mirror of
https://github.com/gradio-app/gradio.git
synced 2025-03-31 12:20:26 +08:00
0.3.1
This commit is contained in:
Abubakar Abid
parent
a1b694b7da
commit
92bd5f6af6
@ -29,15 +29,8 @@
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"outputs": [],
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"source": [
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"inp = gradio.inputs.ImageUpload(image_width=299, image_height=299)\n",
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"out = gradio.outputs.Label(label_names='imagenet1000', max_label_length=8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"out = gradio.outputs.Label(label_names='imagenet1000', max_label_length=8, num_top_classes=5)\n",
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"\n",
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"iface = gradio.Interface(inputs=inp, \n",
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" outputs=out,\n",
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" model=model, \n",
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@ -54,8 +47,8 @@
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"output_type": "stream",
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"text": [
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"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
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"Model is running locally at: http://localhost:7860/interface.html\n",
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"Model available publicly for 8 hours at: https://efb97fa5.ngrok.io/interface.html\n"
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"Model is running locally at: http://localhost:7861/interface.html\n",
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"Model available publicly for 8 hours at: http://4d315e61.ngrok.io/interface.html\n"
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]
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},
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{
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@ -65,50 +58,63 @@
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" <iframe\n",
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" width=\"1000\"\n",
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" height=\"500\"\n",
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" src=\"http://localhost:7860/interface.html\"\n",
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" src=\"http://localhost:7861/interface.html\"\n",
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" frameborder=\"0\"\n",
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" allowfullscreen\n",
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" ></iframe>\n",
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" "
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],
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"text/plain": [
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"<IPython.lib.display.IFrame at 0x118f6285eb8>"
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"<IPython.lib.display.IFrame at 0x26985fbd940>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"('http://localhost:7860/interface.html', 'https://efb97fa5.ngrok.io')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"127.0.0.1 - - [06/Mar/2019 11:24:00] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:24:00] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:24:00] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:24:00] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dddddddddddddddddddd cleaver,\n",
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"dddddddddddddddddddd cleaver,\n"
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /static/css/gradio.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /static/js/image-upload-input.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:01] \"GET /static/js/class-output.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:02] code 404, message File not found\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:02] \"GET /favicon.ico HTTP/1.1\" 404 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:08] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/css/style.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/css/gradio.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/js/utils.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/img/logo_inline.png HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/js/image-upload-input.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:09] \"GET /static/js/class-output.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:10] code 404, message File not found\n",
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"127.0.0.1 - - [06/Mar/2019 11:55:10] \"GET /favicon.ico HTTP/1.1\" 404 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:07] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:07] \"GET /static/css/gradio.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:07] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:07] \"GET /static/js/image-upload-input.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:07] \"GET /static/js/class-output.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:31] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:43] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/js/utils.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/css/gradio.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/css/style.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/js/image-upload-input.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/js/class-output.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:44] \"GET /static/img/logo_inline.png HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:45] code 404, message File not found\n",
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"127.0.0.1 - - [06/Mar/2019 11:58:45] \"GET /favicon.ico HTTP/1.1\" 404 -\n"
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]
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}
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],
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"source": [
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"iface.launch(inline=True, browser=True, share=True)"
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"iface.launch(inline=True, browser=True, share=True);"
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]
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}
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],
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@ -1 +1 @@
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from gradio.interface import Interface # This makes Interface importable as gradio.Interface.
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from gradio.interface import Interface # This makes it possible to import `Interface` as `gradio.Interface`.
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1000
build/lib/gradio/imagenet_class_labels.py
Normal file
1000
build/lib/gradio/imagenet_class_labels.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,9 +1,15 @@
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"""
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This module defines various classes that can serve as the `input` to an interface. Each class must inherit from
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`AbstractInput`, and each class must define a path to its template. All of the subclasses of `AbstractInput` are
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automatically added to a registry, which allows them to be easily referenced in other parts of the code.
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"""
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from abc import ABC, abstractmethod
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import base64
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from PIL import Image
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from gradio import preprocessing_utils
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from io import BytesIO
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import numpy as np
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from gradio import preprocessing_utils
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from PIL import Image
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class AbstractInput(ABC):
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"""
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@ -12,84 +18,115 @@ class AbstractInput(ABC):
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"""
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def __init__(self, preprocessing_fn=None):
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"""
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:param preprocessing_fn: an optional preprocessing function that overrides the default
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"""
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if preprocessing_fn is not None:
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self._pre_process = preprocessing_fn
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if not callable(preprocessing_fn):
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raise ValueError('`preprocessing_fn` must be a callable function')
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self.preprocess = preprocessing_fn
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super().__init__()
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@abstractmethod
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def _get_template_path(self):
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def get_template_path(self):
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"""
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All interfaces should define a method that returns the path to its template.
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"""
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pass
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@abstractmethod
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def _pre_process(self):
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def preprocess(self, inp):
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"""
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All interfaces should define a method that returns the path to its template.
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All interfaces should define a default preprocessing method
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"""
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pass
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class Sketchpad(AbstractInput):
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def __init__(self, preprocessing_fn=None, image_width=28, image_height=28):
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self.image_width = image_width
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self.image_height = image_height
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super().__init__(preprocessing_fn=preprocessing_fn)
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def _get_template_path(self):
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def get_template_path(self):
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return 'templates/sketchpad_input.html'
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def _pre_process(self, imgstring):
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def preprocess(self, inp):
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"""
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Default preprocessing method for the SketchPad is to convert the sketch to black and white and resize 28x28
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"""
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content = imgstring.split(';')[1]
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content = inp.split(';')[1]
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image_encoded = content.split(',')[1]
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body = base64.decodebytes(image_encoded.encode('utf-8'))
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im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
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im = preprocessing_utils.resize_and_crop(im, (28, 28))
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array = np.array(im).flatten().reshape(1, 28, 28, 1)
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im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
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array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, 1)
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return array
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class Webcam(AbstractInput):
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def __init__(self, preprocessing_fn=None, image_width=224, image_height=224, num_channels=3):
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self.image_width = image_width
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self.image_height = image_height
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self.num_channels = num_channels
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super().__init__(preprocessing_fn=preprocessing_fn)
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def _get_template_path(self):
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def get_template_path(self):
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return 'templates/webcam_input.html'
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def _pre_process(self, imgstring):
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def preprocess(self, inp):
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"""
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Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
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"""
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content = imgstring.split(';')[1]
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content = inp.split(';')[1]
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image_encoded = content.split(',')[1]
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body = base64.decodebytes(image_encoded.encode('utf-8'))
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im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
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im = preprocessing_utils.resize_and_crop(im, (48, 48))
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array = np.array(im).flatten().reshape(1, 48, 48, 1)
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im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('RGB')
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im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
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array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, self.num_channels)
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return array
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class Textbox(AbstractInput):
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def _get_template_path(self):
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def get_template_path(self):
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return 'templates/textbox_input.html'
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def _pre_process(self, text):
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def preprocess(self, inp):
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"""
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By default, no pre-processing is applied to text.
