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b/demo/pictionary/run.py @@ -1,5 +1,6 @@ from pathlib import Path +import numpy as np import torch import gradio as gr from torch import nn @@ -30,10 +31,10 @@ model.load_state_dict(state_dict, strict=False) model.eval() -def predict(input): - im = input +def predict(im): if im is None: return None + im = np.asarray(im.resize((28, 28))) x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. @@ -47,5 +48,9 @@ def predict(input): return {LABELS[i]: v.item() for i, v in zip(indices, values)} -interface = gr.Interface(predict, inputs=gr.templates.Sketchpad(label="Draw Here"), outputs=gr.Label(label="Guess"), theme="default", css=".footer{display:none !important}", live=True) -interface.launch(enable_queue=False) +interface = gr.Interface(predict, + inputs=gr.Sketchpad(label="Draw Here", brush_radius=5, type="pil", shape=(120, 120)), + outputs=gr.Label(label="Guess"), + live=True) + +interface.queue().launch() diff --git a/gradio/components.py b/gradio/components.py index fb5b87db9fd93..c43cb2c2f5299 100644 --- a/gradio/components.py +++ b/gradio/components.py @@ -2144,7 +2144,7 @@ class Audio( ): """ Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output). - Preprocessing: passes the uploaded audio as a {Tuple(int, numpy.array)} corresponding to (sample rate, data) or as a {str} filepath, depending on `type` + Preprocessing: passes the uploaded audio as a {Tuple(int, numpy.array)} corresponding to (sample rate in Hz, audio data as a 16-bit int array whose values range from -32768 to 32767), or as a {str} filepath, depending on `type`. Postprocessing: expects a {Tuple(int, numpy.array)} corresponding to (sample rate, data) or as a {str} filepath or URL to an audio file, which gets displayed Examples-format: a {str} filepath to a local file that contains audio. Demos: main_note, generate_tone, reverse_audio diff --git a/gradio/interface.py b/gradio/interface.py index 3b0d1cb16c3d2..717e1d507062b 100644 --- a/gradio/interface.py +++ b/gradio/interface.py @@ -130,7 +130,7 @@ def __init__( description: str | None = None, article: str | None = None, thumbnail: str | None = None, - theme: Theme | None = None, + theme: Theme | str | None = None, css: str | None = None, allow_flagging: str | None = None, flagging_options: List[str] | List[Tuple[str, str]] | None = None, diff --git a/gradio/templates.py b/gradio/templates.py index 561a07d38de21..304a3b242afad 100644 --- a/gradio/templates.py +++ b/gradio/templates.py @@ -68,6 +68,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, + brush_radius: int | None = None, **kwargs, ): super().__init__( @@ -85,6 +86,7 @@ def __init__( streaming=streaming, elem_id=elem_id, mirror_webcam=mirror_webcam, + brush_radius=brush_radius, **kwargs, ) @@ -113,6 +115,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, + brush_radius: int | None = None, **kwargs, ): super().__init__( @@ -130,6 +133,7 @@ def __init__( streaming=streaming, elem_id=elem_id, mirror_webcam=mirror_webcam, + brush_radius=brush_radius, **kwargs, ) @@ -158,6 +162,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, + brush_radius: int | None = None, **kwargs, ): super().__init__( @@ -175,6 +180,7 @@ def __init__( streaming=streaming, elem_id=elem_id, mirror_webcam=mirror_webcam, + brush_radius=brush_radius, **kwargs, ) @@ -203,6 +209,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, + brush_radius: int | None = None, **kwargs, ): super().__init__( @@ -220,6 +227,7 @@ def __init__( streaming=streaming, elem_id=elem_id, mirror_webcam=mirror_webcam, + brush_radius=brush_radius, **kwargs, ) @@ -248,6 +256,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, + brush_radius: int | None = None, **kwargs, ): super().__init__( @@ -265,6 +274,7 @@ def __init__( streaming=streaming, elem_id=elem_id, mirror_webcam=mirror_webcam, + brush_radius=brush_radius, **kwargs, ) @@ -293,6 +303,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, + brush_radius: int | None = None, **kwargs, ): super().