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Switch linting to Ruff (#3710)

Browse Source 
* Sort requirements.in

* Switch flake8 + isort to ruff

* Apply ruff import order fixes

* Fix ruff complaints in demo/

* Fix ruff complaints in test/

* Use `x is not y`, not `not x is y`

* Remove unused listdir from website generator

* Clean up duplicate dict keys

* Add changelog entry

* Clean up unused imports (except in gradio/__init__.py)

* add space

---------

Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
This commit is contained in:
Aarni Koskela 2023-04-04 01:48:18 +03:00 committed by GitHub
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1
CHANGELOG.md
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@ -120,6 +120,7 @@ By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3581](https://git
- Removed heavily-mocked tests related to comet_ml, wandb, and mlflow as they added a significant amount of test dependencies that prevented installation of test dependencies on Windows environemnts. By [@abidlabs](https://github.com/abidlabs) in [PR 3608](https://github.com/gradio-app/gradio/pull/3608)
- Added Windows continuous integration, by [@space-nuko](https://github.com/space-nuko) in [PR 3628](https://github.com/gradio-app/gradio/pull/3628)
- Switched linting from flake8 + isort to `ruff`, by [@akx](https://github.com/akx) in [PR 3710](https://github.com/gradio-app/gradio/pull/3710)
## Breaking Changes:

5
client/python/gradio_client/__init__.py
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@ -1,2 +1,7 @@
from gradio_client.client import Client
from gradio_client.utils import __version__
__all__ = [
"Client",
"__version__",
]

8
client/python/pyproject.toml
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@ -41,3 +41,11 @@ include = [
"/README.md",
"/requirements.txt",
]
[tool.ruff]
extend = "../../pyproject.toml"
[tool.ruff.isort]
known-first-party = [
"gradio_client"
]

5
client/python/scripts/ci.sh
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@ -4,9 +4,8 @@ set -e
cd "$(dirname ${0})/.."
echo "Linting..."
python -m black --check test gradio_client
python -m isort --profile=black --check-only test gradio_client
python -m flake8 --ignore=E731,E501,E722,W503,E126,E203,F403,F541 test gradio_client --exclude gradio_client/__init__.py
ruff test gradio_client
black --check test gradio_client
echo "Testing..."
python -m pip install -e ../../. # Install gradio from local source (as the latest version may not yet be published to PyPI)

5
client/python/scripts/format.sh
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@ -3,6 +3,5 @@
cd "$(dirname ${0})/.."
echo "Formatting the client library.. Our style follows the Black code style."
python -m black test gradio_client
python -m isort --profile=black test gradio_client
python -m flake8 --ignore=E731,E501,E722,W503,E126,E203,F403 test gradio_client --exclude gradio_client/__init__.py
ruff --fix test gradio_client
black test gradio_client

5
client/python/test/requirements.txt
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@ -1,5 +1,4 @@
black==22.6.0
flake8==4.0.1
isort==5.10.1
pytest-asyncio
pytest==7.1.2
pytest-asyncio
ruff==0.0.260

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demo/Echocardiogram-Segmentation/run.ipynb
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demo/Echocardiogram-Segmentation/run.py
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@ -1,8 +1,5 @@
import os
from os.path import splitext
import numpy as np
import sys
import matplotlib.pyplot as plt
import torch
import torchvision
import wget

2
demo/animeganv2/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: animeganv2\n", "### Recreate the viral AnimeGAN image transformation demo.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision Pillow gdown numpy scipy cmake onnxruntime-gpu opencv-python-headless"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/gongyoo.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/groot.jpeg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from PIL import Image\n", "import torch\n", "\n", "model2 = torch.hub.load(\n", " \"AK391/animegan2-pytorch:main\",\n", " \"generator\",\n", " pretrained=True,\n", " progress=False\n", ")\n", "model1 = torch.hub.load(\"AK391/animegan2-pytorch:main\", \"generator\", pretrained=\"face_paint_512_v1\")\n", "face2paint = torch.hub.load(\n", " 'AK391/animegan2-pytorch:main', 'face2paint', \n", " size=512,side_by_side=False\n", ")\n", "\n", "def inference(img, ver):\n", " if ver == 'version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)':\n", " out = face2paint(model2, img)\n", " else:\n", " out = face2paint(model1, img)\n", " return out\n", "\n", "title = \"AnimeGANv2\"\n", "description = \"Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below.\"\n", "article = \"<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>\"\n", "examples=[['groot.jpeg','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'],['gongyoo.jpeg','version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)']]\n", "\n", "demo = gr.Interface(\n", " fn=inference, \n", " inputs=[gr.inputs.Image(type=\"pil\"),gr.inputs.Radio(['version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'], type=\"value\", default='version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)', label='version')], \n", " outputs=gr.outputs.Image(type=\"pil\"),\n", " title=title,\n", " description=description,\n", " article=article,\n", " examples=examples)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: animeganv2\n", "### Recreate the viral AnimeGAN image transformation demo.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision Pillow gdown numpy scipy cmake onnxruntime-gpu opencv-python-headless"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/gongyoo.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/groot.jpeg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "\n", "model2 = torch.hub.load(\n", " \"AK391/animegan2-pytorch:main\",\n", " \"generator\",\n", " pretrained=True,\n", " progress=False\n", ")\n", "model1 = torch.hub.load(\"AK391/animegan2-pytorch:main\", \"generator\", pretrained=\"face_paint_512_v1\")\n", "face2paint = torch.hub.load(\n", " 'AK391/animegan2-pytorch:main', 'face2paint', \n", " size=512,side_by_side=False\n", ")\n", "\n", "def inference(img, ver):\n", " if ver == 'version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)':\n", " out = face2paint(model2, img)\n", " else:\n", " out = face2paint(model1, img)\n", " return out\n", "\n", "title = \"AnimeGANv2\"\n", "description = \"Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below.\"\n", "article = \"<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>\"\n", "examples=[['groot.jpeg','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'],['gongyoo.jpeg','version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)']]\n", "\n", "demo = gr.Interface(\n", " fn=inference, \n", " inputs=[gr.inputs.Image(type=\"pil\"),gr.inputs.Radio(['version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'], type=\"value\", default='version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)', label='version')], \n", " outputs=gr.outputs.Image(type=\"pil\"),\n", " title=title,\n", " description=description,\n", " article=article,\n", " examples=examples)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/animeganv2/run.py
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@ -1,5 +1,4 @@
import gradio as gr
from PIL import Image
import torch
model2 = torch.hub.load(