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"""
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return text
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return inp
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class ImageUpload(AbstractInput):
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def __init__(self, preprocessing_fn=None, image_width=224, image_height=224, num_channels=3, image_mode='RGB',
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scale = 1/127.5, shift = -1):
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self.image_width = image_width
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self.image_height = image_height
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self.num_channels = num_channels
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self.image_mode = image_mode
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self.scale = scale
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self.shift = shift
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super().__init__(preprocessing_fn=preprocessing_fn)
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def _get_template_path(self):
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def get_template_path(self):
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return 'templates/image_upload_input.html'
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def _pre_process(self, imgstring):
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def preprocess(self, inp):
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"""
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Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
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"""
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content = imgstring.split(';')[1]
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content = inp.split(';')[1]
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image_encoded = content.split(',')[1]
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body = base64.decodebytes(image_encoded.encode('utf-8'))
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im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
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im = preprocessing_utils.resize_and_crop(im, (48, 48))
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array = np.array(im).flatten().reshape(1, 48, 48, 1)
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im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert(self.image_mode)
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im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
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im = np.array(im).flatten()
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im = im * self.scale + self.shift
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if self.num_channels is None:
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array = im.reshape(1, self.image_width, self.image_height)
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else:
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array = im.reshape(1, self.image_width, self.image_height, self.num_channels)
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return array
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# Automatically adds all subclasses of AbstractInput into a dictionary (keyed by class name) for easy referencing.
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registry = {cls.__name__.lower(): cls for cls in AbstractInput.__subclasses__()}
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@ -1,14 +1,15 @@
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'''
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This is the core file in the `gradio` package, and defines the Interface class, including methods for constructing the
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interface using the input and output types.
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'''
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import asyncio
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import websockets
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import nest_asyncio
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import webbrowser
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import pkg_resources
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from bs4 import BeautifulSoup
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from gradio import inputs
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from gradio import outputs
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import gradio.inputs
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import gradio.outputs
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from gradio import networking
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import os
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import shutil
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import tempfile
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nest_asyncio.apply()
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@ -17,46 +18,53 @@ LOCALHOST_IP = '127.0.0.1'
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INITIAL_WEBSOCKET_PORT = 9200
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TRY_NUM_PORTS = 100
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BASE_TEMPLATE = pkg_resources.resource_filename('gradio', 'templates/base_template.html')
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JS_PATH_LIB = pkg_resources.resource_filename('gradio', 'js/')
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CSS_PATH_LIB = pkg_resources.resource_filename('gradio', 'css/')
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JS_PATH_TEMP = 'js/'
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CSS_PATH_TEMP = 'css/'
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TEMPLATE_TEMP = 'interface.html'
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BASE_JS_FILE = 'js/all-io.js'
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class Interface():
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class Interface:
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"""
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The Interface class represents a general input/output interface for a machine learning model. During construction,
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||||
the appropriate inputs and outputs
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"""
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# Dictionary in which each key is a valid `model_type` argument to constructor, and the value being the description.
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VALID_MODEL_TYPES = {'sklearn': 'sklearn model', 'keras': 'keras model', 'function': 'python function'}
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def __init__(self, input, output, model, model_type=None, preprocessing_fn=None, postprocessing_fn=None):
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def __init__(self, inputs, outputs, model, model_type=None, preprocessing_fns=None, postprocessing_fns=None,
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verbose=True):
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"""
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:param model_type: what kind of trained model, can be 'keras' or 'sklearn'.
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:param inputs: a string or `AbstractInput` representing the input interface.
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:param outputs: a string or `AbstractOutput` representing the output interface.
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:param model_obj: the model object, such as a sklearn classifier or keras model.
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:param model_params: additional model parameters.
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:param model_type: what kind of trained model, can be 'keras' or 'sklearn' or 'function'. Inferred if not
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provided.
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:param preprocessing_fns: an optional function that overrides the preprocessing function of the input interface.
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:param postprocessing_fns: an optional function that overrides the postprocessing fn of the output interface.
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"""
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self.input_interface = inputs.registry[input](preprocessing_fn)
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self.output_interface = outputs.registry[output](postprocessing_fn)
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if isinstance(inputs, str):
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self.input_interface = gradio.inputs.registry[inputs.lower()](preprocessing_fns)
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elif isinstance(inputs, gradio.inputs.AbstractInput):
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self.input_interface = inputs
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else:
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raise ValueError('Input interface must be of type `str` or `AbstractInput`')
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if isinstance(outputs, str):
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self.output_interface = gradio.outputs.registry[outputs.lower()](postprocessing_fns)
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elif isinstance(outputs, gradio.outputs.AbstractOutput):
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self.output_interface = outputs
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else:
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raise ValueError('Output interface must be of type `str` or `AbstractOutput`')
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self.model_obj = model
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if model_type is None:
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model_type = self._infer_model_type(model)
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if model_type is None:
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raise ValueError("model_type could not be inferred, please specify parameter `model_type`")
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else:
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if verbose:
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print("Model type not explicitly identified, inferred to be: {}".format(
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self.VALID_MODEL_TYPES[model_type]))
|
||||
self.VALID_MODEL_TYPES[model_type]))
|
||||
elif not(model_type.lower() in self.VALID_MODEL_TYPES):
|
||||
ValueError('model_type must be one of: {}'.format(self.VALID_MODEL_TYPES))
|
||||
self.model_type = model_type
|
||||
self.verbose = verbose
|
||||
|
||||
def _infer_model_type(self, model):
|
||||
if callable(model):
|
||||
return 'function'
|
||||
|
||||
@staticmethod
|
||||
def _infer_model_type(model):
|
||||
""" Helper method that attempts to identify the type of trained ML model."""
