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added networking methods

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Abubakar Abid 2019-02-17 17:47:15 -08:00
parent 9ed6d3b8d7
commit e993131a4b
10 changed files with 808 additions and 291 deletions
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.gitignore vendored
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{
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train shape: (60000, 28, 28, 1)\n",
"60000 train samples\n",
"10000 test samples\n",
"Train on 60000 samples, validate on 10000 samples\n",
"Epoch 1/12\n"
]
}
],
"source": [
"'''Trains a simple convnet on the MNIST dataset.\n",
"Gets to 99.25% test accuracy after 12 epochs\n",
"(there is still a lot of margin for parameter tuning).\n",
"16 seconds per epoch on a GRID K520 GPU.\n",
"'''\n",
"\n",
"from __future__ import print_function\n",
"import keras\n",
"from keras.datasets import mnist\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"from keras import backend as K\n",
"\n",
"batch_size = 128\n",
"num_classes = 10\n",
"epochs = 12\n",
"\n",
"# input image dimensions\n",
"img_rows, img_cols = 28, 28\n",
"\n",
"# the data, split between train and test sets\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
"if K.image_data_format() == 'channels_first':\n",
" x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
" x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
" input_shape = (1, img_rows, img_cols)\n",
"else:\n",
" x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
" x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
" input_shape = (img_rows, img_cols, 1)\n",
"\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"x_train /= 255\n",
"x_test /= 255\n",
"print('x_train shape:', x_train.shape)\n",
"print(x_train.shape[0], 'train samples')\n",
"print(x_test.shape[0], 'test samples')\n",
"\n",
"# convert class vectors to binary class matrices\n",
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)\n",
"\n",
"model = Sequential()\n",
"model.add(Conv2D(32, kernel_size=(3, 3),\n",
" activation='relu',\n",
" input_shape=input_shape))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Flatten())\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes, activation='softmax'))\n",
"\n",
"model.compile(loss=keras.losses.categorical_crossentropy,\n",
" optimizer=keras.optimizers.Adadelta(),\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_test, y_test))\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])\n"
]
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@ -15,23 +111,73 @@
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import sklearn\n",
"import gradio\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
"from keras.losses import categorical_crossentropy\n",
"from keras.optimizers import Adam\n",
"from keras.regularizers import l2\n",
"from keras.callbacks import ReduceLROnPlateau, TensorBoard, EarlyStopping, ModelCheckpoint\n",
"from keras.models import load_model\n",
"\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"model = load_model('model.h5') # found random emotion detector model on github ''(its not very accurate)"
]
},
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]
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{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\islam\\Repos\\gradio\\gradio.py:194: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 194 of the file C:\\Users\\islam\\Repos\\gradio\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
"\n",
" input_soup = BeautifulSoup(input_page.read())\n",
"C:\\Users\\islam\\Repos\\gradio\\gradio.py:195: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 195 of the file C:\\Users\\islam\\Repos\\gradio\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
"\n",
" output_soup = BeautifulSoup(output_page.read())\n",
"C:\\Users\\islam\\Repos\\gradio\\gradio.py:199: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 199 of the file C:\\Users\\islam\\Repos\\gradio\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
"\n",
" all_io_soup = BeautifulSoup(all_io_page.read())\n",
"Error in connection handler\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\islam\\Repos\\gradio\\gradio.py\", line 226, in communicate\n",
" processed_input = self.input_interface._pre_process(await websocket.recv())\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\protocol.py\", line 434, in recv\n",
" yield from self.ensure_open()\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\protocol.py\", line 646, in ensure_open\n",
" ) from self.transfer_data_exc\n",
"websockets.exceptions.ConnectionClosed: WebSocket connection is closed: code = 1001 (going away), no reason\n"
]
}
],
"source": [
"iface = gradio.Interface(input='webcam',output='class',model_obj=model, model_type='keras')\n",
"iface.launch()"
]
},
{
"cell_type": "code",
"execution_count": 2,
@ -41,82 +187,126 @@
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n"
"serving at port 7000\n"
]
}
],
"source": [