2
demo/bokeh_plot/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.plotting import figure\n", "from bokeh.tile_providers import get_provider\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", " if plot_type == \"map\":\n", " tile_provider = get_provider(xyz.OpenStreetMap.Mapnik)\n", " plot = figure(\n", " x_range=(-2000000, 6000000),\n", " y_range=(-1000000, 7000000),\n", " x_axis_type=\"mercator\",\n", " y_axis_type=\"mercator\",\n", " )\n", " plot.add_tile(tile_provider)\n", " return plot\n", " elif plot_type == \"whisker\":\n", " classes = list(sorted(df[\"class\"].unique()))\n", "\n", " p = figure(\n", " height=400,\n", " x_range=classes,\n", " background_fill_color=\"#efefef\",\n", " title=\"Car class vs HWY mpg with quintile ranges\",\n", " )\n", " p.xgrid.grid_line_color = None\n", "\n", " g = df.groupby(\"class\")\n", " upper = g.hwy.quantile(0.80)\n", " lower = g.hwy.quantile(0.20)\n", " source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", " error = Whisker(\n", " base=\"base\",\n", " upper=\"upper\",\n", " lower=\"lower\",\n", " source=source,\n", " level=\"annotation\",\n", " line_width=2,\n", " )\n", " error.upper_head.size = 20\n", " error.lower_head.size = 20\n", " p.add_layout(error)\n", "\n", " p.circle(\n", " jitter(\"class\", 0.3, range=p.x_range),\n", " \"hwy\",\n", " source=df,\n", " alpha=0.5,\n", " size=13,\n", " line_color=\"white\",\n", " color=factor_cmap(\"class\", \"Light6\", classes),\n", " )\n", " return p\n", " elif plot_type == \"scatter\":\n", "\n", " SPECIES = sorted(data.species.unique())\n", " MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", " p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", " p.xaxis.axis_label = \"Flipper Length (mm)\"\n", " p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", " p.scatter(\n", " \"flipper_length_mm\",\n", " \"body_mass_g\",\n", " source=data,\n", " legend_group=\"species\",\n", " fill_alpha=0.4,\n", " size=12,\n", " marker=factor_mark(\"species\", MARKERS, SPECIES),\n", " color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", " )\n", "\n", " p.legend.location = \"top_left\"\n", " p.legend.title = \"Species\"\n", " return p\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", " plot = gr.Plot()\n", " plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", " demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.tile_providers import get_provider\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", " if plot_type == \"map\":\n", " tile_provider = get_provider(xyz.OpenStreetMap.Mapnik)\n", " plot = figure(\n", " x_range=(-2000000, 6000000),\n", " y_range=(-1000000, 7000000),\n", " x_axis_type=\"mercator\",\n", " y_axis_type=\"mercator\",\n", " )\n", " plot.add_tile(tile_provider)\n", " return plot\n", " elif plot_type == \"whisker\":\n", " classes = list(sorted(df[\"class\"].unique()))\n", "\n", " p = figure(\n", " height=400,\n", " x_range=classes,\n", " background_fill_color=\"#efefef\",\n", " title=\"Car class vs HWY mpg with quintile ranges\",\n", " )\n", " p.xgrid.grid_line_color = None\n", "\n", " g = df.groupby(\"class\")\n", " upper = g.hwy.quantile(0.80)\n", " lower = g.hwy.quantile(0.20)\n", " source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", " error = Whisker(\n", " base=\"base\",\n", " upper=\"upper\",\n", " lower=\"lower\",\n", " source=source,\n", " level=\"annotation\",\n", " line_width=2,\n", " )\n", " error.upper_head.size = 20\n", " error.lower_head.size = 20\n", " p.add_layout(error)\n", "\n", " p.circle(\n", " jitter(\"class\", 0.3, range=p.x_range),\n", " \"hwy\",\n", " source=df,\n", " alpha=0.5,\n", " size=13,\n", " line_color=\"white\",\n", " color=factor_cmap(\"class\", \"Light6\", classes),\n", " )\n", " return p\n", " elif plot_type == \"scatter\":\n", "\n", " SPECIES = sorted(data.species.unique())\n", " MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", " p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", " p.xaxis.axis_label = \"Flipper Length (mm)\"\n", " p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", " p.scatter(\n", " \"flipper_length_mm\",\n", " \"body_mass_g\",\n", " source=data,\n", " legend_group=\"species\",\n", " fill_alpha=0.4,\n", " size=12,\n", " marker=factor_mark(\"species\", MARKERS, SPECIES),\n", " color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", " )\n", "\n", " p.legend.location = \"top_left\"\n", " p.legend.title = \"Species\"\n", " return p\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", " plot = gr.Plot()\n", " plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", " demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

3
demo/bokeh_plot/run.py
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@ -1,6 +1,5 @@
import gradio as gr
import xyzservices.providers as xyz
from bokeh.plotting import figure
from bokeh.tile_providers import get_provider
from bokeh.models import ColumnDataSource, Whisker
from bokeh.plotting import figure
@ -91,4 +90,4 @@ with gr.Blocks() as demo:
if __name__ == "__main__":
demo.launch()
demo.launch()

2
demo/chicago-bikeshare-dashboard/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "matplotlib.use(\"Agg\")\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", " f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, rideable_type\n", " FROM rides\n", " GROUP BY rideable_type\n", " ORDER BY n DESC\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", " FROM RIDES\n", " WHERE start_station_name is NOT NULL\n", " GROUP BY start_station_id\n", " ORDER BY n DESC\n", " LIMIT 5\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Chicago Bike Share Dashboard\n", " \n", " This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", " This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", " is compatible with gradio.\n", " \n", " Connection credentials are handled by environment variables\n", " defined as secrets in the Space.\n", "\n", " If data were added to the database, the plots in this demo would update\n", " whenever the webpage is reloaded.\n", " \n", " This demo serves as a starting point for your database-connected apps!\n", " \"\"\"\n", " )\n", " with gr.Row():\n", " bike_type = gr.BarPlot(\n", " x=\"rideable_type\",\n", " y='n',\n", " title=\"Number of rides per bicycle type\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Bicycle Type\",\n", " vertical=False,\n", " tooltip=['rideable_type', \"n\"],\n", " height=300,\n", " width=300,\n", " )\n", " station = gr.BarPlot(\n", " x='station',\n", " y='n',\n", " title=\"Most Popular Stations\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Station Name\",\n", " vertical=False,\n", " tooltip=['station', 'n'],\n", " height=300,\n", " width=300\n", " )\n", "\n", " demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", " demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import matplotlib\n", "import pandas as pd\n", "\n", "matplotlib.use(\"Agg\")\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", " f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, rideable_type\n", " FROM rides\n", " GROUP BY rideable_type\n", " ORDER BY n DESC\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", " FROM RIDES\n", " WHERE start_station_name is NOT NULL\n", " GROUP BY start_station_id\n", " ORDER BY n DESC\n", " LIMIT 5\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Chicago Bike Share Dashboard\n", " \n", " This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", " This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", " is compatible with gradio.\n", " \n", " Connection credentials are handled by environment variables\n", " defined as secrets in the Space.\n", "\n", " If data were added to the database, the plots in this demo would update\n", " whenever the webpage is reloaded.\n", " \n", " This demo serves as a starting point for your database-connected apps!\n", " \"\"\"\n", " )\n", " with gr.Row():\n", " bike_type = gr.BarPlot(\n", " x=\"rideable_type\",\n", " y='n',\n", " title=\"Number of rides per bicycle type\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Bicycle Type\",\n", " vertical=False,\n", " tooltip=['rideable_type', \"n\"],\n", " height=300,\n", " width=300,\n", " )\n", " station = gr.BarPlot(\n", " x='station',\n", " y='n',\n", " title=\"Most Popular Stations\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Station Name\",\n", " vertical=False,\n", " tooltip=['station', 'n'],\n", " height=300,\n", " width=300\n", " )\n", "\n", " demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", " demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/chicago-bikeshare-dashboard/run.py
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@ -1,7 +1,6 @@
import os
import gradio as gr
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
matplotlib.use("Agg")

2
demo/depth_estimation/run.ipynb
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File diff suppressed because one or more lines are too long

5
demo/depth_estimation/run.py
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@ -5,7 +5,6 @@ import numpy as np
from PIL import Image
import open3d as o3d
from pathlib import Path
import os
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
@ -38,7 +37,7 @@ def process_image(image_path):
gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
img = Image.fromarray(depth_image)
return [img, gltf_path, gltf_path]
except Exception as e:
except Exception:
gltf_path = create_3d_obj(
np.array(image), depth_image, image_path, depth=8)
img = Image.fromarray(depth_image)
@ -79,7 +78,7 @@ def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
[0, 0, 0, 1]])
print('run Poisson surface reconstruction')
with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):
mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
pcd, depth=depth, width=0, scale=1.1, linear_fit=True)

2
demo/digit_classifier/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: digit_classifier"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "from urllib.request import urlretrieve\n", "\n", "import tensorflow as tf\n", "\n", "import gradio\n", "import gradio as gr\n", "\n", "urlretrieve(\n", " \"https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5\", \"mnist-model.h5\"\n", ")\n", "model = tf.keras.models.load_model(\"mnist-model.h5\")\n", "\n", "\n", "def recognize_digit(image):\n", " image = image.reshape(1, -1)\n", " prediction = model.predict(image).tolist()[0]\n", " return {str(i): prediction[i] for i in range(10)}\n", "\n", "\n", "im = gradio.Image(shape=(28, 28), image_mode=\"L\", invert_colors=False, source=\"canvas\")\n", "\n", "demo = gr.Interface(\n", " recognize_digit,\n", " im,\n", " gradio.Label(num_top_classes=3),\n", " live=True,\n", " interpretation=\"default\",\n", " capture_session=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: digit_classifier"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from urllib.request import urlretrieve\n", "\n", "import tensorflow as tf\n", "\n", "import gradio\n", "import gradio as gr\n", "\n", "urlretrieve(\n", " \"https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5\", \"mnist-model.h5\"\n", ")\n", "model = tf.keras.models.load_model(\"mnist-model.h5\")\n", "\n", "\n", "def recognize_digit(image):\n", " image = image.reshape(1, -1)\n", " prediction = model.predict(image).tolist()[0]\n", " return {str(i): prediction[i] for i in range(10)}\n", "\n", "\n", "im = gradio.Image(shape=(28, 28), image_mode=\"L\", invert_colors=False, source=\"canvas\")\n", "\n", "demo = gr.Interface(\n", " recognize_digit,\n", " im,\n", " gradio.Label(num_top_classes=3),\n", " live=True,\n", " interpretation=\"default\",\n", " capture_session=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/digit_classifier/run.py
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@ -1,4 +1,3 @@
import os
from urllib.request import urlretrieve
import tensorflow as tf