|
||||
try:
|
||||
import sklearn
|
||||
if isinstance(model, sklearn.base.BaseEstimator):
|
||||
@ -78,124 +86,100 @@ class Interface():
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
return None
|
||||
if callable(model):
|
||||
return 'function'
|
||||
|
||||
def _build_template(self, temp_dir):
|
||||
input_template_path = pkg_resources.resource_filename(
|
||||
'gradio', self.input_interface._get_template_path())
|
||||
output_template_path = pkg_resources.resource_filename(
|
||||
'gradio', self.output_interface._get_template_path())
|
||||
input_page = open(input_template_path)
|
||||
output_page = open(output_template_path)
|
||||
input_soup = BeautifulSoup(input_page.read(), features="html.parser")
|
||||
output_soup = BeautifulSoup(output_page.read(), features="html.parser")
|
||||
|
||||
all_io_page = open(BASE_TEMPLATE)
|
||||
all_io_soup = BeautifulSoup(all_io_page.read(), features="html.parser")
|
||||
input_tag = all_io_soup.find("div", {"id": "input"})
|
||||
output_tag = all_io_soup.find("div", {"id": "output"})
|
||||
|
||||
input_tag.replace_with(input_soup)
|
||||
output_tag.replace_with(output_soup)
|
||||
|
||||
f = open(os.path.join(temp_dir, TEMPLATE_TEMP), "w")
|
||||
f.write(str(all_io_soup.prettify))
|
||||
|
||||
self._copy_files(JS_PATH_LIB, os.path.join(temp_dir, JS_PATH_TEMP))
|
||||
self._copy_files(CSS_PATH_LIB, os.path.join(temp_dir, CSS_PATH_TEMP))
|
||||
return
|
||||
|
||||
def _copy_files(self, src_dir, dest_dir):
|
||||
if not os.path.exists(dest_dir):
|
||||
os.makedirs(dest_dir)
|
||||
src_files = os.listdir(src_dir)
|
||||
for file_name in src_files:
|
||||
full_file_name = os.path.join(src_dir, file_name)
|
||||
if os.path.isfile(full_file_name):
|
||||
shutil.copy(full_file_name, dest_dir)
|
||||
|
||||
def _set_socket_url_in_js(self, temp_dir, socket_url):
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE)) as fin:
|
||||
lines = fin.readlines()
|
||||
lines[0] = 'var NGROK_URL = "{}"\n'.format(socket_url.replace('http', 'ws'))
|
||||
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE), 'w') as fout:
|
||||
for line in lines:
|
||||
fout.write(line)
|
||||
|
||||
def _set_socket_port_in_js(self, temp_dir, socket_port):
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE)) as fin:
|
||||
lines = fin.readlines()
|
||||
lines[1] = 'var SOCKET_PORT = {}\n'.format(socket_port)
|
||||
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE), 'w') as fout:
|
||||
for line in lines:
|
||||
fout.write(line)
|
||||
|
||||
def predict(self, array):
|
||||
if self.model_type=='sklearn':
|
||||
return self.model_obj.predict(array)
|
||||
elif self.model_type=='keras':
|
||||
return self.model_obj.predict(array)
|
||||
elif self.model_type=='function':
|
||||
return self.model_obj(array)
|
||||
else:
|
||||
ValueError('model_type must be one of: {}'.format(self.VALID_MODEL_TYPES))
|
||||
raise ValueError("model_type could not be inferred, please specify parameter `model_type`")
|
||||
|
||||
async def communicate(self, websocket, path):
|
||||
"""
|
||||
Method that defines how this interface communicates with the websocket.
|
||||
:param websocket: a Websocket object used to communicate with the interface frontend
|
||||
:param path: ignored
|
||||
Method that defines how this interface should communicates with the websocket. (1) When an input is received by
|
||||
the websocket, it is passed into the input interface and preprocssed. (2) Then the model is called to make a
|
||||
prediction. (3) Finally, the prediction is postprocessed to get something to be displayed by the output.
|
||||
:param websocket: a Websocket server used to communicate with the interface frontend
|
||||
:param path: not used, but required for compliance with websocket library
|
||||
"""
|
||||
while True:
|
||||
try:
|
||||
msg = await websocket.recv()
|
||||
processed_input = self.input_interface._pre_process(msg)
|
||||
processed_input = self.input_interface.preprocess(msg)
|
||||
prediction = self.predict(processed_input)
|
||||
processed_output = self.output_interface._post_process(prediction)
|
||||
processed_output = self.output_interface.postprocess(prediction)
|
||||
await websocket.send(str(processed_output))
|
||||
except websockets.exceptions.ConnectionClosed:
|
||||
pass
|
||||
# except Exception as e:
|
||||
# print(e)
|
||||
|
||||
def launch(self, share_link=False, verbose=True):
|
||||
def predict(self, preprocessed_input):
|
||||
"""
|
||||
Standard method shared by interfaces that launches a websocket at a specified IP address.
|
||||
Method that calls the relevant method of the model object to make a prediction.
|
||||
:param preprocessed_input: the preprocessed input returned by the input interface
|
||||
"""
|
||||
if self.model_type=='sklearn':
|
||||
return self.model_obj.predict(preprocessed_input)
|
||||
elif self.model_type=='keras':
|
||||
return self.model_obj.predict(preprocessed_input)
|
||||
elif self.model_type=='function':
|
||||
return self.model_obj(preprocessed_input)
|
||||
else:
|
||||
ValueError('model_type must be one of: {}'.format(self.VALID_MODEL_TYPES))
|
||||
|
||||
def launch(self, inline=None, browser=None, share=False):
|
||||
"""
|
||||
Standard method shared by interfaces that creates the interface and sets up a websocket to communicate with it.
|
||||
:param share: boolean. If True, then a share link is generated using ngrok is displayed to the user.