"model = load_model('model.h5') # found random emotion detector model on github ''(its not very accurate)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py:191: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 191 of the file C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
"\n",
" input_soup = BeautifulSoup(input_page.read())\n",
"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py:192: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 192 of the file C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
"\n",
" output_soup = BeautifulSoup(output_page.read())\n",
"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py:196: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 196 of the file C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
"\n",
" all_io_soup = BeautifulSoup(all_io_page.read())\n",
"Error in connection handler\n",
"Exception in thread Thread-6:\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py\", line 223, in communicate\n",
" processed_input = self.input_interface._pre_process(await websocket.recv())\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\protocol.py\", line 434, in recv\n",
" yield from self.ensure_open()\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\protocol.py\", line 646, in ensure_open\n",
" ) from self.transfer_data_exc\n",
"websockets.exceptions.ConnectionClosed: WebSocket connection is closed: code = 1001 (going away), no reason\n",
"Error in connection handler\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py\", line 223, in communicate\n",
" processed_input = self.input_interface._pre_process(await websocket.recv())\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\protocol.py\", line 434, in recv\n",
" yield from self.ensure_open()\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\protocol.py\", line 646, in ensure_open\n",
" ) from self.transfer_data_exc\n",
"websockets.exceptions.ConnectionClosed: WebSocket connection is closed: code = 1001 (going away), no reason\n"
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\threading.py\", line 916, in _bootstrap_inner\n",
" self.run()\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\threading.py\", line 864, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\socketserver.py\", line 236, in serve_forever\n",
" ready = selector.select(poll_interval)\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\selectors.py\", line 323, in select\n",
" r, w, _ = self._select(self._readers, self._writers, [], timeout)\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\selectors.py\", line 314, in _select\n",
" r, w, x = select.select(r, w, w, timeout)\n",
"OSError: [WinError 10038] An operation was attempted on something that is not a socket\n",
"\n"
]
}
],
"source": [
"iface = gradio.Interface(input='sketchpad',output='class',model_obj=model, model_type='keras')\n",
"iface.launch()"
"import http.server\n",
"import socketserver\n",
"import os\n",
"import threading\n",
"\n",
"TEMPLATE_DIRECTORY = 'templates'\n",
"PORT = 7000\n",
"\n",
"\n",
"web_dir = os.path.realpath(TEMPLATE_DIRECTORY)\n",
"os.chdir(web_dir)\n",
"Handler = http.server.SimpleHTTPRequestHandler\n",
"\n",
"with socketserver.TCPServer((\"\", PORT), Handler) as httpd:\n",
" print(\"serving at port\", PORT)\n",
" t = threading.Thread(target=httpd.serve_forever)\n",
" t.start()\n",
"\n",
"path_to_server = 'localhost:{}/'.format(PORT)\n",
"os.chdir('..') # TODO(abidlabs): make this better\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"serving at port 7000\n"
]
}
],
"source": [
"import http.server\n",
"import socketserver\n",
"import os\n",
"import threading\n",
"\n",
"TEMPLATE_DIRECTORY = 'templates'\n",
"PORT = 7000\n",
"\n",
"\n",
"web_dir = os.path.realpath(TEMPLATE_DIRECTORY)\n",
"os.chdir(web_dir)\n",
"Handler = http.server.SimpleHTTPRequestHandler\n",
"\n",
"with socketserver.TCPServer((\"\", PORT), Handler) as httpd:\n",
" print(\"serving at port\", PORT)\n",
" httpd.serve_forever()\n",
"# t = threading.Thread(target=httpd.serve_forever)\n",
"# t.start()\n",
"\n",
"path_to_server = 'localhost:{}/'.format(PORT)\n",
"os.chdir('..') # TODO(abidlabs): make this better "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import subprocess"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<subprocess.Popen at 0x16a3f8aa390>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subprocess.Popen(['python', '-m', 'http.server', '6001'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3.6 (tensorflow)",
"language": "python",
"name": "python3"
"name": "tensorflow"
},
"language_info": {
"codemirror_mode": {
@ -128,7 +318,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
"version": "3.6.7"
}
},
"nbformat": 4,

230
Emotion Detector.ipynb
View File

@ -1,5 +1,12 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
@ -15,23 +22,73 @@
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import sklearn\n",
"import gradio\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
"from keras.losses import categorical_crossentropy\n",
"from keras.optimizers import Adam\n",
"from keras.regularizers import l2\n",
"from keras.callbacks import ReduceLROnPlateau, TensorBoard, EarlyStopping, ModelCheckpoint\n",