2
demo/fake_gan/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan\n", "### This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan/files/cheetah1.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import os\n", "import random\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", " images = [\n", " (random.choice(\n", " [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", " \"https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80\",\n", " ]\n", " ), f\"label {i}\" if i != 0 else \"label\" * 50)\n", " for i in range(3)\n", " ]\n", " return images\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column(variant=\"panel\"):\n", " with gr.Row(variant=\"compact\"):\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " show_label=False,\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " ).style(\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\").style(full_width=False)\n", "\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " ).style(grid=[2], height=\"auto\")\n", "\n", " btn.click(fake_gan, None, gallery)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan\n", "### This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan/files/cheetah1.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import random\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", " images = [\n", " (random.choice(\n", " [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", " \"https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80\",\n", " ]\n", " ), f\"label {i}\" if i != 0 else \"label\" * 50)\n", " for i in range(3)\n", " ]\n", " return images\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column(variant=\"panel\"):\n", " with gr.Row(variant=\"compact\"):\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " show_label=False,\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " ).style(\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\").style(full_width=False)\n", "\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " ).style(grid=[2], height=\"auto\")\n", "\n", " btn.click(fake_gan, None, gallery)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/fake_gan/run.py
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@ -1,6 +1,5 @@
# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import os
import random
import gradio as gr

2
demo/fake_gan_2/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan_2"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/cheetah1.jpg\n", "!wget -q -O files/elephant.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/elephant.jpg\n", "!wget -q -O files/tiger.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/tiger.jpg\n", "!wget -q -O files/zebra.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/zebra.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import os\n", "import random\n", "import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan(desc):\n", " if desc == \"NSFW\":\n", " raise gr.Error(\"NSFW - banned content.\")\n", " if desc == \"error\":\n", " raise ValueError(\"error\")\n", " time.sleep(9)\n", " image = random.choice(\n", " [\n", " \"files/cheetah1.jpg\",\n", " \"files/elephant.jpg\",\n", " \"files/tiger.jpg\",\n", " \"files/zebra.jpg\",\n", " ]\n", " )\n", " return image\n", "\n", "\n", "demo = gr.Interface(\n", " fn=fake_gan,\n", " inputs=gr.Textbox(),\n", " outputs=gr.Image(label=\"Generated Image\"),\n", " title=\"FD-GAN\",\n", " description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "demo.queue(max_size=3)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan_2"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/cheetah1.jpg\n", "!wget -q -O files/elephant.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/elephant.jpg\n", "!wget -q -O files/tiger.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/tiger.jpg\n", "!wget -q -O files/zebra.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/zebra.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import random\n", "import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan(desc):\n", " if desc == \"NSFW\":\n", " raise gr.Error(\"NSFW - banned content.\")\n", " if desc == \"error\":\n", " raise ValueError(\"error\")\n", " time.sleep(9)\n", " image = random.choice(\n", " [\n", " \"files/cheetah1.jpg\",\n", " \"files/elephant.jpg\",\n", " \"files/tiger.jpg\",\n", " \"files/zebra.jpg\",\n", " ]\n", " )\n", " return image\n", "\n", "\n", "demo = gr.Interface(\n", " fn=fake_gan,\n", " inputs=gr.Textbox(),\n", " outputs=gr.Image(label=\"Generated Image\"),\n", " title=\"FD-GAN\",\n", " description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "demo.queue(max_size=3)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/fake_gan_2/run.py
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@ -1,6 +1,5 @@
# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import os
import random
import time

2
demo/fake_gan_no_input/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan_no_input"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import random\n", "import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", " time.sleep(1)\n", " images = [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", " ]\n", " return images\n", "\n", "\n", "demo = gr.Interface(\n", " fn=fake_gan,\n", " inputs=None,\n", " outputs=gr.Gallery(label=\"Generated Images\").style(grid=[2]),\n", " title=\"FD-GAN\",\n", " description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan_no_input"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", " time.sleep(1)\n", " images = [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", " ]\n", " return images\n", "\n", "\n", "demo = gr.Interface(\n", " fn=fake_gan,\n", " inputs=None,\n", " outputs=gr.Gallery(label=\"Generated Images\").style(grid=[2]),\n", " title=\"FD-GAN\",\n", " description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/fake_gan_no_input/run.py
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@ -1,4 +1,3 @@
import random
import time
import gradio as gr

2
demo/hangman/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: hangman"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "\n", "secret_word = \"gradio\"\n", "\n", "with gr.Blocks() as demo: \n", " used_letters_var = gr.State([])\n", " with gr.Row() as row:\n", " with gr.Column():\n", " input_letter = gr.Textbox(label=\"Enter letter\")\n", " btn = gr.Button(\"Guess Letter\")\n", " with gr.Column():\n", " hangman = gr.Textbox(\n", " label=\"Hangman\",\n", " value=\"_\"*len(secret_word)\n", " )\n", " used_letters_box = gr.Textbox(label=\"Used Letters\")\n", "\n", " def guess_letter(letter, used_letters):\n", " used_letters.append(letter)\n", " answer = \"\".join([\n", " (letter if letter in used_letters else \"_\")\n", " for letter in secret_word\n", " ])\n", " return {\n", " used_letters_var: used_letters,\n", " used_letters_box: \", \".join(used_letters),\n", " hangman: answer\n", " }\n", " btn.click(\n", " guess_letter, \n", " [input_letter, used_letters_var],\n", " [used_letters_var, used_letters_box, hangman]\n", " )\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: hangman"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "secret_word = \"gradio\"\n", "\n", "with gr.Blocks() as demo: \n", " used_letters_var = gr.State([])\n", " with gr.Row() as row:\n", " with gr.Column():\n", " input_letter = gr.Textbox(label=\"Enter letter\")\n", " btn = gr.Button(\"Guess Letter\")\n", " with gr.Column():\n", " hangman = gr.Textbox(\n", " label=\"Hangman\",\n", " value=\"_\"*len(secret_word)\n", " )\n", " used_letters_box = gr.Textbox(label=\"Used Letters\")\n", "\n", " def guess_letter(letter, used_letters):\n", " used_letters.append(letter)\n", " answer = \"\".join([\n", " (letter if letter in used_letters else \"_\")\n", " for letter in secret_word\n", " ])\n", " return {\n", " used_letters_var: used_letters,\n", " used_letters_box: \", \".join(used_letters),\n", " hangman: answer\n", " }\n", " btn.click(\n", " guess_letter, \n", " [input_letter, used_letters_var],\n", " [used_letters_var, used_letters_box, hangman]\n", " )\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/hangman/run.py
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@ -1,5 +1,4 @@
import gradio as gr
import random
secret_word = "gradio"