|
||||
"""
|
||||
output_directory = tempfile.mkdtemp()
|
||||
|
||||
# Set up a port to serve the directory containing the static files with interface.
|
||||
server_port = networking.start_simple_server(output_directory)
|
||||
path_to_server = 'http://localhost:{}/'.format(server_port)
|
||||
self._build_template(output_directory)
|
||||
networking.build_template(output_directory, self.input_interface, self.output_interface)
|
||||
|
||||
ports_in_use = networking.get_ports_in_use(INITIAL_WEBSOCKET_PORT, INITIAL_WEBSOCKET_PORT + TRY_NUM_PORTS)
|
||||
for i in range(TRY_NUM_PORTS):
|
||||
if not ((INITIAL_WEBSOCKET_PORT + i) in ports_in_use):
|
||||
break
|
||||
else:
|
||||
raise OSError("All ports from {} to {} are in use. Please close a port.".format(
|
||||
INITIAL_WEBSOCKET_PORT, INITIAL_WEBSOCKET_PORT + TRY_NUM_PORTS))
|
||||
|
||||
start_server = websockets.serve(self.communicate, LOCALHOST_IP, INITIAL_WEBSOCKET_PORT + i)
|
||||
self._set_socket_port_in_js(output_directory, INITIAL_WEBSOCKET_PORT + i)
|
||||
if verbose:
|
||||
# Set up a port to serve a websocket that sets up the communication between the front-end and model.
|
||||
websocket_port = networking.get_first_available_port(
|
||||
INITIAL_WEBSOCKET_PORT, INITIAL_WEBSOCKET_PORT + TRY_NUM_PORTS)
|
||||
start_server = websockets.serve(self.communicate, LOCALHOST_IP, websocket_port)
|
||||
networking.set_socket_port_in_js(output_directory, websocket_port) # sets the websocket port in the JS file.
|
||||
if self.verbose:
|
||||
print("NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu")
|
||||
print("Model available locally at: {}".format(path_to_server + TEMPLATE_TEMP))
|
||||
print("Model is running locally at: {}".format(path_to_server + networking.TEMPLATE_TEMP))
|
||||
|
||||
if share_link:
|
||||
networking.kill_processes([4040, 4041])
|
||||
site_ngrok_url = networking.setup_ngrok(server_port)
|
||||
socket_ngrok_url = networking.setup_ngrok(INITIAL_WEBSOCKET_PORT, api_url=networking.NGROK_TUNNELS_API_URL2)
|
||||
self._set_socket_url_in_js(output_directory, socket_ngrok_url)
|
||||
if verbose:
|
||||
print("Model available publicly for 8 hours at: {}".format(site_ngrok_url + '/' + TEMPLATE_TEMP))
|
||||
if share:
|
||||
site_ngrok_url = networking.setup_ngrok(server_port, websocket_port, output_directory)
|
||||
if self.verbose:
|
||||
print("Model available publicly for 8 hours at: {}".format(
|
||||
site_ngrok_url + '/' + networking.TEMPLATE_TEMP))
|
||||
else:
|
||||
if verbose:
|
||||
print("To create a public link, set `share_link=True` in the argument to `launch()`")
|
||||
if self.verbose:
|
||||
print("To create a public link, set `share=True` in the argument to `launch()`")
|
||||
site_ngrok_url = None
|
||||
|
||||
# Keep the server running in the background.
|
||||
asyncio.get_event_loop().run_until_complete(start_server)
|
||||
try:
|
||||
asyncio.get_event_loop().run_forever()
|
||||
except RuntimeError: # Runtime errors are thrown in jupyter notebooks because of async.
|
||||
pass
|
||||
|
||||
webbrowser.open(path_to_server + TEMPLATE_TEMP)
|
||||
if inline is None:
|
||||
try: # Check if running interactively using ipython.
|
||||
_ = get_ipython()
|
||||
inline = True
|
||||
if browser is None:
|
||||
browser = False
|
||||
except NameError:
|
||||
inline = False
|
||||
if browser is None:
|
||||
browser = True
|
||||
else:
|
||||
if browser is None:
|
||||
browser = False
|
||||
if browser:
|
||||
webbrowser.open(path_to_server + networking.TEMPLATE_TEMP) # Open a browser tab with the interface.
|
||||
if inline:
|
||||
from IPython.display import IFrame
|
||||
display(IFrame(path_to_server + networking.TEMPLATE_TEMP, width=1000, height=500))
|
||||
|
||||
return path_to_server + networking.TEMPLATE_TEMP, site_ngrok_url
|
||||
@ -1,3 +1,7 @@
|
||||
'''
|
||||
Defines helper methods useful for setting up ports, launching servers, and handling `ngrok`
|
||||
'''
|
||||
|
||||
import subprocess
|
||||
import requests
|
||||
import zipfile
|
||||
@ -5,21 +9,31 @@ import io
|
||||
import sys
|
||||
import os
|
||||
import socket
|
||||
from psutil import process_iter, AccessDenied
|
||||
from psutil import process_iter, AccessDenied, NoSuchProcess
|
||||
from signal import SIGTERM # or SIGKILL
|
||||
import threading
|
||||
from http.server import HTTPServer as BaseHTTPServer, SimpleHTTPRequestHandler
|
||||
import stat
|
||||
from requests.adapters import HTTPAdapter
|
||||
from requests.packages.urllib3.util.retry import Retry
|
||||
import pkg_resources
|
||||
from bs4 import BeautifulSoup
|
||||
from distutils import dir_util
|
||||
|
||||
INITIAL_PORT_VALUE = 7860
|
||||
TRY_NUM_PORTS = 100
|
||||
INITIAL_PORT_VALUE = 7860 # The http server will try to open on port 7860. If not available, 7861, 7862, etc.
|
||||
TRY_NUM_PORTS = 100 # Number of ports to try before giving up and throwing an exception.