__init__( @@ -310,6 +321,7 @@ def __init__( streaming=streaming, elem_id=elem_id, mirror_webcam=mirror_webcam, + brush_radius=brush_radius, **kwargs, ) diff --git a/pytorch_model.bin b/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..bd2857289548b24e165d8c627b3943b2aad347c8 GIT binary patch literal 1656903 zcmaI72|QKb_y14kAt7^8L^L6i;hwdPnKfu64JxJ5aElCO$`pk}s1zcVXrKZ2tVNpV zxm2Vce0m+q?nkjtl0nY zQWjGc3tY1>cdFw3Xg%Tzvvt;_ptnK?{;F2Jr_1ETZ>|MP z1J??bg8NPyEE)Wd=vd)^U>UEWp+e<8Ew79?-kAQQi(8X6c;U5-@|5T3aza(sgZhuKE 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z4i1=n=ksc%W$?G^eUU6tk$;+(ySRjih>VQL|8E*1A|VpAd|`M*+#-{ZpcO$Tlb!8F zL^PFTVWgK7+^o~)wBD=GGrQ_BJJJTv{z#!3qy=aCMUbWXd1z(6K`2(Qf?xl%1HCTE z1sVs@HwgatyLZ4H=`(a~n*-OSpDxs?`vK2#Gz9f2ZE&(?FcVo?i62hhrqv>O@Ov^9tSru> zU5o3`!XcXL{h z9PTIWW-IY0)Np<+=jcZddG2t!0h4%m2g%1H)VyUJdk{LE?HuNfruGZDQ~Kk$lIcBg zdg}``6)539`}5+bsFnY{JO3~HbEt^L|Lo7WMayF&mPR|aqrZLgQAy&F|Fj|hLZ{`5*jmY?k3)Sj)e$dz@R{{%aTT+yCF#&V!QwUhc%d zvGc2@O%)MQ;#XSfFRViCUs$WZvFkq9OcCMt#y{=f%k`=q{_o{Z`WyS7=bnFKvyS|Q zwf-CXpZ)yb*iL)(e=m3P-`M}`u|xS)|36cil+>?{m+}HF8}-gfBJ-%yQI`VpAF{!{+a)O&-=dsf=Xuo From 85e836401e653ef0370908d4f49b44b5c33bcb1f Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Fri, 24 Mar 2023 15:22:47 -0700 Subject: [PATCH 3/8] doc --- gradio/components.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gradio/components.py b/gradio/components.py index c43cb2c2f5299..c648578657506 100644 --- a/gradio/components.py +++ b/gradio/components.py @@ -3113,7 +3113,7 @@ def __init__( ): """ Parameters: - value: the initial value of the state. If callable, the function will be called whenever the app loads to set the initial value of the component. + value: the initial value (of abitrary type) of the state. The provided argument is deepcopied. If a callable is provided, the function will be called whenever the app loads to set the initial value of the state. """ self.stateful = True IOComponent.__init__(self, value=deepcopy(value), **kwargs) From c931d80958a8954ca7aa960288736d522a6c36d4 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Fri, 24 Mar 2023 15:26:29 -0700 Subject: [PATCH 4/8] changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 71736cb5edb02..d46b3d678ecea 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,8 @@ ## Documentation Changes: - Makes some fixes to the Theme Guide related to naming of variables, by [@abidlabs](https://github.com/abidlabs) in [PR 3561](https://github.com/gradio-app/gradio/pull/3561) +- Makes some additions to documentation of `Audio` and `State` components, and fixes the `pictionary` demo by [@abidlabs](https://github.com/abidlabs) in [PR 3611](https://github.com/gradio-app/gradio/pull/3611) + ## Testing and Infrastructure Changes: From a1118eb1651baf7e5cf4c3b1373ce246b67db809 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Fri, 24 Mar 2023 15:28:02 -0700 Subject: [PATCH 5/8] typing --- gradio/components.py | 4 ++-- gradio/templates.py | 12 ++++++------ 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/gradio/components.py b/gradio/components.py index c648578657506..36c336026abe5 100644 --- a/gradio/components.py +++ b/gradio/components.py @@ -1578,7 +1578,7 @@ def __init__( elem_id: str | None = None, elem_classes: List[str] | str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): """ @@ -1661,7 +1661,7 @@ def update( show_label: bool | None = None, interactive: bool | None = None, visible: bool | None = None, - brush_radius: int | None = None, + brush_radius: float | None = None, ): return { "label": label, diff --git a/gradio/templates.py b/gradio/templates.py index 304a3b242afad..dc27f6e118f9a 100644 --- a/gradio/templates.py +++ b/gradio/templates.py @@ -68,7 +68,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): super().__init__( @@ -115,7 +115,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): super().__init__( @@ -162,7 +162,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): super().__init__( @@ -209,7 +209,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): super().__init__( @@ -256,7 +256,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): super().__init__( @@ -303,7 +303,7 @@ def __init__( streaming: bool = False, elem_id: str | None = None, mirror_webcam: bool = True, - brush_radius: int | None = None, + brush_radius: float | None = None, **kwargs, ): super().