2
demo/main_note/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: main_note"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/cantina.wav\n", "!wget -q -O audio/recording1.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/recording1.wav"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from math import log2, pow\n", "import os\n", "\n", "import numpy as np\n", "from scipy.fftpack import fft\n", "\n", "import gradio as gr\n", "\n", "A4 = 440\n", "C0 = A4 * pow(2, -4.75)\n", "name = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n", "\n", "\n", "def get_pitch(freq):\n", " h = round(12 * log2(freq / C0))\n", " n = h % 12\n", " return name[n]\n", "\n", "\n", "def main_note(audio):\n", " rate, y = audio\n", " if len(y.shape) == 2:\n", " y = y.T[0]\n", " N = len(y)\n", " T = 1.0 / rate\n", " x = np.linspace(0.0, N * T, N)\n", " yf = fft(y)\n", " yf2 = 2.0 / N * np.abs(yf[0 : N // 2])\n", " xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)\n", "\n", " volume_per_pitch = {}\n", " total_volume = np.sum(yf2)\n", " for freq, volume in zip(xf, yf2):\n", " if freq == 0:\n", " continue\n", " pitch = get_pitch(freq)\n", " if pitch not in volume_per_pitch:\n", " volume_per_pitch[pitch] = 0\n", " volume_per_pitch[pitch] += 1.0 * volume / total_volume\n", " volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}\n", " return volume_per_pitch\n", "\n", "\n", "demo = gr.Interface(\n", " main_note,\n", " gr.Audio(source=\"microphone\"),\n", " gr.Label(num_top_classes=4),\n", " examples=[\n", " [os.path.join(os.path.abspath(''),\"audio/recording1.wav\")],\n", " [os.path.join(os.path.abspath(''),\"audio/cantina.wav\")],\n", " ],\n", " interpretation=\"default\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: main_note"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/cantina.wav\n", "!wget -q -O audio/recording1.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/recording1.wav"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from math import log2, pow\n", "import os\n", "\n", "import numpy as np\n", "from scipy.fftpack import fft\n", "\n", "import gradio as gr\n", "\n", "A4 = 440\n", "C0 = A4 * pow(2, -4.75)\n", "name = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n", "\n", "\n", "def get_pitch(freq):\n", " h = round(12 * log2(freq / C0))\n", " n = h % 12\n", " return name[n]\n", "\n", "\n", "def main_note(audio):\n", " rate, y = audio\n", " if len(y.shape) == 2:\n", " y = y.T[0]\n", " N = len(y)\n", " T = 1.0 / rate\n", " yf = fft(y)\n", " yf2 = 2.0 / N * np.abs(yf[0 : N // 2])\n", " xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)\n", "\n", " volume_per_pitch = {}\n", " total_volume = np.sum(yf2)\n", " for freq, volume in zip(xf, yf2):\n", " if freq == 0:\n", " continue\n", " pitch = get_pitch(freq)\n", " if pitch not in volume_per_pitch:\n", " volume_per_pitch[pitch] = 0\n", " volume_per_pitch[pitch] += 1.0 * volume / total_volume\n", " volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}\n", " return volume_per_pitch\n", "\n", "\n", "demo = gr.Interface(\n", " main_note,\n", " gr.Audio(source=\"microphone\"),\n", " gr.Label(num_top_classes=4),\n", " examples=[\n", " [os.path.join(os.path.abspath(''),\"audio/recording1.wav\")],\n", " [os.path.join(os.path.abspath(''),\"audio/cantina.wav\")],\n", " ],\n", " interpretation=\"default\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/main_note/run.py
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@ -23,7 +23,6 @@ def main_note(audio):
y = y.T[0]
N = len(y)
T = 1.0 / rate
x = np.linspace(0.0, N * T, N)
yf = fft(y)
yf2 = 2.0 / N * np.abs(yf[0 : N // 2])
xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)

2
demo/map_airbnb/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: map_airbnb\n", "### Display an interactive map of AirBnB locations with Plotly. Data is hosted on HuggingFace Datasets. \n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.graph_objects as go\n", "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\n", "df = dataset.to_pandas()\n", "\n", "def filter_map(min_price, max_price, boroughs):\n", "\n", " filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & \n", " (df['price'] > min_price) & (df['price'] < max_price)]\n", " names = filtered_df[\"name\"].tolist()\n", " prices = filtered_df[\"price\"].tolist()\n", " text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n", " fig = go.Figure(go.Scattermapbox(\n", " customdata=text_list,\n", " lat=filtered_df['latitude'].tolist(),\n", " lon=filtered_df['longitude'].tolist(),\n", " mode='markers',\n", " marker=go.scattermapbox.Marker(\n", " size=6\n", " ),\n", " hoverinfo=\"text\",\n", " hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'\n", " ))\n", "\n", " fig.update_layout(\n", " mapbox_style=\"open-street-map\",\n", " hovermode='closest',\n", " mapbox=dict(\n", " bearing=0,\n", " center=go.layout.mapbox.Center(\n", " lat=40.67,\n", " lon=-73.90\n", " ),\n", " pitch=0,\n", " zoom=9\n", " ),\n", " )\n", "\n", " return fig\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " with gr.Row():\n", " min_price = gr.Number(value=250, label=\"Minimum Price\")\n", " max_price = gr.Number(value=1000, label=\"Maximum Price\")\n", " boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n", " btn = gr.Button(value=\"Update Filter\")\n", " map = gr.Plot().style()\n", " demo.load(filter_map, [min_price, max_price, boroughs], map)\n", " btn.click(filter_map, [min_price, max_price, boroughs], map)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: map_airbnb\n", "### Display an interactive map of AirBnB locations with Plotly. Data is hosted on HuggingFace Datasets. \n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import plotly.graph_objects as go\n", "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\n", "df = dataset.to_pandas()\n", "\n", "def filter_map(min_price, max_price, boroughs):\n", "\n", " filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & \n", " (df['price'] > min_price) & (df['price'] < max_price)]\n", " names = filtered_df[\"name\"].tolist()\n", " prices = filtered_df[\"price\"].tolist()\n", " text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n", " fig = go.Figure(go.Scattermapbox(\n", " customdata=text_list,\n", " lat=filtered_df['latitude'].tolist(),\n", " lon=filtered_df['longitude'].tolist(),\n", " mode='markers',\n", " marker=go.scattermapbox.Marker(\n", " size=6\n", " ),\n", " hoverinfo=\"text\",\n", " hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'\n", " ))\n", "\n", " fig.update_layout(\n", " mapbox_style=\"open-street-map\",\n", " hovermode='closest',\n", " mapbox=dict(\n", " bearing=0,\n", " center=go.layout.mapbox.Center(\n", " lat=40.67,\n", " lon=-73.90\n", " ),\n", " pitch=0,\n", " zoom=9\n", " ),\n", " )\n", "\n", " return fig\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " with gr.Row():\n", " min_price = gr.Number(value=250, label=\"Minimum Price\")\n", " max_price = gr.Number(value=1000, label=\"Maximum Price\")\n", " boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n", " btn = gr.Button(value=\"Update Filter\")\n", " map = gr.Plot().style()\n", " demo.load(filter_map, [min_price, max_price, boroughs], map)\n", " btn.click(filter_map, [min_price, max_price, boroughs], map)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/map_airbnb/run.py
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@ -1,5 +1,4 @@
import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from datasets import load_dataset

2
demo/musical_instrument_identification/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: musical_instrument_identification\n", "### This demo identifies musical instruments from an audio file. It uses Gradio's Audio and Label components.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 librosa==0.9.2 gdown"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/musical_instrument_identification/data_setups.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch, torchaudio\n", "from timeit import default_timer as timer\n", "from data_setups import audio_preprocess, resample\n", "import gdown\n", "\n", "url = 'https://drive.google.com/uc?id=1X5CR18u0I-ZOi_8P0cNptCe5JGk9Ro0C'\n", "output = 'piano.wav'\n", "gdown.download(url, output, quiet=False)\n", "url = 'https://drive.google.com/uc?id=1W-8HwmGR5SiyDbUcGAZYYDKdCIst07__'\n", "output= 'torch_efficientnet_fold2_CNN.pth'\n", "gdown.download(url, output, quiet=False)\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "SAMPLE_RATE = 44100\n", "AUDIO_LEN = 2.90\n", "model = torch.load(\"torch_efficientnet_fold2_CNN.pth\", map_location=torch.device('cpu'))\n", "LABELS = [\n", " \"Cello\", \"Clarinet\", \"Flute\", \"Acoustic Guitar\", \"Electric Guitar\", \"Organ\", \"Piano\", \"Saxophone\", \"Trumpet\", \"Violin\", \"Voice\"\n", "]\n", "example_list = [\n", " [\"piano.wav\"]\n", "]\n", "\n", "\n", "def predict(audio_path):\n", " start_time = timer()\n", " wavform, sample_rate = torchaudio.load(audio_path)\n", " wav = resample(wavform, sample_rate, SAMPLE_RATE)\n", " if len(wav) > int(AUDIO_LEN * SAMPLE_RATE):\n", " wav = wav[:int(AUDIO_LEN * SAMPLE_RATE)]\n", " else:\n", " print(f\"input length {len(wav)} too small!, need over {int(AUDIO_LEN * SAMPLE_RATE)}\")\n", " return\n", " img = audio_preprocess(wav, SAMPLE_RATE).unsqueeze(0)\n", " model.eval()\n", " with torch.inference_mode():\n", " pred_probs = torch.softmax(model(img), dim=1)\n", " pred_labels_and_probs = {LABELS[i]: float(pred_probs[0][i]) for i in range(len(LABELS))}\n", " pred_time = round(timer() - start_time, 5)\n", " return pred_labels_and_probs, pred_time\n", "\n", "demo = gr.Interface(fn=predict,\n", " inputs=gr.Audio(type=\"filepath\"),\n", " outputs=[gr.Label(num_top_classes=11, label=\"Predictions\"), \n", " gr.Number(label=\"Prediction time (s)\")],\n", " examples=example_list,\n", " cache_examples=False\n", " )\n", "\n", "demo.launch(debug=False)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: musical_instrument_identification\n", "### This demo identifies musical instruments from an audio file. It uses Gradio's Audio and Label components.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 librosa==0.9.2 gdown"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/musical_instrument_identification/data_setups.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import torchaudio\n", "from timeit import default_timer as timer\n", "from data_setups import audio_preprocess, resample\n", "import gdown\n", "\n", "url = 'https://drive.google.com/uc?id=1X5CR18u0I-ZOi_8P0cNptCe5JGk9Ro0C'\n", "output = 'piano.wav'\n", "gdown.download(url, output, quiet=False)\n", "url = 'https://drive.google.com/uc?id=1W-8HwmGR5SiyDbUcGAZYYDKdCIst07__'\n", "output= 'torch_efficientnet_fold2_CNN.pth'\n", "gdown.download(url, output, quiet=False)\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "SAMPLE_RATE = 44100\n", "AUDIO_LEN = 2.90\n", "model = torch.load(\"torch_efficientnet_fold2_CNN.pth\", map_location=torch.device('cpu'))\n", "LABELS = [\n", " \"Cello\", \"Clarinet\", \"Flute\", \"Acoustic Guitar\", \"Electric Guitar\", \"Organ\", \"Piano\", \"Saxophone\", \"Trumpet\", \"Violin\", \"Voice\"\n", "]\n", "example_list = [\n", " [\"piano.wav\"]\n", "]\n", "\n", "\n", "def predict(audio_path):\n", " start_time = timer()\n", " wavform, sample_rate = torchaudio.load(audio_path)\n", " wav = resample(wavform, sample_rate, SAMPLE_RATE)\n", " if len(wav) > int(AUDIO_LEN * SAMPLE_RATE):\n", " wav = wav[:int(AUDIO_LEN * SAMPLE_RATE)]\n", " else:\n", " print(f\"input length {len(wav)} too small!, need over {int(AUDIO_LEN * SAMPLE_RATE)}\")\n", " return\n", " img = audio_preprocess(wav, SAMPLE_RATE).unsqueeze(0)\n", " model.eval()\n", " with torch.inference_mode():\n", " pred_probs = torch.softmax(model(img), dim=1)\n", " pred_labels_and_probs = {LABELS[i]: float(pred_probs[0][i]) for i in range(len(LABELS))}\n", " pred_time = round(timer() - start_time, 5)\n", " return pred_labels_and_probs, pred_time\n", "\n", "demo = gr.Interface(fn=predict,\n", " inputs=gr.Audio(type=\"filepath\"),\n", " outputs=[gr.Label(num_top_classes=11, label=\"Predictions\"), \n", " gr.Number(label=\"Prediction time (s)\")],\n", " examples=example_list,\n", " cache_examples=False\n", " )\n", "\n", "demo.launch(debug=False)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