|
||||
LOCALHOST_NAME = 'localhost'
|
||||
LOCALHOST_PREFIX = 'localhost:'
|
||||
NGROK_TUNNELS_API_URL = "http://localhost:4040/api/tunnels" # TODO(this should be captured from output)
|
||||
NGROK_TUNNELS_API_URL2 = "http://localhost:4041/api/tunnels" # TODO(this should be captured from output)
|
||||
|
||||
|
||||
BASE_TEMPLATE = pkg_resources.resource_filename('gradio', 'templates/base_template.html')
|
||||
STATIC_PATH_LIB = pkg_resources.resource_filename('gradio', 'static/')
|
||||
STATIC_PATH_TEMP = 'static/'
|
||||
TEMPLATE_TEMP = 'interface.html'
|
||||
BASE_JS_FILE = 'static/js/all-io.js'
|
||||
|
||||
|
||||
NGROK_ZIP_URLS = {
|
||||
"linux": "https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip",
|
||||
"darwin": "https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-darwin-amd64.zip",
|
||||
@ -27,48 +41,83 @@ NGROK_ZIP_URLS = {
|
||||
}
|
||||
|
||||
|
||||
def get_ports_in_use(start, stop):
|
||||
ports_in_use = []
|
||||
for port in range(start, stop):
|
||||
def build_template(temp_dir, input_interface, output_interface):
|
||||
"""
|
||||
Builds a complete HTML template with supporting JS and CSS files in a given directory.
|
||||
:param temp_dir: string with path to temp directory in which the template should be built
|
||||
:param input_interface: an AbstractInput object which includes is used to get the input template
|
||||
:param output_interface: an AbstractInput object which includes is used to get the input template
|
||||
"""
|
||||
input_template_path = pkg_resources.resource_filename('gradio', input_interface.get_template_path())
|
||||
output_template_path = pkg_resources.resource_filename('gradio', output_interface.get_template_path())
|
||||
input_page = open(input_template_path)
|
||||
output_page = open(output_template_path)
|
||||
input_soup = BeautifulSoup(input_page.read(), features="html.parser")
|
||||
output_soup = BeautifulSoup(output_page.read(), features="html.parser")
|
||||
|
||||
all_io_page = open(BASE_TEMPLATE)
|
||||
all_io_soup = BeautifulSoup(all_io_page.read(), features="html.parser")
|
||||
input_tag = all_io_soup.find("div", {"id": "input"})
|
||||
output_tag = all_io_soup.find("div", {"id": "output"})
|
||||
|
||||
input_tag.replace_with(input_soup)
|
||||
output_tag.replace_with(output_soup)
|
||||
|
||||
f = open(os.path.join(temp_dir, TEMPLATE_TEMP), "w")
|
||||
f.write(str(all_io_soup))
|
||||
|
||||
copy_files(STATIC_PATH_LIB, os.path.join(temp_dir, STATIC_PATH_TEMP))
|
||||
|
||||
|
||||
def copy_files(src_dir, dest_dir):
|
||||
"""
|
||||
Copies all the files from one directory to another
|
||||
:param src_dir: string path to source directory
|
||||
:param dest_dir: string path to destination directory
|
||||
"""
|
||||
dir_util.copy_tree(src_dir, dest_dir)
|
||||
|
||||
|
||||
#TODO(abidlabs): Handle the http vs. https issue that sometimes happens (a ws cannot be loaded from an https page)
|
||||
def set_socket_url_in_js(temp_dir, socket_url):
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE)) as fin:
|
||||
lines = fin.readlines()
|
||||
lines[0] = 'var NGROK_URL = "{}"\n'.format(socket_url.replace('http', 'ws'))
|
||||
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE), 'w') as fout:
|
||||
for line in lines:
|
||||
fout.write(line)
|
||||
|
||||
|
||||
def set_socket_port_in_js(temp_dir, socket_port):
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE)) as fin:
|
||||
lines = fin.readlines()
|
||||
lines[1] = 'var SOCKET_PORT = {}\n'.format(socket_port)
|
||||
|
||||
with open(os.path.join(temp_dir, BASE_JS_FILE), 'w') as fout:
|
||||
for line in lines:
|
||||
fout.write(line)
|
||||
|
||||
|
||||
def get_first_available_port(initial, final):
|
||||
"""
|
||||
Gets the first open port in a specified range of port numbers
|
||||
:param initial: the initial value in the range of port numbers
|
||||
:param final: final (exclusive) value in the range of port numbers, should be greater than `initial`
|
||||
:return:
|
||||
"""
|
||||
for port in range(initial, final):
|
||||
try:
|
||||
s = socket.socket() # create a socket object
|
||||
s.bind((LOCALHOST_NAME, port)) # Bind to the port
|
||||
s.close()
|
||||
return port
|
||||
except OSError:
|
||||
ports_in_use.append(port)
|
||||
return ports_in_use
|
||||
# ports_in_use = []
|
||||
# try:
|
||||
# for proc in process_iter():
|
||||
# for conns in proc.connections(kind='inet'):
|
||||
# ports_in_use.append(conns.laddr.port)
|
||||
# except AccessDenied:
|
||||
# pass # TODO(abidlabs): somehow find a way to handle this issue?
|
||||
# return ports_in_use
|
||||
pass
|
||||
raise OSError("All ports from {} to {} are in use. Please close a port.".format(initial, final))
|
||||
|
||||
|
||||
def serve_files_in_background(port, directory_to_serve=None):
|
||||
# class Handler(http.server.SimpleHTTPRequestHandler):
|
||||
# def __init__(self, *args, **kwargs):
|
||||
# super().__init__(*args, directory=directory_to_serve, **kwargs)
|
||||
#
|
||||
# server = socketserver.ThreadingTCPServer(('localhost', port), Handler)
|
||||
# # Ensures that Ctrl-C cleanly kills all spawned threads
|
||||
# server.daemon_threads = True
|
||||
# # Quicker rebinding
|
||||
# server.allow_reuse_address = True
|
||||
#
|
||||
# # A custom signal handle to allow us to Ctrl-C out of the process
|
||||
# def signal_handler(signal, frame):
|
||||
# print('Exiting http server (Ctrl+C pressed)')
|
||||
# try:
|
||||
# if (server):
|
||||
# server.server_close()
|
||||
# finally:
|
||||
# sys.exit(0)
|
||||
#
|
||||
# # Install the keyboard interrupt handler
|
||||
# signal.signal(signal.SIGINT, signal_handler)
|
||||
class HTTPHandler(SimpleHTTPRequestHandler):
|
||||
"""This handler uses server.base_path instead of always using os.getcwd()"""
|
||||
|
||||
@ -101,21 +150,9 @@ def serve_files_in_background(port, directory_to_serve=None):
|
||||
|
||||
|
||||
def start_simple_server(directory_to_serve=None):
|
||||
# TODO(abidlabs): increment port number until free port is found
|
||||
ports_in_use = get_ports_in_use(start=INITIAL_PORT_VALUE, stop=INITIAL_PORT_VALUE + TRY_NUM_PORTS)
|
||||
for i in range(TRY_NUM_PORTS):
|
||||
if not((INITIAL_PORT_VALUE + i) in ports_in_use):
|
||||
break
|
||||
else:
|
||||
raise OSError("All ports from {} to {} are in use. Please close a port.".format(
|
||||
INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS))
|
||||
serve_files_in_background(INITIAL_PORT_VALUE + i, directory_to_serve)