__init__( From d26770e1e18492501fa4b45d96ee619ba803776f Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Fri, 24 Mar 2023 15:31:43 -0700 Subject: [PATCH 6/8] run on windows --- demo/generate_notebooks.py | 12 ++++++------ demo/pictionary/run.ipynb | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/demo/generate_notebooks.py b/demo/generate_notebooks.py index 8d3d71a5f0053..a1644742ec499 100644 --- a/demo/generate_notebooks.py +++ b/demo/generate_notebooks.py @@ -20,7 +20,7 @@ def git_tracked(demo, file): text = f"# Gradio Demo: {demo}" if os.path.exists(os.path.join(GRADIO_DEMO_DIR, demo, "DESCRIPTION.md")): - with open(os.path.join(GRADIO_DEMO_DIR, demo, "DESCRIPTION.md"), "r") as f: + with open(os.path.join(GRADIO_DEMO_DIR, demo, "DESCRIPTION.md"), "r", encoding="utf8") as f: description = f.read() text += f"""\n### {description} """ @@ -45,13 +45,13 @@ def git_tracked(demo, file): requirements = "" if os.path.exists(os.path.join(GRADIO_DEMO_DIR, demo, "requirements.txt")): - with open(os.path.join(GRADIO_DEMO_DIR, demo, "requirements.txt"), "r") as f: + with open(os.path.join(GRADIO_DEMO_DIR, demo, "requirements.txt"), "r", encoding="utf8") as f: requirements = f.read().split("\n") requirements = " ".join(requirements) installs = f"!pip install -q gradio {requirements}" - with open(os.path.join(GRADIO_DEMO_DIR, demo, "run.py"), "r") as f: + with open(os.path.join(GRADIO_DEMO_DIR, demo, "run.py"), "r", encoding="utf8") as f: code = f.read() code = code.replace("os.path.dirname(__file__)", "os.path.abspath('')") @@ -67,10 +67,10 @@ def git_tracked(demo, file): output_notebook = os.path.join(GRADIO_DEMO_DIR, demo, "run.ipynb") - with open(output_notebook, 'w') as f: + with open(output_notebook, 'w', encoding="utf8") as f: nbf.write(nb, f) - with open(output_notebook, "r") as f: + with open(output_notebook, "r", encoding="utf8") as f: content = f.read() content = json.loads(content) @@ -78,5 +78,5 @@ def git_tracked(demo, file): random.seed(i) cell["id"] = random.getrandbits(128) - with open(output_notebook, "w") as f: + with open(output_notebook, "w", encoding="utf8") as f: f.write(json.dumps(content)) \ No newline at end of file diff --git a/demo/pictionary/run.ipynb b/demo/pictionary/run.ipynb index d60ddbeb5f24c..a3200f294fba7 100644 --- a/demo/pictionary/run.ipynb +++ b/demo/pictionary/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: pictionary"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch 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/pictionary/class_names.txt"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from pathlib import Path\n", "\n", "import torch\n", "import gradio as gr\n", "from torch import nn\n", "import gdown \n", "\n", "url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ'\n", "output = 'pytorch_model.bin'\n", "gdown.download(url, output, quiet=False)\n", "\n", "LABELS = Path('class_names.txt').read_text().splitlines()\n", "\n", "model = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Flatten(),\n", " nn.Linear(1152, 256),\n", " nn.ReLU(),\n", " nn.Linear(256, len(LABELS)),\n", ")\n", "state_dict = torch.load('pytorch_model.bin', map_location='cpu')\n", "model.load_state_dict(state_dict, strict=False)\n", "model.eval()\n", "\n", "def predict(input):\n", " im = input\n", " if im is None:\n", " return None\n", " \n", " x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.\n", "\n", " with torch.no_grad():\n", " out = model(x)\n", "\n", " probabilities = torch.nn.functional.softmax(out[0], dim=0)\n", "\n", " values, indices = torch.topk(probabilities, 5)\n", "\n", " return {LABELS[i]: v.item() for i, v in zip(indices, values)}\n", "\n", "\n", "interface = gr.Interface(predict, inputs=gr.templates.Sketchpad(label=\"Draw Here\"), outputs=gr.Label(label=\"Guess\"), theme=\"default\", css=\".footer{display:none !important}\", live=True)\n", "interface.launch(enable_queue=False)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: pictionary"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch gdown numpy"]}, {"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/pictionary/class_names.txt"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from pathlib import Path\n", "\n", "import numpy as np\n", "import