5
demo/musical_instrument_identification/run.py
View File

@ -1,5 +1,6 @@
import gradio as gr
import torch, torchaudio
import torch
import torchaudio
from timeit import default_timer as timer
from data_setups import audio_preprocess, resample
import gdown
@ -47,4 +48,4 @@ demo = gr.Interface(fn=predict,
cache_examples=False
)
demo.launch(debug=False)
demo.launch(debug=False)

2
demo/progress/run.ipynb
View File

@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: progress"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tqdm datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "import tqdm\n", "from datasets import load_dataset\n", "import shutil\n", "from uuid import uuid4\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " text = gr.Textbox()\n", " textb = gr.Textbox()\n", " with gr.Row():\n", " load_set_btn = gr.Button(\"Load Set\")\n", " load_nested_set_btn = gr.Button(\"Load Nested Set\")\n", " load_random_btn = gr.Button(\"Load Random\")\n", " clean_imgs_btn = gr.Button(\"Clean Images\")\n", " wait_btn = gr.Button(\"Wait\")\n", " do_all_btn = gr.Button(\"Do All\")\n", " track_tqdm_btn = gr.Button(\"Bind TQDM\")\n", " bind_internal_tqdm_btn = gr.Button(\"Bind Internal TQDM\")\n", "\n", " text2 = gr.Textbox()\n", "\n", " # track list\n", " def load_set(text, text2, progress=gr.Progress()):\n", " imgs = [None] * 24\n", " for img in progress.tqdm(imgs, desc=\"Loading from list\"):\n", " time.sleep(0.1)\n", " return \"done\"\n", " load_set_btn.click(load_set, [text, textb], text2)\n", "\n", " # track nested list\n", " def load_nested_set(text, text2, progress=gr.Progress()):\n", " imgs = [[None] * 8] * 3\n", " for img_set in progress.tqdm(imgs, desc=\"Nested list\"):\n", " time.sleep(2)\n", " for img in progress.tqdm(img_set, desc=\"inner list\"):\n", " time.sleep(0.1)\n", " return \"done\"\n", " load_nested_set_btn.click(load_nested_set, [text, textb], text2)\n", "\n", " # track iterable of unknown length\n", " def load_random(data, progress=gr.Progress()):\n", " def yielder():\n", " for i in range(0, random.randint(15, 20)):\n", " time.sleep(0.1)\n", " yield None\n", " for img in progress.tqdm(yielder()):\n", " pass\n", " return \"done\"\n", " load_random_btn.click(load_random, {text, textb}, text2)\n", " \n", " # manual progress\n", " def clean_imgs(text, progress=gr.Progress()):\n", " progress(0.2, desc=\"Collecting Images\")\n", " time.sleep(1)\n", " progress(0.5, desc=\"Cleaning Images\")\n", " time.sleep(1.5)\n", " progress(0.8, desc=\"Sending Images\")\n", " time.sleep(1.5)\n", " return \"done\"\n", " clean_imgs_btn.click(clean_imgs, text, text2)\n", "\n", " # no progress\n", " def wait(text):\n", " time.sleep(4)\n", " return \"done\"\n", " wait_btn.click(wait, text, text2)\n", "\n", " # multiple progressions\n", " def do_all(data, progress=gr.Progress()):\n", " load_set(data[text], data[textb], progress)\n", " load_random(data, progress)\n", " clean_imgs(data[text], progress)\n", " progress(None)\n", " wait(text)\n", " return \"done\"\n", " do_all_btn.click(do_all, {text, textb}, text2)\n", "\n", " def track_tqdm(data, progress=gr.Progress(track_tqdm=True)):\n", " for i in tqdm.tqdm(range(5), desc=\"outer\"):\n", " for j in tqdm.tqdm(range(4), desc=\"inner\"):\n", " time.sleep(1)\n", " return \"done\"\n", " track_tqdm_btn.click(track_tqdm, {text, textb}, text2)\n", "\n", " def bind_internal_tqdm(data, progress=gr.Progress(track_tqdm=True)):\n", " outdir = \"__tmp/\" + str(uuid4())\n", " dataset = load_dataset(\"beans\", split=\"train\", cache_dir=outdir)\n", " shutil.rmtree(outdir)\n", " return \"done\"\n", " bind_internal_tqdm_btn.click(bind_internal_tqdm, {text, textb}, text2)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue(concurrency_count=20).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: progress"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tqdm datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "import tqdm\n", "from datasets import load_dataset\n", "import shutil\n", "from uuid import uuid4\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " text = gr.Textbox()\n", " textb = gr.Textbox()\n", " with gr.Row():\n", " load_set_btn = gr.Button(\"Load Set\")\n", " load_nested_set_btn = gr.Button(\"Load Nested Set\")\n", " load_random_btn = gr.Button(\"Load Random\")\n", " clean_imgs_btn = gr.Button(\"Clean Images\")\n", " wait_btn = gr.Button(\"Wait\")\n", " do_all_btn = gr.Button(\"Do All\")\n", " track_tqdm_btn = gr.Button(\"Bind TQDM\")\n", " bind_internal_tqdm_btn = gr.Button(\"Bind Internal TQDM\")\n", "\n", " text2 = gr.Textbox()\n", "\n", " # track list\n", " def load_set(text, text2, progress=gr.Progress()):\n", " imgs = [None] * 24\n", " for img in progress.tqdm(imgs, desc=\"Loading from list\"):\n", " time.sleep(0.1)\n", " return \"done\"\n", " load_set_btn.click(load_set, [text, textb], text2)\n", "\n", " # track nested list\n", " def load_nested_set(text, text2, progress=gr.Progress()):\n", " imgs = [[None] * 8] * 3\n", " for img_set in progress.tqdm(imgs, desc=\"Nested list\"):\n", " time.sleep(2)\n", " for img in progress.tqdm(img_set, desc=\"inner list\"):\n", " time.sleep(0.1)\n", " return \"done\"\n", " load_nested_set_btn.click(load_nested_set, [text, textb], text2)\n", "\n", " # track iterable of unknown length\n", " def load_random(data, progress=gr.Progress()):\n", " def yielder():\n", " for i in range(0, random.randint(15, 20)):\n", " time.sleep(0.1)\n", " yield None\n", " for img in progress.tqdm(yielder()):\n", " pass\n", " return \"done\"\n", " load_random_btn.click(load_random, {text, textb}, text2)\n", " \n", " # manual progress\n", " def clean_imgs(text, progress=gr.Progress()):\n", " progress(0.2, desc=\"Collecting Images\")\n", " time.sleep(1)\n", " progress(0.5, desc=\"Cleaning Images\")\n", " time.sleep(1.5)\n", " progress(0.8, desc=\"Sending Images\")\n", " time.sleep(1.5)\n", " return \"done\"\n", " clean_imgs_btn.click(clean_imgs, text, text2)\n", "\n", " # no progress\n", " def wait(text):\n", " time.sleep(4)\n", " return \"done\"\n", " wait_btn.click(wait, text, text2)\n", "\n", " # multiple progressions\n", " def do_all(data, progress=gr.Progress()):\n", " load_set(data[text], data[textb], progress)\n", " load_random(data, progress)\n", " clean_imgs(data[text], progress)\n", " progress(None)\n", " wait(text)\n", " return \"done\"\n", " do_all_btn.click(do_all, {text, textb}, text2)\n", "\n", " def track_tqdm(data, progress=gr.Progress(track_tqdm=True)):\n", " for i in tqdm.tqdm(range(5), desc=\"outer\"):\n", " for j in tqdm.tqdm(range(4), desc=\"inner\"):\n", " time.sleep(1)\n", " return \"done\"\n", " track_tqdm_btn.click(track_tqdm, {text, textb}, text2)\n", "\n", " def bind_internal_tqdm(data, progress=gr.Progress(track_tqdm=True)):\n", " outdir = \"__tmp/\" + str(uuid4())\n", " load_dataset(\"beans\", split=\"train\", cache_dir=outdir)\n", " shutil.rmtree(outdir)\n", " return \"done\"\n", " bind_internal_tqdm_btn.click(bind_internal_tqdm, {text, textb}, text2)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue(concurrency_count=20).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