|
||||
# if directory_to_serve is None:
|
||||
# subprocess.Popen(['python', '-m', 'http.server', str(INITIAL_PORT_VALUE + i)])
|
||||
# else:
|
||||
# cmd = ' '.join(['python', '-m', 'http.server', '-d', directory_to_serve, str(INITIAL_PORT_VALUE + i)])
|
||||
# subprocess.Popen(cmd, shell=True) # Doesn't seem to work if list is passed for some reason.
|
||||
return INITIAL_PORT_VALUE + i
|
||||
port = get_first_available_port(INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS)
|
||||
serve_files_in_background(port, directory_to_serve)
|
||||
return port
|
||||
|
||||
|
||||
def download_ngrok():
|
||||
@ -133,7 +170,7 @@ def download_ngrok():
|
||||
os.chmod('ngrok', st.st_mode | stat.S_IEXEC)
|
||||
|
||||
|
||||
def setup_ngrok(local_port, api_url=NGROK_TUNNELS_API_URL):
|
||||
def create_ngrok_tunnel(local_port, api_url):
|
||||
if not(os.path.isfile('ngrok.exe') or os.path.isfile('ngrok')):
|
||||
download_ngrok()
|
||||
if sys.platform == 'win32':
|
||||
@ -147,18 +184,26 @@ def setup_ngrok(local_port, api_url=NGROK_TUNNELS_API_URL):
|
||||
session.mount('https://', adapter)
|
||||
r = session.get(api_url)
|
||||
for tunnel in r.json()['tunnels']:
|
||||
if LOCALHOST_PREFIX + str(local_port) in tunnel['config']['addr']:
|
||||
if '{}:'.format(LOCALHOST_NAME) + str(local_port) in tunnel['config']['addr']:
|
||||
return tunnel['public_url']
|
||||
raise RuntimeError("Not able to retrieve ngrok public URL")
|
||||
|
||||
|
||||
def kill_processes(process_ids):
|
||||
def setup_ngrok(server_port, websocket_port, output_directory):
|
||||
kill_processes([4040, 4041]) #TODO(abidlabs): better way to do this
|
||||
site_ngrok_url = create_ngrok_tunnel(server_port, NGROK_TUNNELS_API_URL)
|
||||
socket_ngrok_url = create_ngrok_tunnel(websocket_port, NGROK_TUNNELS_API_URL2)
|
||||
set_socket_url_in_js(output_directory, socket_ngrok_url)
|
||||
return site_ngrok_url
|
||||
|
||||
|
||||
def kill_processes(process_ids): #TODO(abidlabs): remove this, we shouldn't need to kill
|
||||
for proc in process_iter():
|
||||
try:
|
||||
for conns in proc.connections(kind='inet'):
|
||||
if conns.laddr.port in process_ids:
|
||||
proc.send_signal(SIGTERM) # or SIGKILL
|
||||
except AccessDenied:
|
||||
except (AccessDenied, NoSuchProcess):
|
||||
pass
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,13 @@
|
||||
"""
|
||||
This module defines various classes that can serve as the `output` to an interface. Each class must inherit from
|
||||
`AbstractOutput`, and each class must define a path to its template. All of the subclasses of `AbstractOutput` are
|
||||
automatically added to a registry, which allows them to be easily referenced in other parts of the code.
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
import numpy as np
|
||||
import json
|
||||
from gradio import imagenet_class_labels
|
||||
|
||||
class AbstractOutput(ABC):
|
||||
"""
|
||||
@ -9,52 +17,86 @@ class AbstractOutput(ABC):
|
||||
|
||||
def __init__(self, postprocessing_fn=None):
|
||||
"""
|
||||
:param postprocessing_fn: an optional postprocessing function that overrides the default
|
||||
"""
|
||||
if postprocessing_fn is not None:
|
||||
self._post_process = postprocessing_fn
|
||||
self.postprocess = postprocessing_fn
|
||||
super().__init__()
|
||||
|
||||
@abstractmethod
|
||||
def _get_template_path(self):
|
||||
def get_template_path(self):
|
||||
"""
|
||||
All interfaces should define a method that returns the path to its template.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def _post_process(self):
|
||||
def postprocess(self, prediction):
|
||||
"""
|
||||
All interfaces should define a method that returns the path to its template.
