{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c68f03e-620c-46a9-a7ec-a6cde27043cd",
   "metadata": {},
   "source": [
    "# Hugging Face Spaces From A Notebook\n",
    "\n",
    "> A demo of using nbdev with Hugging Face Spaces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96483373-4ae1-49b2-85ed-ceee8456df19",
   "metadata": {},
   "source": [
    "## 1. Create a Gradio-enabled Space on Hugging Face\n",
    "\n",
    "The first step is to create a space and select the appropriate sdk (which is Gradio in this example), per [these instructions](https://huggingface.co/docs/hub/spaces-overview#creating-a-new-space):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34d7ec6-69b8-48c4-a68b-fad6db3c2fab",
   "metadata": {},
   "source": [
    "![](./create_space.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25e8e7a-52d9-4305-a107-ba03e3d6a5f3",
   "metadata": {},
   "source": [
    "After you are done creating the space, **clone the repo per the instructions provided in the app.**  In this example, I ran the command `git clone https://huggingface.co/spaces/hamel/hfspace_demo`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff26114c-329b-4a97-98b5-c652554b0114",
   "metadata": {},
   "source": [
    "## 2. Make an app with Gradio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a884fc-36e2-43ec-8e42-ca2903aaa4de",
   "metadata": {},
   "source": [
    "Below, we will create a [gradio](https://gradio.app/) app in a notebook and show you how to deploy it to [Hugging Face Spaces](https://huggingface.co/docs/hub/spaces).\n",
    "\n",
    "First, lets specify the libraries we need, which in this case are `gradio` and `fastcore`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e5d597-19ad-46e5-81ad-8f646d8a1c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export app\n",
    "import gradio as gr\n",
    "from fastcore.net import urljson, HTTPError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38a4389f-ef53-4626-a6f5-a859354f854b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "def size(repo:str):\n",
    "    \"Returns the size in GB of a HuggingFace Dataset.\"\n",
    "    url = f'https://huggingface.co/api/datasets/{repo}'\n",
    "    try: resp = urljson(f'{url}/treesize/main')\n",
    "    except HTTPError: return f'Did not find repo: {url}'\n",
    "    gb = resp['size'] / 1e9\n",
    "    return f'{gb:.2f} GB'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff9f84d-7744-46ad-80ed-2cf1fa6d0643",
   "metadata": {},
   "source": [
    "`size` take as an input a [Hugging Face Dataset](https://huggingface.co/docs/datasets/index) repo and returns the total size in GB of the data.\n",
    "\n",
    "For example, we can check the size of [tglcourse/CelebA-faces-cropped-128](https://huggingface.co/datasets/tglcourse/CelebA-faces-cropped-128) like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95bc32b8-d8ff-4761-a2d7-0880c51d0a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5.49 GB'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(\"tglcourse/CelebA-faces-cropped-128\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb13747b-ea48-4146-846d-deb9e855d32d",
   "metadata": {},
   "source": [
    "You can construct a simple UI with the `gradio.interface` and then call the `launch` method of that interface to display a preview in a notebook.  This is a great way to test your app to see if it works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b20e2a1-b622-4970-9069-0202ce10a2ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"500\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App>, 'http://127.0.0.1:7860/', None)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|export\n",
    "iface = gr.Interface(fn=size, inputs=gr.Text(value=\"tglcourse/CelebA-faces-cropped-128\"), outputs=\"text\")\n",
    "iface.launch(width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59926b18-a9af-4387-9fcc-f88e588da577",
   "metadata": {},
   "source": [
    "Note how running the `launch()` method in a notebook runs a webserver in the background.  Below, we call the `close()` method to close the webserver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d7be72-9389-42cf-91b1-78e8f4bbd083",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing server running on port: 7860\n"
     ]
    }
   ],
   "source": [
    "# this is only necessary in a notebook\n",
    "iface.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249b2cd7-3123-45bf-945f-882b8a964cf5",
   "metadata": {},
   "source": [
    "## 3. Converting This Notebook Into A Gradio App"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c18ca6e-8de8-49e1-b95a-304070bbc171",
   "metadata": {},
   "source": [
    "In order to host this code on Hugging Faces spaces, you will export parts of this notebook to a script named `app.py`.  That is what the special `#|export` comment that you have seen in cells above do!  You can export code from this notebook like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6706d92c-5785-4f09-9773-b9a944c493a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbdev.export import nb_export\n",
    "nb_export('app.ipynb', lib_path='.', name='app')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0182403f-d1d6-48c0-8e66-46aefb23a9ab",
   "metadata": {},
   "source": [
    "<div>\n",
    "<link rel=\"stylesheet\" href=\"https://gradio.s3-us-west-2.amazonaws.com/2.6.5/static/bundle.css\">\n",
    "<div id=\"target\"></div>\n",
    "<script src=\"https://gradio.s3-us-west-2.amazonaws.com/2.6.5/static/bundle.js\"></script>\n",
    "<script>\n",
    "launchGradioFromSpaces(\"abidlabs/question-answering\", \"#target\")\n",
    "</script>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d5fd19-7880-459c-8382-b3574ed11141",
   "metadata": {},
   "source": [
    "### Understanding what is generated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea562e7-b67a-45df-b822-2f4528a307c2",
   "metadata": {},
   "source": [
    "Notice how the contents of app.py only contains the exported cells from this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bae6a5c-58bc-4a0f-9aac-34c092150fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31m# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# %% auto 0\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0m__all__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'iface'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# %% app.ipynb 7\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"Returns the size in GB of a HuggingFace Dataset.\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'https://huggingface.co/api/datasets/{repo}'\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murljson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{url}/treesize/main'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34mf'Did not find repo: {url}'\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1e9\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0;34mf'{gb:.2f} GB'\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# %% app.ipynb 11\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0miface\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tglcourse/CelebA-faces-cropped-128\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0miface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pycat app.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a081bb0f-5cad-4b99-962b-4dd49cee61a2",
   "metadata": {},
   "source": [
    "### Fill out `requirements.txt`\n",
    "\n",
    "You must supply a requirements.txt file so the gradio app knows how to build your dependencies.  In this example, the only depdency other than gradio is `fastcore`.  You don't need to specify gradio itself as a depdendency in `requirements.txt` so our `requirements.txt` file has only one dependency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b611d9c-d262-4124-9e9e-4fe754ac4378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fastcore"
     ]
    }
   ],
   "source": [
    "!cat requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15d9c78-1f55-449e-8058-9af1832367a0",
   "metadata": {},
   "source": [
    "## 4. Launch Your Gradio App\n",
    "\n",
    "To launch your gradio app, you need to commit the changes in the Hugging Face repo:\n",
    "\n",
    "```\n",
    "git add -A; git commit -m \"Add application files\"; git push\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa661f93-73b4-465a-9c22-cc38197505cb",
   "metadata": {},
   "source": [
    "## 5. Voilà!  Enjoy your Gradio App"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b20ff94-6842-4078-9ec1-be740944e721",
   "metadata": {},
   "source": [
    "After a couple of minutes, you will see your app published!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a4f7c06-406a-4a7d-be6b-6cb606c35d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook app.ipynb to markdown\n",
      "[NbConvertApp] Writing 6113 bytes to app.md\n"
     ]
    }
   ],
   "source": [
    "# this is only for hamel, you can ignore this.\n",
    "!jupyter nbconvert --to markdown app.ipynb \n",
    "!cat yaml.md app.md > README.md "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "958174fe-537e-4635-90b8-22ac11eae396",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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 },
 "nbformat": 4,
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}