2
demo/progress/run.py
View File

@ -87,7 +87,7 @@ with gr.Blocks() as demo:
def bind_internal_tqdm(data, progress=gr.Progress(track_tqdm=True)):
outdir = "__tmp/" + str(uuid4())
dataset = load_dataset("beans", split="train", cache_dir=outdir)
load_dataset("beans", split="train", cache_dir=outdir)
shutil.rmtree(outdir)
return "done"
bind_internal_tqdm_btn.click(bind_internal_tqdm, {text, textb}, text2)

2
demo/progress_component/run.ipynb
View File

@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: progress_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tqdm"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time \n", "import tqdm\n", "\n", "css = \"footer {display: none !important;} .gradio-container {min-height: 0px !important;}\"\n", "\n", "def load_set(progress=gr.Progress()):\n", " imgs = [None] * 24\n", " for img in progress.tqdm(imgs, desc=\"Loading...\"):\n", " time.sleep(0.1)\n", " return \"Loaded\"\n", "\n", "\n", "with gr.Blocks(css=css) as demo:\n", " load = gr.Button(\"Load\")\n", " label = gr.Label(label=\"Loader\")\n", " load.click(load_set, outputs=label)\n", "\n", "demo.queue(concurrency_count=20).launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: progress_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tqdm"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time \n", "\n", "css = \"footer {display: none !important;} .gradio-container {min-height: 0px !important;}\"\n", "\n", "def load_set(progress=gr.Progress()):\n", " imgs = [None] * 24\n", " for img in progress.tqdm(imgs, desc=\"Loading...\"):\n", " time.sleep(0.1)\n", " return \"Loaded\"\n", "\n", "\n", "with gr.Blocks(css=css) as demo:\n", " load = gr.Button(\"Load\")\n", " label = gr.Label(label=\"Loader\")\n", " load.click(load_set, outputs=label)\n", "\n", "demo.queue(concurrency_count=20).launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/progress_component/run.py
View File

@ -1,6 +1,5 @@
import gradio as gr
import time
import tqdm
css = "footer {display: none !important;} .gradio-container {min-height: 0px !important;}"

2
demo/stable-diffusion/run.ipynb
View File

@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stable-diffusion\n", "### Note: This is a simplified version of the code needed to create the Stable Diffusion demo. See full code here: https://hf.co/spaces/stabilityai/stable-diffusion/tree/main\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio diffusers transformers nvidia-ml-py3 ftfy torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from torch import autocast\n", "from diffusers import StableDiffusionPipeline\n", "from datasets import load_dataset\n", "from PIL import Image \n", "import re\n", "import os\n", "\n", "auth_token = os.getenv(\"auth_token\")\n", "model_id = \"CompVis/stable-diffusion-v1-4\"\n", "device = \"cpu\"\n", "pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=auth_token, revision=\"fp16\", torch_dtype=torch.float16)\n", "pipe = pipe.to(device)\n", "\n", "def infer(prompt, samples, steps, scale, seed): \n", " generator = torch.Generator(device=device).manual_seed(seed)\n", " images_list = pipe(\n", " [prompt] * samples,\n", " num_inference_steps=steps,\n", " guidance_scale=scale,\n", " generator=generator,\n", " )\n", " images = []\n", " safe_image = Image.open(r\"unsafe.png\")\n", " for i, image in enumerate(images_list[\"sample\"]):\n", " if(images_list[\"nsfw_content_detected\"][i]):\n", " images.append(safe_image)\n", " else:\n", " images.append(image)\n", " return images\n", " \n", "\n", "\n", "block = gr.Blocks()\n", "\n", "with block:\n", " with gr.Group():\n", " with gr.Box():\n", " with gr.Row().style(mobile_collapse=False, equal_height=True):\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " show_label=False,\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " ).style(\n", " border=(True, False, True, True),\n", " rounded=(True, False, False, True),\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\").style(\n", " margin=False,\n", " rounded=(False, True, True, False),\n", " )\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " ).style(grid=[2], height=\"auto\")\n", "\n", " advanced_button = gr.Button(\"Advanced options\", elem_id=\"advanced-btn\")\n", "\n", " with gr.Row(elem_id=\"advanced-options\"):\n", " samples = gr.Slider(label=\"Images\", minimum=1, maximum=4, value=4, step=1)\n", " steps = gr.Slider(label=\"Steps\", minimum=1, maximum=50, value=45, step=1)\n", " scale = gr.Slider(\n", " label=\"Guidance Scale\", minimum=0, maximum=50, value=7.5, step=0.1\n", " )\n", " seed = gr.Slider(\n", " label=\"Seed\",\n", " minimum=0,\n", " maximum=2147483647,\n", " step=1,\n", " randomize=True,\n", " )\n", " text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " advanced_button.click(\n", " None,\n", " [],\n", " text,\n", " )\n", " \n", "block.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stable-diffusion\n", "### Note: This is a simplified version of the code needed to create the Stable Diffusion demo. See full code here: https://hf.co/spaces/stabilityai/stable-diffusion/tree/main\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio diffusers transformers nvidia-ml-py3 ftfy torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from diffusers import StableDiffusionPipeline\n", "from PIL import Image \n", "import os\n", "\n", "auth_token = os.getenv(\"auth_token\")\n", "model_id = \"CompVis/stable-diffusion-v1-4\"\n", "device = \"cpu\"\n", "pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=auth_token, revision=\"fp16\", torch_dtype=torch.float16)\n", "pipe = pipe.to(device)\n", "\n", "def infer(prompt, samples, steps, scale, seed): \n", " generator = torch.Generator(device=device).manual_seed(seed)\n", " images_list = pipe(\n", " [prompt] * samples,\n", " num_inference_steps=steps,\n", " guidance_scale=scale,\n", " generator=generator,\n", " )\n", " images = []\n", " safe_image = Image.open(r\"unsafe.png\")\n", " for i, image in enumerate(images_list[\"sample\"]):\n", " if(images_list[\"nsfw_content_detected\"][i]):\n", " images.append(safe_image)\n", " else:\n", " images.append(image)\n", " return images\n", " \n", "\n", "\n", "block = gr.Blocks()\n", "\n", "with block:\n", " with gr.Group():\n", " with gr.Box():\n", " with gr.Row().style(mobile_collapse=False, equal_height=True):\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " show_label=False,\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " ).style(\n", " border=(True, False, True, True),\n", " rounded=(True, False, False, True),\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\").style(\n", " margin=False,\n", " rounded=(False, True, True, False),\n", " )\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " ).style(grid=[2], height=\"auto\")\n", "\n", " advanced_button = gr.Button(\"Advanced options\", elem_id=\"advanced-btn\")\n", "\n", " with gr.Row(elem_id=\"advanced-options\"):\n", " samples = gr.Slider(label=\"Images\", minimum=1, maximum=4, value=4, step=1)\n", " steps = gr.Slider(label=\"Steps\", minimum=1, maximum=50, value=45, step=1)\n", " scale = gr.Slider(\n", " label=\"Guidance Scale\", minimum=0, maximum=50, value=7.5, step=0.1\n", " )\n", " seed = gr.Slider(\n", " label=\"Seed\",\n", " minimum=0,\n", " maximum=2147483647,\n", " step=1,\n", " randomize=True,\n", " )\n", " text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " advanced_button.click(\n", " None,\n", " [],\n", " text,\n", " )\n", " \n", "block.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