torch\n", "import gradio as gr\n", "from torch import nn\n", "import gdown \n", "\n", "url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ'\n", "output = 'pytorch_model.bin'\n", "gdown.download(url, output, quiet=False)\n", "\n", "LABELS = Path('class_names.txt').read_text().splitlines()\n", "\n", "model = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Flatten(),\n", " nn.Linear(1152, 256),\n", " nn.ReLU(),\n", " nn.Linear(256, len(LABELS)),\n", ")\n", "state_dict = torch.load('pytorch_model.bin', map_location='cpu')\n", "model.load_state_dict(state_dict, strict=False)\n", "model.eval()\n", "\n", "def predict(im):\n", " if im is None:\n", " return None\n", " im = np.asarray(im.resize((28, 28)))\n", " \n", " x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.\n", "\n", " with torch.no_grad():\n", " out = model(x)\n", "\n", " probabilities = torch.nn.functional.softmax(out[0], dim=0)\n", "\n", " values, indices = torch.topk(probabilities, 5)\n", "\n", " return {LABELS[i]: v.item() for i, v in zip(indices, values)}\n", "\n", "\n", "interface = gr.Interface(predict, \n", " inputs=gr.Sketchpad(label=\"Draw Here\", brush_radius=5, type=\"pil\", shape=(120, 120)), \n", " outputs=gr.Label(label=\"Guess\"), \n", " live=True)\n", "\n", "interface.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file From 3eecf49ff69f5108acef1f17ebaec8841eae8327 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Fri, 24 Mar 2023 15:50:16 -0700 Subject: [PATCH 7/8] cancels --- gradio/events.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gradio/events.py b/gradio/events.py index c19539e45c933..b68682a0f635a 100644 --- a/gradio/events.py +++ b/gradio/events.py @@ -118,7 +118,7 @@ def __call__( max_batch_size: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). postprocess: If False, will not run postprocessing of component data before returning 'fn' output to the browser. - cancels: A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. + cancels: A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. """ # _js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. From 01ac758b137be7cbd36cfd583b1d1d06f6dad73e Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Fri, 24 Mar 2023 16:16:50 -0700 Subject: [PATCH 8/8] added clarifications --- gradio/components.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gradio/components.py b/gradio/components.py index 36c336026abe5..8270880517095 100644 --- a/gradio/components.py +++ b/gradio/components.py @@ -2145,7 +2145,7 @@ class Audio( """ Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output). Preprocessing: passes the uploaded audio as a {Tuple(int, numpy.array)} corresponding to (sample rate in Hz, audio data as a 16-bit int array whose values range from -32768 to 32767), or as a {str} filepath, depending on `type`. - Postprocessing: expects a {Tuple(int, numpy.array)} corresponding to (sample rate, data) or as a {str} filepath or URL to an audio file, which gets displayed + Postprocessing: expects a {Tuple(int, numpy.array)} corresponding to (sample rate in Hz, audio data as a float or int numpy array) or as a {str} filepath or URL to an audio file, which gets displayed Examples-format: a {str} filepath to a local file that contains audio. Demos: main_note, generate_tone, reverse_audio Guides: real_time_speech_recognition @@ -2169,7 +2169,7 @@ def __init__( ): """ Parameters: - value: A path, URL, or [sample_rate, numpy array] tuple for the default value that Audio component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component. + value: A path, URL, or [sample_rate, numpy array] tuple (sample rate in Hz, audio data as a float or int numpy array) for the default value that Audio component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component. source: Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input. type: The format the audio file is converted to before being passed into the prediction function. "numpy" converts the audio to a tuple consisting of: (int sample rate, numpy.array for the data), "filepath" passes a str path to a temporary file containing the audio. label: component name in interface.

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