and call the function like below : "
    ]
   },
   {
@@ -405,7 +366,7 @@
    "source": [
     "ds_size = kili.count_assets(project_id=project_id)\n",
     "print(ds_size)\n",
-    "assert ds_size == 6"
+    "assert ds_size == 3"
    ]
   },
   {