3
demo/stable-diffusion/run.py
View File

@ -1,10 +1,7 @@
import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
from datasets import load_dataset
from PIL import Image
import re
import os
auth_token = os.getenv("auth_token")

2
demo/webcam/run.ipynb
View File

@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: webcam"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def snap(image, video):\n", " return [image, video]\n", "\n", "\n", "demo = gr.Interface(\n", " snap,\n", " [gr.Image(source=\"webcam\", tool=None), gr.Video(source=\"webcam\")],\n", " [\"image\", \"video\"],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: webcam"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def snap(image, video):\n", " return [image, video]\n", "\n", "\n", "demo = gr.Interface(\n", " snap,\n", " [gr.Image(source=\"webcam\", tool=None), gr.Video(source=\"webcam\")],\n", " [\"image\", \"video\"],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

1
demo/webcam/run.py
View File

@ -1,4 +1,3 @@
import numpy as np
import gradio as gr

7
gradio/__init__.py
View File

@ -16,8 +16,8 @@ from gradio.components import (
Carousel,
Chatbot,
Checkbox,
Checkboxgroup,
CheckboxGroup,
Checkboxgroup,
Code,
ColorPicker,
DataFrame,
@ -27,8 +27,8 @@ from gradio.components import (
File,
Gallery,
Highlight,
Highlightedtext,
HighlightedText,
Highlightedtext,
Image,
Interpretation,
Json,
@ -62,9 +62,8 @@ from gradio.flagging import (
HuggingFaceDatasetSaver,
SimpleCSVLogger,
)
from gradio.helpers import EventData, Progress
from gradio.helpers import EventData, Progress, make_waveform, skip, update
from gradio.helpers import create_examples as Examples
from gradio.helpers import make_waveform, skip, update
from gradio.interface import Interface, TabbedInterface, close_all
from gradio.ipython_ext import load_ipython_extension
from gradio.layouts import Accordion, Box, Column, Group, Row, Tab, TabItem, Tabs

12
gradio/components.py
View File

@ -887,7 +887,6 @@ class Slider(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -1019,7 +1018,6 @@ class Checkbox(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -1140,7 +1138,6 @@ class CheckboxGroup(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -1322,7 +1319,6 @@ class Radio(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -1511,7 +1507,6 @@ class Dropdown(
"choices": choices,
"label": label,
"show_label": show_label,
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
@ -1707,7 +1702,6 @@ class Image(
"visible": visible,
"value": value,
"brush_radius": brush_radius,
"interactive": interactive,
"__type__": "update",
}
@ -2054,7 +2048,6 @@ class Video(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -2274,7 +2267,6 @@ class Audio(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -2591,7 +2583,6 @@ class File(
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -2856,7 +2847,6 @@ class Dataframe(Changeable, Selectable, IOComponent, JSONSerializable):
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -3073,7 +3063,6 @@ class Timeseries(Changeable, IOComponent, JSONSerializable):
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}
@ -3331,7 +3320,6 @@ class UploadButton(Clickable, Uploadable, IOComponent, FileSerializable):
"interactive": interactive,
"visible": visible,
"value": value,
"interactive": interactive,
"__type__": "update",
}

2
gradio/external.py
View File

@ -344,7 +344,7 @@ def from_model(model_name: str, api_key: str | None, alias: str | None, **kwargs
"examples": example_data,
}
if p is None or not (p in pipelines):
if p is None or p not in pipelines:
raise ValueError("Unsupported pipeline type: {}".format(p))
pipeline = pipelines[p]

2
gradio/helpers.py
View File

@ -172,7 +172,7 @@ class Examples:
input_has_examples = [False] * len(inputs)
for example in examples:
for idx, example_for_input in enumerate(example):
if not (example_for_input is None):
if example_for_input is not None:
try:
input_has_examples[idx] = True
except IndexError:

2
gradio/queueing.py
View File

@ -122,7 +122,7 @@ class Queue:
await asyncio.sleep(self.sleep_when_free)
continue
if not (None in self.active_jobs):
if None not in self.active_jobs:
await asyncio.sleep(self.sleep_when_free)
continue
# Using mutex to avoid editing a list in use

4
gradio/routes.py
View File

@ -160,7 +160,7 @@ class App(FastAPI):
@app.get("/login_check")
@app.get("/login_check/")
def login_check(user: str = Depends(get_current_user)):
if app.auth is None or not (user is None):
if app.auth is None or user is not None:
return
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED, detail="Not authenticated"
@ -219,7 +219,7 @@ class App(FastAPI):
mimetypes.add_type("application/javascript", ".js")
blocks = app.get_blocks()
if app.auth is None or not (user is None):
if app.auth is None or user is not None:
config = app.get_blocks().config
else:
config = {

1
gradio/utils.py
View File

@ -187,6 +187,7 @@ def launched_telemetry(blocks: gradio.Blocks, data: Dict[str, Any]) -> None:
)
except Exception:
pass
threading.Thread(target=launched_telemtry_thread, args=(data,)).start()

24
pyproject.toml
View File

@ -60,3 +60,27 @@ include = [
"/README.md",
"/requirements.txt",
]
[tool.ruff]
target-version = "py37"
extend-select = [
"I",
]
ignore = [
"E501", # from scripts/lint_backend.sh
"E722", # from scripts/lint_backend.sh
"E731", # from scripts/lint_backend.sh
"F403", # from scripts/lint_backend.sh
"F541", # from scripts/lint_backend.sh
]
[tool.ruff.per-file-ignores]
"demo/*" = [
"E402", # Demos may have imports not at the top
"E741", # Demos may have ambiguous variable names
"F405", # Demos may use star imports
"I", # Don't care about import order
]
"gradio/__init__.py" = [
"F401", # "Imported but unused" (TODO: it would be better to be explicit and use __all__)
]

16
scripts/benchmark_queue.py
View File

@ -16,15 +16,17 @@ to with the -o parameter:
>> python scripts/benchmark_queue.py -n 1000 -o results.json
'''
import argparse
import asyncio
import json
import random
import time
import pandas as pd
import websockets
import gradio as gr
from gradio import media_data
import asyncio
import websockets
import json
import time
import random
import pandas as pd
import argparse
def identity_with_sleep(x):

4
scripts/check_pypi.py
View File

@ -1,6 +1,6 @@
import urllib.request
import json
import json
import sys
import urllib.request
from pathlib import Path
root_directory = Path(__file__).parent.parent

4
scripts/copy_demos.py
View File

@ -1,8 +1,8 @@
import shutil
import argparse
import os
import pathlib
import shutil
import textwrap
import argparse
def copy_all_demos(source_dir: str, dest_dir: str):

1
scripts/download_artifacts.py
View File

@ -1,4 +1,5 @@
import argparse
import requests
WORKFLOW_RUN_ENDPOINT = "https://api.github.com/repos/{owner}/{repo}/actions/runs/{run_id}/artifacts"

5
scripts/format_backend.sh
View File

@ -3,8 +3,7 @@
cd "$(dirname ${0})/.."
echo "Formatting the backend... Our style follows the Black code style."
python -m black gradio test
python -m isort --profile=black gradio test
python -m flake8 --ignore=E731,E501,E722,W503,E126,E203,F403 gradio test --exclude gradio/__init__.py
ruff gradio test
black gradio test
bash client/python/scripts/format.sh # Call the client library's formatting script

2
scripts/format_release_notes.py
View File

@ -1,5 +1,5 @@
import pathlib
import argparse
import pathlib
import textwrap
current_dir = (pathlib.Path(__file__).parent / "..").resolve()

6
scripts/lint_backend.sh
View File

@ -1,7 +1,5 @@
#!/bin/bash
cd "$(dirname ${0})/.."
python -m black --check gradio test client/python/gradio_client
python -m isort --profile=black --check-only gradio test client/python/gradio_client
python -m flake8 --ignore=E731,E501,E722,W503,E126,E203,F403,F541 gradio test client/python/gradio_client --exclude gradio/__init__.py,client/python/gradio_client/__init__.py
ruff gradio test client
black --check gradio test client

