Delete app.ipynb

app.ipynb

@@ -1,262 +0,0 @@
-{
- "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": []
-     },
-     "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": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hf",
-   "language": "python",
-   "name": "hf"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}