[SN-100] Update notebook for Project move_data_rows_to_task_queue (#1343)

Author: Gabefire

examples/basics/projects.ipynb

@@ -48,7 +48,7 @@
     {
       "metadata": {},
       "source": [
-        "!pip install labelbox -q"
+        "!pip install -q \"labelbox[data]\""
       ],
       "cell_type": "code",
       "outputs": [],
@@ -58,7 +58,7 @@
       "metadata": {},
       "source": [
         "import labelbox as lb\n",
-        "import os\n",
+        "import labelbox.types as lb_types\n",
         "import uuid"
       ],
       "cell_type": "code",
@@ -76,8 +76,9 @@
     {
       "metadata": {},
       "source": [
-        "# Add your api key\n",
+        "# Add your API key\n",
         "API_KEY = \"\"\n",
+        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
         "client = lb.Client(api_key=API_KEY)"
       ],
       "cell_type": "code",
@@ -154,6 +155,127 @@
       "outputs": [],
       "execution_count": null
     },
+    {
+      "metadata": {},
+      "source": [
+        "### Attach ontology and label data rows\n",
+        "\n",
+        "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create your ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create normalized json with a radio classification\n",
+        "ontology_builder = lb.OntologyBuilder(classifications=[  # List of Classification objects\n",
+        "        lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
+        "                          name=\"radio_question\",\n",
+        "                          options=[\n",
+        "                              lb.Option(value=\"first_radio_answer\"),\n",
+        "                              lb.Option(value=\"second_radio_answer\")\n",
+        "                          ]),\n",
+        "])\n",
+        "# Creating an ontology\n",
+        "ontology = client.create_ontology(\"test-ontology\",\n",
+        "                                  ontology_builder.asdict())"
+      ],
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Attach ontology to project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "project.setup_editor(ontology)"
+      ],
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create labels and upload them to project as ground truths"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create labels\n",
+        "labels = []\n",
+        "for global_key in global_keys:\n",
+        "    labels.append(lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n",
+        "                   annotations=[\n",
+        "                        # Create radio classification annotation for labels\n",
+        "                        lb_types.ClassificationAnnotation(\n",
+        "                         name=\"radio_question\",\n",
+        "                         value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+        "                         name=\"second_radio_answer\")))\n",
+        "                   ]))\n",
+        "\n",
+        "# Upload labels for the data rows in project\n",
+        "upload_job = lb.LabelImport.create_from_objects(\n",
+        "     client = client,\n",
+        "     project_id = project.uid,\n",
+        "     name=\"label_import_job\"+str(uuid.uuid4()),\n",
+        "     labels=labels)\n",
+        "\n",
+        "upload_job.wait_until_done()\n",
+        "\n",
+        "print(f\"Errors: {upload_job.errors}\")"
+      ],
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Move data rows in project to different task queues"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Get list of task queues for project\n",
+        "task_queues = project.task_queues()\n",
+        "\n",
+        "for task_queue in task_queues:\n",
+        "    print(task_queue)"
+      ],
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "project.move_data_rows_to_task_queue(data_row_ids=lb.GlobalKeys(global_keys), #Provide a list of global keys\n",
+        "                                     task_queue_id=task_queues[2].uid #Passing None moves data rows to \"Done\" task queue\n",
+        ")"
+      ],
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
     {
       "metadata": {},
       "source": [
@@ -180,14 +302,16 @@
     {
       "metadata": {},
       "source": [
-        "### Delete"
+        "### Clean Up"
       ],
       "cell_type": "markdown"
     },
     {
       "metadata": {},
       "source": [
-        "# project.delete()"
+        "# project.delete()\n",
+        "# dataset.delete()\n",
+        "# client.delete_unused_ontology(ontology.uid)"
       ],
       "cell_type": "code",
       "outputs": [],