13
test/requirements-37.txt
View File

@ -51,8 +51,6 @@ filelock==3.7.1
# via
# huggingface-hub
# transformers
flake8==4.0.1
# via -r requirements.in
h11==0.12.0
# via httpcore
httpcore==0.15.0
@ -75,7 +73,6 @@ imageio==2.19.5
importlib-metadata==4.2.0
# via
# click
# flake8
# huggingface-hub
# jsonschema
# pluggy
@ -87,8 +84,6 @@ iniconfig==1.1.1
# via pytest
ipython==7.34.0
# via -r requirements.in
isort==5.10.1
# via -r requirements.in
jedi==0.18.1
# via ipython
jinja2==3.1.2
@ -107,8 +102,6 @@ markupsafe==2.1.1
# via jinja2
matplotlib-inline==0.1.3
# via ipython
mccabe==0.6.1
# via flake8
mypy-extensions==0.4.3
# via black
networkx==2.6.3
@ -162,14 +155,10 @@ ptyprocess==0.7.0
# via pexpect
py==1.11.0
# via pytest
pycodestyle==2.8.0
# via flake8
pydantic==1.9.1
# via
# -r requirements.in
# fastapi
pyflakes==2.4.0
# via flake8
pygments==2.12.0
# via ipython
pyparsing==3.0.9
@ -207,6 +196,8 @@ respx==0.19.2
# via -r requirements.in
rfc3986[idna2008]==1.5.0
# via httpx
ruff==0.0.260
# via -r requirements.in
s3transfer==0.6.0
# via boto3
scikit-image==0.19.3

33
test/requirements.in
View File

@ -1,23 +1,22 @@
# Don't forget to run bash scripts/create_test_requirements.sh and scripts/create_test_requirements-37.sh from unix or wsl when you update this file.
asyncio
boto3
IPython
altair
asyncio
black
boto3
coverage
torch
transformers
fastapi>=0.87.0
httpx
huggingface_hub
pydantic
pytest
pytest-asyncio
pytest-cov
ruff>=0.0.260
respx
scikit-image
shap
pytest
huggingface_hub
pytest-cov
pytest-asyncio
black
isort
flake8
httpx
pydantic
respx
fastapi>=0.87.0
altair
torch
tqdm
transformers
vega_datasets
tqdm

12
test/requirements.txt
View File

@ -51,8 +51,6 @@ filelock==3.7.1
# via
# huggingface-hub
# transformers
flake8==4.0.1
# via -r requirements.in
h11==0.12.0
# via httpcore
httpcore==0.15.0
@ -76,8 +74,6 @@ iniconfig==1.1.1
# via pytest
ipython==7.34.0
# via -r requirements.in
isort==5.10.1
# via -r requirements.in
jedi==0.18.1
# via ipython
jinja2==3.1.2
@ -96,8 +92,6 @@ markupsafe==2.1.1
# via jinja2
matplotlib-inline==0.1.3
# via ipython
mccabe==0.6.1
# via flake8
mypy-extensions==0.4.3
# via black
networkx==2.6.3
@ -151,14 +145,10 @@ ptyprocess==0.7.0
# via pexpect
py==1.11.0
# via pytest
pycodestyle==2.8.0
# via flake8
pydantic==1.9.1
# via
# -r requirements.in
# fastapi
pyflakes==2.4.0
# via flake8
pygments==2.12.0
# via ipython
pyparsing==3.0.9
@ -194,6 +184,8 @@ requests==2.28.1
# transformers
respx==0.19.2
# via -r requirements.in
ruff==0.0.260
# via -r requirements.in
rfc3986[idna2008]==1.5.0
# via httpx
s3transfer==0.6.0

4
test/test_blocks.py
View File

@ -685,9 +685,7 @@ class TestCallFunction:
class TestBatchProcessing:
def test_raise_exception_if_batching_an_event_thats_not_queued(self):
def trim(words, lens):
trimmed_words = []
for w, l in zip(words, lens):
trimmed_words.append(w[: int(l)])
trimmed_words = [word[: int(length)] for word, length in zip(words, lens)]
return [trimmed_words]
msg = "In order to use batching, the queue must be enabled."

8
test/test_examples.py
View File

@ -258,9 +258,7 @@ class TestProcessExamples:
@pytest.mark.asyncio
async def test_caching_with_batch(self):
def trim_words(words, lens):
trimmed_words = []
for w, l in zip(words, lens):
trimmed_words.append(w[:l])
trimmed_words = [word[:length] for word, length in zip(words, lens)]
return [trimmed_words]
io = gr.Interface(
@ -278,9 +276,7 @@ class TestProcessExamples:
@pytest.mark.asyncio
async def test_caching_with_batch_multiple_outputs(self):
def trim_words(words, lens):
trimmed_words = []
for w, l in zip(words, lens):
trimmed_words.append(w[:l])
trimmed_words = [word[:length] for word, length in zip(words, lens)]
return trimmed_words, lens
io = gr.Interface(

5
website/check_version.py
View File

@ -1,8 +1,7 @@
import time
import requests
import warnings
import os
import sys
import time
from homepage.utils import get_latest_stable
VERSION_TXT = os.path.abspath(os.path.join(os.getcwd(), "..", "gradio", "version.txt"))

4
website/homepage/build-version-docs.py
View File

@ -1,8 +1,8 @@
import os
import shutil
import jinja2
from src import index, guides, docs, demos, changelog
import requests
from src import docs
from utils import get_latest_stable
SRC_DIR = "src"

6
website/homepage/build.py
View File

@ -1,9 +1,9 @@
import argparse
import os
import shutil
import jinja2
from src import index, guides, docs, demos, changelog
import argparse
import requests
from src import changelog, demos, docs, guides, index
from utils import get_latest_stable
SRC_DIR = "src"

5
website/homepage/restart_demos.py
View File

@ -1,6 +1,7 @@
from upload_demos import demos, upload_demo_to_space, AUTH_TOKEN, latest_gradio_stable
from gradio.networking import url_ok
import huggingface_hub
from upload_demos import AUTH_TOKEN, demos, latest_gradio_stable, upload_demo_to_space
from gradio.networking import url_ok
for demo in demos:
space_id = "gradio/" + demo

4
website/homepage/src/changelog/__init__.py
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@ -1,8 +1,8 @@
import os
import markdown2
import shutil
import re
import markdown2
DIR = os.path.dirname(__file__)
INNER_TEMPLATE_FILE = os.path.join(DIR, "inner_template.html")
CHANGELOG_FILE = os.path.join(DIR, "..", "..", "..", "..", "CHANGELOG.md")

1
website/homepage/src/demos/__init__.py
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@ -1,5 +1,4 @@
import os
import json
GRADIO_DEMO_DIR = os.path.abspath(os.path.join(os.getcwd(), "..", "..", "demo"))
DIR = os.path.dirname(__file__)

5
website/homepage/src/docs/__init__.py
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@ -1,6 +1,8 @@
import os
from gradio.documentation import generate_documentation, document_cls
from gradio.documentation import document_cls, generate_documentation
from gradio.events import EventListener
from ..guides import guides
DIR = os.path.dirname(__file__)
@ -166,7 +168,6 @@ def build(output_dir, jinja_env, gradio_wheel_url, gradio_version):
def build_pip_template(version, jinja_env):
docs_files = os.listdir("src/docs")
template = jinja_env.get_template("docs/template.html")
output = template.render(
docs=docs, find_cls=find_cls, version="pip", gradio_version=version, canonical_suffix="", ordered_events=ordered_events

5
website/homepage/src/guides/__init__.py
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@ -1,7 +1,8 @@
import os
import markdown2
import shutil
import re
import shutil
import markdown2
DIR = os.path.dirname(__file__)
TEMPLATE_FILE = os.path.join(DIR, "template.html")

3
website/homepage/src/index/__init__.py
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@ -1,5 +1,6 @@
import os
import json
import os
import requests
DIR = os.path.dirname(__file__)

7
website/homepage/upload_demos.py
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@ -1,13 +1,12 @@
import argparse
import os
import pathlib
import shutil
import tempfile
import textwrap
from typing import Optional
import huggingface_hub
import os
import json
import argparse
import requests
from utils import get_latest_stable
AUTH_TOKEN = os.getenv("AUTH_TOKEN")

3
website/homepage/utils.py
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@ -1,4 +1,5 @@
import requests
import requests
def get_latest_stable():
return requests.get("https://pypi.org/pypi/gradio/json").json()["info"]["version"]