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Upgrade from numpy's Legacy Random Generator to Random Generator (#7397)

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* Update run.py

* replace all np.random.random with new generator code

* notebooks

---------

Co-authored-by: aroffe99 <ari.roffe@morningstar.com>
Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
This commit is contained in:
Ari Roffe 2024-02-12 12:20:46 -06:00 committed by GitHub
parent 0dccf825c4
commit 10951105b8
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GPG Key ID: B5690EEEBB952194
7 changed files with 12 additions and 8 deletions
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2
demo/dataframe_datatype/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_datatype"]}, {"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 pandas as pd\n", "import numpy as np\n", "\n", "\n", "def make_dataframe(n_periods):\n", " return pd.DataFrame({\"date_1\": pd.date_range(\"2021-01-01\", periods=n_periods),\n", " \"date_2\": pd.date_range(\"2022-02-15\", periods=n_periods).strftime('%B %d, %Y, %r'),\n", " \"number\": np.random.random(n_periods).astype(np.float64),\n", " \"number_2\": np.random.randint(0, 100, n_periods).astype(np.int32),\n", " \"bool\": [True] * n_periods,\n", " \"markdown\": [\"# Hello\"] * n_periods})\n", "\n", "\n", "demo = gr.Interface(make_dataframe,\n", " gr.Number(precision=0),\n", " gr.Dataframe(datatype=[\"date\", \"date\", \"number\", \"number\", \"bool\", \"markdown\"]))\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: dataframe_datatype"]}, {"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 pandas as pd\n", "import numpy as np\n", "\n", "\n", "def make_dataframe(n_periods):\n", " rng = np.random.default_rng()\n", " return pd.DataFrame({\"date_1\": pd.date_range(\"2021-01-01\", periods=n_periods),\n", " \"date_2\": pd.date_range(\"2022-02-15\", periods=n_periods).strftime('%B %d, %Y, %r'),\n", " \"number\": rng.random(n_periods).astype(np.float64),\n", " \"number_2\": rng.integers(0, 100, n_periods).astype(np.int32),\n", " \"bool\": [True] * n_periods,\n", " \"markdown\": [\"# Hello\"] * n_periods})\n", "\n", "\n", "demo = gr.Interface(make_dataframe,\n", " gr.Number(precision=0),\n", " gr.Dataframe(datatype=[\"date\", \"date\", \"number\", \"number\", \"bool\", \"markdown\"]))\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

5
demo/dataframe_datatype/run.py
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@ -4,10 +4,11 @@ import numpy as np
def make_dataframe(n_periods):
rng = np.random.default_rng()
return pd.DataFrame({"date_1": pd.date_range("2021-01-01", periods=n_periods),
"date_2": pd.date_range("2022-02-15", periods=n_periods).strftime('%B %d, %Y, %r'),
"number": np.random.random(n_periods).astype(np.float64),
"number_2": np.random.randint(0, 100, n_periods).astype(np.int32),
"number": rng.random(n_periods).astype(np.float64),
"number_2": rng.integers(0, 100, n_periods).astype(np.int32),
"bool": [True] * n_periods,
"markdown": ["# Hello"] * n_periods})

2
demo/fake_diffusion/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_diffusion\n", "### This demo uses a fake model to showcase iterative output. The Image output will update every time a generator is returned until the final image.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "def fake_diffusion(steps):\n", " for i in range(steps):\n", " time.sleep(1)\n", " image = np.random.random((600, 600, 3))\n", " yield image\n", " image = np.ones((1000,1000,3), np.uint8)\n", " image[:] = [255, 124, 0]\n", " yield image\n", "\n", "\n", "demo = gr.Interface(fake_diffusion,\n", " inputs=gr.Slider(1, 10, 3, step=1),\n", " outputs=\"image\")\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_diffusion\n", "### This demo uses a fake model to showcase iterative output. The Image output will update every time a generator is returned until the final image.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "def fake_diffusion(steps):\n", " rng = np.random.default_rng()\n", " for i in range(steps):\n", " time.sleep(1)\n", " image = rng.random(size=(600, 600, 3))\n", " yield image\n", " image = np.ones((1000,1000,3), np.uint8)\n", " image[:] = [255, 124, 0]\n", " yield image\n", "\n", "\n", "demo = gr.Interface(fake_diffusion,\n", " inputs=gr.Slider(1, 10, 3, step=1),\n", " outputs=\"image\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