|
||||
All interfaces should define a default postprocessing method
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class Class(AbstractOutput):
|
||||
class Label(AbstractOutput):
|
||||
LABEL_KEY = 'label'
|
||||
CONFIDENCES_KEY = 'confidences'
|
||||
CONFIDENCE_KEY = 'confidence'
|
||||
|
||||
def _get_template_path(self):
|
||||
return 'templates/class_output.html'
|
||||
def __init__(self, postprocessing_fn=None, num_top_classes=3, show_confidences=True, label_names=None,
|
||||
max_label_length=None):
|
||||
self.num_top_classes = num_top_classes
|
||||
self.show_confidences = show_confidences
|
||||
self.label_names = label_names
|
||||
self.max_label_length = max_label_length
|
||||
super().__init__(postprocessing_fn=postprocessing_fn)
|
||||
|
||||
def _post_process(self, prediction):
|
||||
def get_label_name(self, label):
|
||||
if self.label_names is None:
|
||||
name = label
|
||||
elif self.label_names == 'imagenet1000':
|
||||
name = imagenet_class_labels.NAMES1000[label]
|
||||
else: # if list or dictionary
|
||||
name = self.label_names[label]
|
||||
if self.max_label_length is not None:
|
||||
name = name[:self.max_label_length]
|
||||
return name
|
||||
|
||||
def get_template_path(self):
|
||||
return 'templates/label_output.html'
|
||||
|
||||
def postprocess(self, prediction):
|
||||
"""
|
||||
"""
|
||||
response = dict()
|
||||
# TODO(abidlabs): check if list, if so convert to numpy array
|
||||
if isinstance(prediction, np.ndarray):
|
||||
prediction = prediction.squeeze()
|
||||
if prediction.size == 1:
|
||||
return prediction
|
||||
else:
|
||||
return prediction.argmax()
|
||||
if prediction.size == 1: # if it's single value
|
||||
response[Label.LABEL_KEY] = self.get_label_name(np.asscalar(prediction))
|
||||
elif len(prediction.shape) == 1: # if a 1D
|
||||
response[Label.LABEL_KEY] = self.get_label_name(int(prediction.argmax()))
|
||||
if self.show_confidences:
|
||||
response[Label.CONFIDENCES_KEY] = []
|
||||
for i in range(self.num_top_classes):
|
||||
response[Label.CONFIDENCES_KEY].append({
|
||||
Label.LABEL_KEY: self.get_label_name(int(prediction.argmax())),
|
||||
Label.CONFIDENCE_KEY: float(prediction.max()),
|
||||
})
|
||||
prediction[prediction.argmax()] = 0
|
||||
elif isinstance(prediction, str):
|
||||
return prediction
|
||||
response[Label.LABEL_KEY] = prediction
|
||||
else:
|
||||
raise ValueError("Unable to post-process model prediction.")
|
||||
return json.dumps(response)
|
||||
|
||||
|
||||
class Textbox(AbstractOutput):
|
||||
|
||||
def _get_template_path(self):
|
||||
def get_template_path(self):
|
||||
return 'templates/textbox_output.html'
|
||||
|
||||
def _post_process(self, prediction):
|
||||
def postprocess(self, prediction):
|
||||
"""
|
||||
"""
|
||||
return prediction
|
||||
|
||||
28
build/lib/gradio/templates/base_template.html
Normal file
28
build/lib/gradio/templates/base_template.html
Normal file
@ -0,0 +1,28 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<title>Gradio</title>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
|
||||
<link rel="stylesheet" href="../static/css/style.css">
|
||||
<link rel="stylesheet" href="../static/css/gradio.css">
|
||||
<script src="../static/js/utils.js"></script>
|
||||
<script src="../static/js/all-io.js"></script>
|
||||
<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" crossorigin="anonymous"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<nav>
|
||||
<img src="../static/img/logo_inline.png" />
|
||||
</nav>
|
||||
<div id="panels">
|
||||
<div class="panel">
|
||||
<div id="input"></div>
|
||||
<input class="submit" type="submit" value="Submit"/><!--DO NOT DELETE
|
||||
--><input class="clear" type="reset" value="Clear">
|
||||
</div>
|
||||
<div class="panel">
|
||||
<div id="output"></div>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@ -1,31 +1,11 @@
|
||||
<!-- <link rel="stylesheet" href="../css/dropzone.css">
|
||||
<div class="col-6">
|
||||
<h5>Image Upload Input:</h5>
|
||||
|
||||
<input type="file" onchange="previewFile()"><br>
|
||||
<img src="" height="200" alt="Image preview...">
|
||||
|
||||
<div class="btn-group" role="group" aria-label="Basic example">
|
||||
<button type="button" class="btn btn-primary" id="submit-button">Submit</button>
|
||||
<button type="button" class="btn btn-secondary" id="clear-button">Clear</button>
|
||||
<div class="gradio input image_file">
|
||||
<div class="role">Input</div>
|
||||
<div class="input_image drop_mode">
|
||||
<div class="input_caption">Drop Image Here<br>- or -<br>Click to Upload</div>
|
||||
<img />
|
||||
</div>
|
||||
<input class="hidden_upload" type="file" accept="image/x-png,image/gif,image/jpeg" />
|
||||
</div>
|
||||
<script src="../js/image-upload-input.js"></script>
|
||||
|
||||
-->
|
||||
<div class="col-md-6">
|
||||
<div> <h5>Image Upload Input:</h5> </div>
|
||||
|
||||
<div class="uploader" style="text-align: center; vertical-align: middle" onclick="$('#filePhoto').click()">
|
||||
<br>Click here <br> or <br> drag and drop <br>an image
|
||||
<img/>
|
||||
<input type="file" name="userprofile_picture" id="filePhoto" />
|
||||
</div>
|
||||
<div class="btn-group" role="group" aria-label="Basic example">
|
||||
<button type="button" class="btn btn-primary" id="submit-button">Submit</button>
|
||||
<button type="button" class="btn btn-secondary" id="clear-button">Clear</button>
|
||||
</div>
|
||||
|
||||
|
||||
</div>
|
||||
<script src="../js/image-upload-input.js"></script>
|
||||
<link rel="stylesheet" href="https://fengyuanchen.github.io/cropper/css/cropper.css">
|
||||
<script src="https://fengyuanchen.github.io/cropper/js/cropper.js"></script>
|
||||
<script src="../static/js/image-upload-input.js"></script>
|
||||
|
||||
7
build/lib/gradio/templates/label_output.html
Normal file
7
build/lib/gradio/templates/label_output.html
Normal file
@ -0,0 +1,7 @@
|
||||
<div class="gradio output classifier">
|
||||
<div class="role">Output</div>
|
||||
<div class="output_class"></div>
|
||||
<div class="confidence_intervals">
|
||||
</div>
|
||||
</div>
|
||||
<script src="../static/js/class-output.js"></script>
|
||||
Binary file not shown.
BIN
dist/gradio-0.3.1.tar.gz
vendored
Normal file
BIN
dist/gradio-0.3.1.tar.gz
vendored
Normal file
Binary file not shown.
