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{
"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
}
|