3
demo/fake_diffusion/run.py
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@ -3,9 +3,10 @@ import numpy as np
import time
def fake_diffusion(steps):
rng = np.random.default_rng()
for i in range(steps):
time.sleep(1)
image = np.random.random((600, 600, 3))
image = rng.random(size=(600, 600, 3))
yield image
image = np.ones((1000,1000,3), np.uint8)
image[:] = [255, 124, 0]

2
demo/fake_diffusion_with_gif/run.ipynb
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@ -1 +1 @@
{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_diffusion_with_gif"]}, {"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", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/fake_diffusion_with_gif/image.gif"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "import os\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "\n", "def create_gif(images):\n", " pil_images = []\n", " for image in images:\n", " if isinstance(image, str):\n", " response = requests.get(image)\n", " image = Image.open(BytesIO(response.content))\n", " else:\n", " image = Image.fromarray((image * 255).astype(np.uint8))\n", " pil_images.append(image)\n", " fp_out = os.path.join(os.path.abspath(''), \"image.gif\")\n", " img = pil_images.pop(0)\n", " img.save(fp=fp_out, format='GIF', append_images=pil_images,\n", " save_all=True, duration=400, loop=0)\n", " return fp_out\n", "\n", "\n", "def fake_diffusion(steps):\n", " images = []\n", " for _ in range(steps):\n", " time.sleep(1)\n", " image = np.random.random((600, 600, 3))\n", " images.append(image)\n", " yield image, gr.Image(visible=False)\n", "\n", " time.sleep(1)\n", " image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\n", " images.append(image)\n", " gif_path = create_gif(images)\n", "\n", " yield image, gr.Image(value=gif_path, visible=True)\n", "\n", "\n", "demo = gr.Interface(fake_diffusion,\n", " inputs=gr.Slider(1, 10, 3, step=1),\n", " outputs=[\"image\", gr.Image(label=\"All Images\", visible=False)])\n", "demo.queue()\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_diffusion_with_gif"]}, {"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", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/fake_diffusion_with_gif/image.gif"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "import os\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "\n", "def create_gif(images):\n", " pil_images = []\n", " for image in images:\n", " if isinstance(image, str):\n", " response = requests.get(image)\n", " image = Image.open(BytesIO(response.content))\n", " else:\n", " image = Image.fromarray((image * 255).astype(np.uint8))\n", " pil_images.append(image)\n", " fp_out = os.path.join(os.path.abspath(''), \"image.gif\")\n", " img = pil_images.pop(0)\n", " img.save(fp=fp_out, format='GIF', append_images=pil_images,\n", " save_all=True, duration=400, loop=0)\n", " return fp_out\n", "\n", "\n", "def fake_diffusion(steps):\n", " rng = np.random.default_rng()\n", " images = []\n", " for _ in range(steps):\n", " time.sleep(1)\n", " image = rng.random((600, 600, 3))\n", " images.append(image)\n", " yield image, gr.Image(visible=False)\n", "\n", " time.sleep(1)\n", " image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\n", " images.append(image)\n", " gif_path = create_gif(images)\n", "\n", " yield image, gr.Image(value=gif_path, visible=True)\n", "\n", "\n", "demo = gr.Interface(fake_diffusion,\n", " inputs=gr.Slider(1, 10, 3, step=1),\n", " outputs=[\"image\", gr.Image(label=\"All Images\", visible=False)])\n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

3
demo/fake_diffusion_with_gif/run.py
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@ -24,10 +24,11 @@ def create_gif(images):
def fake_diffusion(steps):
rng = np.random.default_rng()
images = []
for _ in range(steps):
time.sleep(1)
image = np.random.random((600, 600, 3))
image = rng.random((600, 600, 3))
images.append(image)
yield image, gr.Image(visible=False)

3
test/test_components.py
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@ -854,8 +854,9 @@ class TestAudio:
with pytest.raises(ValueError):
gr.Audio(type="unknown")
rng = np.random.default_rng()
# Confirm Audio can be instantiated with a numpy array
gr.Audio((100, np.random.random(size=(1000, 2))), label="Play your audio")
gr.Audio((100, rng.random(size=(1000, 2))), label="Play your audio")
# Output functionalities
y_audio = client_utils.decode_base64_to_file(