@ -1,6 +1,6 @@
|
||||
Metadata-Version: 1.0
|
||||
Name: gradio
|
||||
Version: 0.2.1
|
||||
Version: 0.3.1
|
||||
Summary: Python library for easily interacting with trained machine learning models
|
||||
Home-page: https://github.com/abidlabs/gradio
|
||||
Author: Abubakar Abid
|
||||
|
||||
@ -2,6 +2,7 @@ MANIFEST.in
|
||||
README.md
|
||||
setup.py
|
||||
gradio/__init__.py
|
||||
gradio/imagenet_class_labels.py
|
||||
gradio/inputs.py
|
||||
gradio/interface.py
|
||||
gradio/networking.py
|
||||
@ -12,50 +13,10 @@ gradio.egg-info/SOURCES.txt
|
||||
gradio.egg-info/dependency_links.txt
|
||||
gradio.egg-info/requires.txt
|
||||
gradio.egg-info/top_level.txt
|
||||
gradio/css/.DS_Store
|
||||
gradio/css/bootstrap-grid.css
|
||||
gradio/css/bootstrap-grid.css.map
|
||||
gradio/css/bootstrap-grid.min.css
|
||||
gradio/css/bootstrap-grid.min.css.map
|
||||
gradio/css/bootstrap-reboot.css
|
||||
gradio/css/bootstrap-reboot.css.map
|
||||
gradio/css/bootstrap-reboot.min.css
|
||||
gradio/css/bootstrap-reboot.min.css.map
|
||||
gradio/css/bootstrap.css
|
||||
gradio/css/bootstrap.css.map
|
||||
gradio/css/bootstrap.min.css
|
||||
gradio/css/bootstrap.min.css.map
|
||||
gradio/css/draw-a-digit.css
|
||||
gradio/css/dropzone.css
|
||||
gradio/css/index.css
|
||||
gradio/js/all-io.js
|
||||
gradio/js/audio-input.js
|
||||
gradio/js/bootstrap-notify.min.js
|
||||
gradio/js/bootstrap.bundle.js
|
||||
gradio/js/bootstrap.bundle.js.map
|
||||
gradio/js/bootstrap.bundle.min.js
|
||||
gradio/js/bootstrap.bundle.min.js.map
|
||||
gradio/js/bootstrap.js
|
||||
gradio/js/bootstrap.js.map
|
||||
gradio/js/bootstrap.min.js
|
||||
gradio/js/bootstrap.min.js.map
|
||||
gradio/js/class-output.js
|
||||
gradio/js/draw-a-digit.js
|
||||
gradio/js/dropzone.js
|
||||
gradio/js/emotion-detector.js
|
||||
gradio/js/image-upload-input.js
|
||||
gradio/js/jquery-3.3.1.min.js
|
||||
gradio/js/sketchpad-input.js
|
||||
gradio/js/textbox-input.js
|
||||
gradio/js/textbox-output.js
|
||||
gradio/js/webcam-input.js
|
||||
gradio/templates/all_io.html
|
||||
gradio/templates/audio_input.html
|
||||
gradio/templates/class_output.html
|
||||
gradio/templates/draw_a_digit.html
|
||||
gradio/templates/emotion_detector.html
|
||||
gradio/templates/base_template.html
|
||||
gradio/templates/image_upload_input.html
|
||||
gradio/templates/sketchpad_input.html
|
||||
gradio/templates/textbox_input.html
|
||||
gradio/templates/textbox_output.html
|
||||
gradio/templates/webcam_input.html
|
||||
gradio/templates/label_output.html
|
||||
test/test_inputs.py
|
||||
test/test_interface.py
|
||||
test/test_networking.py
|
||||
test/test_outputs.py
|
||||
@ -69,7 +69,6 @@ class Label(AbstractOutput):
|
||||
"""
|
||||
"""
|
||||
response = dict()
|
||||
print('dddddddddddddddddddd', self.get_label_name(499))
|
||||
# TODO(abidlabs): check if list, if so convert to numpy array
|
||||
if isinstance(prediction, np.ndarray):
|
||||
prediction = prediction.squeeze()
|
||||
|
||||
@ -27,19 +27,19 @@ try {
|
||||
sleep(300).then(() => {
|
||||
// $(".output_class").text(event.data);
|
||||
var data = JSON.parse(event.data)
|
||||
data = {
|
||||
label: "happy",
|
||||
confidences : [
|
||||
{
|
||||
label : "happy",
|
||||
confidence: 0.7
|
||||
},
|
||||
{
|
||||
label : "sad",
|
||||
confidence: 0.3
|
||||
},
|
||||
]
|
||||
}
|
||||
// data = {
|
||||
// label: "happy",
|
||||
// confidences : [
|
||||
// {
|
||||
// label : "happy",
|
||||
// confidence: 0.7
|
||||
// },
|
||||
// {
|
||||
// label : "sad",
|
||||
// confidence: 0.3
|
||||
// },
|
||||
// ]
|
||||
// }
|
||||
$(".output_class").text(data["label"])
|
||||
$(".confidence_intervals").empty()
|
||||
if ("confidences" in data) {
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
var cropper;
|
||||
// var cropper;
|
||||
|
||||
$('body').on('click', ".input_image.drop_mode", function (e) {
|
||||
$(this).parent().find(".hidden_upload").click();
|
||||
@ -17,10 +17,10 @@ function loadPreviewFromFiles(files) {
|
||||
$(".input_image").removeClass("drop_mode")
|
||||
var image = $(".input_image img")
|
||||
image.attr("src", this.result)
|
||||
image.cropper({aspectRatio : 1.0});
|
||||
if (!cropper) {
|
||||
cropper = image.data('cropper');
|
||||
}
|
||||
// image.cropper({aspectRatio : 1.0});
|
||||
// if (!cropper) {
|
||||
// cropper = image.data('cropper');
|
||||
// }
|
||||
}
|
||||
}
|
||||
|
||||
@ -50,10 +50,10 @@ $('body').on('click', '.submit', function(e) {
|
||||
})
|
||||
|
||||
$('body').on('click', '.clear', function(e) {
|
||||
if (cropper) {
|
||||
cropper.destroy();
|
||||
cropper = null
|
||||
}
|
||||
// if (cropper) {
|
||||
// cropper.destroy();
|
||||
// cropper = null
|
||||
// }
|
||||
$(".input_caption").show()
|
||||
$(".input_image img").removeAttr("src");
|
||||
$(".input_image").addClass("drop_mode")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user