app.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae4232b9-fb9f-419a-9992-8481d1de6b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "import gradio as gr\n",
    "import pandas as pd\n",
    "from huggingface_hub import list_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "51d7a652-f6d2-4cee-b787-88fc0fae0acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "def make_clickable_model(model_name, link=None):\n",
    "    if link is None:\n",
    "        link = \"https://huggingface.co/\" + model_name\n",
    "    # Remove user from model name\n",
    "    return f'<a target=\"_blank\" href=\"{link}\">{model_name.split(\"/\")[-1]}</a>'\n",
    "\n",
    "\n",
    "def make_clickable_user(user_id):\n",
    "    link = \"https://huggingface.co/\" + user_id\n",
    "    return f'<a  target=\"_blank\" href=\"{link}\">{user_id}</a>'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "82d94a98-0e69-4400-9cb1-2e90ef6da519",
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "def get_submissions(category):\n",
    "    submissions = list_models(filter=[\"dreambooth-hackathon\", category], full=True)\n",
    "    leaderboard_models = []\n",
    "\n",
    "    for submission in submissions:\n",
    "        # user, model, likes\n",
    "        user_id = submission.id.split(\"/\")[0]\n",
    "        leaderboard_models.append(\n",
    "            (\n",
    "                make_clickable_user(user_id),\n",
    "                make_clickable_model(submission.id),\n",
    "                submission.likes,\n",
    "            )\n",
    "        )\n",
    "\n",
    "    df = pd.DataFrame(data=leaderboard_models, columns=[\"User\", \"Model\", \"Likes\"])\n",
    "    df.sort_values(by=[\"Likes\"], ascending=False, inplace=True)\n",
    "    df.insert(0, \"Rank\", list(range(1, len(df) + 1)))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7579bfc6-ddf6-444d-ab7e-505734d86e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7894\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7894/\" width=\"100%\" 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": []
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     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# |export\n",
    "block = gr.Blocks()\n",
    "\n",
    "with block:\n",
    "    gr.Markdown(\n",
    "        \"\"\"# The DreamBooth Hackathon Leaderboard\n",
    "    \n",
    "    Welcome to the leaderboard for the DreamBooth Hackathon! This is a community event where particpants **personalise a Stable Diffusion model** by fine-tuning it with a powerful technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242). This technique allows one to implant a subject (e.g. your pet or favourite dish) into the output domain of the model such that it can be synthesized with a _unique identifier_ in the prompt. \n",
    "    \n",
    "    This competition is composed of 5 _themes_, where each theme will collect models belong to one of the categories shown in the tabs below. We'll be **giving out prizes to the top 3 most liked models per theme**, and you're encouraged to submit as many models as you want!\n",
    "    \n",
    "    For details on how to participate, check out the hackathon's guide [here](https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md).\n",
    "    \"\"\"\n",
    "    )\n",
    "    with gr.Tabs():\n",
    "        with gr.TabItem(\"Animal 🐨\"):\n",
    "            with gr.Row():\n",
    "                animal_data = gr.components.Dataframe(\n",
    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
    "                )\n",
    "            with gr.Row():\n",
    "                data_run = gr.Button(\"Refresh\")\n",
    "                data_run.click(\n",
    "                    get_submissions, inputs=gr.Variable(\"animal\"), outputs=animal_data\n",
    "                )\n",
    "        with gr.TabItem(\"Science 🔬\"):\n",
    "            with gr.Row():\n",
    "                science_data = gr.components.Dataframe(\n",
    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
    "                )\n",
    "            with gr.Row():\n",
    "                data_run = gr.Button(\"Refresh\")\n",
    "                data_run.click(\n",
    "                    get_submissions, inputs=gr.Variable(\"science\"), outputs=science_data\n",
    "                )\n",
    "        with gr.TabItem(\"Food 🍔\"):\n",
    "            with gr.Row():\n",
    "                food_data = gr.components.Dataframe(\n",
    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
    "                )\n",
    "            with gr.Row():\n",
    "                data_run = gr.Button(\"Refresh\")\n",
    "                data_run.click(\n",
    "                    get_submissions, inputs=gr.Variable(\"food\"), outputs=food_data\n",
    "                )\n",
    "        with gr.TabItem(\"Landscape 🏔\"):\n",
    "            with gr.Row():\n",
    "                landscape_data = gr.components.Dataframe(\n",
    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
    "                )\n",
    "            with gr.Row():\n",
    "                data_run = gr.Button(\"Refresh\")\n",
    "                data_run.click(\n",
    "                    get_submissions,\n",
    "                    inputs=gr.Variable(\"landscape\"),\n",
    "                    outputs=landscape_data,\n",
    "                )\n",
    "        with gr.TabItem(\"Wilcard 🔥\"):\n",
    "            with gr.Row():\n",
    "                wildcard_data = gr.components.Dataframe(\n",
    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
    "                )\n",
    "            with gr.Row():\n",
    "                data_run = gr.Button(\"Refresh\")\n",
    "                data_run.click(\n",
    "                    get_submissions,\n",
    "                    inputs=gr.Variable(\"wildcard\"),\n",
    "                    outputs=wildcard_data,\n",
    "                )\n",
    "\n",
    "    block.load(get_submissions, inputs=gr.Variable(\"animal\"), outputs=animal_data)\n",
    "    block.load(get_submissions, inputs=gr.Variable(\"science\"), outputs=science_data)\n",
    "    block.load(get_submissions, inputs=gr.Variable(\"food\"), outputs=food_data)\n",
    "    block.load(get_submissions, inputs=gr.Variable(\"landscape\"), outputs=landscape_data)\n",
    "    block.load(get_submissions, inputs=gr.Variable(\"wildcard\"), outputs=wildcard_data)\n",
    "\n",
    "\n",
    "block.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "17ff7d33-0c9a-4ca0-bb7b-ba1661063035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing server running on port: 7894\n"
     ]
    }
   ],
   "source": [
    "block.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "339fee32-8a83-435d-b882-55b5f0994774",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbdev.export import nb_export\n",
    "\n",
    "nb_export(\"app.ipynb\", lib_path=\".\", name=\"app\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "29f6746e-fbc3-4087-b2d8-46cd1a55e16e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile requirements.txt\n",
    "pandas\n",
    "huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63e8d8ea-31cc-4ddc-a08c-d9cbf02a909d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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