add dashboard nb

vizro-ai/examples/colab-example-chart.ipynb

@@ -13,7 +13,7 @@
    "id": "fbc65f78",
    "metadata": {},
    "source": [
-    "# Use Vizro-AI in Google Colab"
+    "# Use Vizro-AI to generate Vizro charts"
    ]
   },
   {
@@ -30,6 +30,12 @@
     "- click the `Notebook access` toggle to make it available for the notebook run"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c0df4a08",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,

vizro-ai/examples/colab-example-dashboard.ipynb

@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mckinsey/vizro/blob/docs/add_colab_link/vizro-ai/examples/colab-example-dashboard.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Use Vizro-AI to generate Vizro dashboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1. Secure your LLM API key as environment variable\n",
+    "\n",
+    "- click the `Secrets` button on the left\n",
+    "- click `Add new secret`\n",
+    "- insert \"OPENAI_API_KEY\" under `Name`\n",
+    "- insert the API key value under `Value`\n",
+    "- click the `Notebook access` toggle to make it available for the notebook run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"OPENAI_API_KEY\")\n",
+    "os.environ[\"OPENAI_BASE_URL\"] = userdata.get(\"OPENAI_BASE_URL\") # optional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Install Vizro-AI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: change to !pip install vizro-ai when this PR is ready to merge\n",
+    "# !pip install vizro-ai\n",
+    "!pip install \"vizro-ai @ git+https://github.com/mckinsey/vizro.git@main#subdirectory=vizro-ai\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import vizro.plotly.express as px\n",
+    "\n",
+    "from vizro import Vizro\n",
+    "from vizro_ai import VizroAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title ## 1. Input your dashboard question\n",
+    "\n",
+    "# @markdown ---\n",
+    "# @markdown #### Instructions:\n",
+    "user_input = '' # @param {type:\"string\", placeholder:\"Enter the requirements for the dashboard\"}\n",
+    "\n",
+    "print(user_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Upload your data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title\n",
+    "import pandas as pd\n",
+    "from google.colab import files\n",
+    "\n",
+    "uploaded = files.upload()\n",
+    "\n",
+    "dfs = []\n",
+    "for fn in uploaded.keys():\n",
+    "    print('User uploaded file \"{name}\"'.format(\n",
+    "        name=fn, length=len(uploaded[fn])))\n",
+    "    \n",
+    "    df_uploaded = pd.read_csv(fn)\n",
+    "    dfs.append(df_uploaded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title String fields\n",
+    "\n",
+    "LLM = 'gpt-4o' # @param [\"gpt-4o\", \"gpt-3.5-turbo\", \"gpt-4-turbo\"]\n",
+    "\n",
+    "print(LLM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # @title\n",
+    "# import ipywidgets as widgets\n",
+    "# from IPython.display import display\n",
+    "# button = widgets.Button(description=\"Build Dashboard!\")\n",
+    "# output = widgets.Output()\n",
+    "\n",
+    "# def on_button_clicked(b):\n",
+    "#   # Display the message within the output widget.\n",
+    "#   with output:\n",
+    "#     print(\"In progress...\")\n",
+    "#     vizro_ai = VizroAI(model=LLM)\n",
+    "#     dashboard = vizro_ai.dashboard(dfs, user_input)\n",
+    "#     vizro_dashboard = Vizro().build(dashboard)\n",
+    "#     print(\"Build complete.\")\n",
+    "\n",
+    "# button.on_click(on_button_clicked)\n",
+    "# display(button, output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dashboard = vizro_ai.dashboard(dfs, user_input)\n",
+    "vizro_dashboard = Vizro().build(dashboard)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vizro_dashboard.run()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}