"from keras.models import load_model\n",
"\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"model = load_model('model.h5') # found random emotion detector model on github ''(its not very accurate)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:6002/templates/tmp_html.html\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\islam\\Repos\\gradio\\gradio.py:194: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 194 of the file C:\\Users\\islam\\Repos\\gradio\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
"\n",
" input_soup = BeautifulSoup(input_page.read())\n",
"C:\\Users\\islam\\Repos\\gradio\\gradio.py:195: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 195 of the file C:\\Users\\islam\\Repos\\gradio\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
"\n",
" output_soup = BeautifulSoup(output_page.read())\n",
"C:\\Users\\islam\\Repos\\gradio\\gradio.py:199: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 199 of the file C:\\Users\\islam\\Repos\\gradio\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
"\n",
" all_io_soup = BeautifulSoup(all_io_page.read())\n",
"Error in connection handler\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\islam\\Repos\\gradio\\gradio.py\", line 226, in communicate\n",
" processed_input = self.input_interface._pre_process(await websocket.recv())\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\protocol.py\", line 434, in recv\n",
" yield from self.ensure_open()\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\protocol.py\", line 646, in ensure_open\n",
" ) from self.transfer_data_exc\n",
"websockets.exceptions.ConnectionClosed: WebSocket connection is closed: code = 1001 (going away), no reason\n"
]
}
],
"source": [
"iface = gradio.Interface(input='webcam',output='class',model_obj=model, model_type='keras')\n",
"iface.launch()"
]
},
{
"cell_type": "code",
"execution_count": 2,
@ -41,71 +98,126 @@
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n"
"serving at port 7000\n"
]
}
],
"source": [
"model = load_model('model.h5') # found random emotion detector model on github ''(its not very accurate)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py:191: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 191 of the file C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
"\n",
" input_soup = BeautifulSoup(input_page.read())\n",
"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py:192: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 192 of the file C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
"\n",
" output_soup = BeautifulSoup(output_page.read())\n",
"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py:196: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 196 of the file C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
"\n",
" all_io_soup = BeautifulSoup(all_io_page.read())\n",
"Error in connection handler\n",
"Exception in thread Thread-6:\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\ALI\\Desktop\\gradiome\\gradio.py\", line 223, in communicate\n",
" processed_input = self.input_interface._pre_process(await websocket.recv())\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\protocol.py\", line 434, in recv\n",
" yield from self.ensure_open()\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\protocol.py\", line 646, in ensure_open\n",
" ) from self.transfer_data_exc\n",
"websockets.exceptions.ConnectionClosed: WebSocket connection is closed: code = 1001 (going away), no reason\n"
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\threading.py\", line 916, in _bootstrap_inner\n",
" self.run()\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\threading.py\", line 864, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\socketserver.py\", line 236, in serve_forever\n",
" ready = selector.select(poll_interval)\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\selectors.py\", line 323, in select\n",
" r, w, _ = self._select(self._readers, self._writers, [], timeout)\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\selectors.py\", line 314, in _select\n",
" r, w, x = select.select(r, w, w, timeout)\n",
"OSError: [WinError 10038] An operation was attempted on something that is not a socket\n",
"\n"
]
}
],
"source": [
"iface = gradio.Interface(input='webcam',output='class',model_obj=model, model_type='keras')\n",
"iface.launch()"
"import http.server\n",
"import socketserver\n",
"import os\n",
"import threading\n",
"\n",
"TEMPLATE_DIRECTORY = 'templates'\n",
"PORT = 7000\n",
"\n",
"\n",
"web_dir = os.path.realpath(TEMPLATE_DIRECTORY)\n",
"os.chdir(web_dir)\n",
"Handler = http.server.SimpleHTTPRequestHandler\n",
"\n",
"with socketserver.TCPServer((\"\", PORT), Handler) as httpd:\n",
" print(\"serving at port\", PORT)\n",
" t = threading.Thread(target=httpd.serve_forever)\n",
" t.start()\n",
"\n",
"path_to_server = 'localhost:{}/'.format(PORT)\n",
"os.chdir('..') # TODO(abidlabs): make this better\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"serving at port 7000\n"
]
}
],
"source": [
"import http.server\n",
"import socketserver\n",
"import os\n",
"import threading\n",
"\n",
"TEMPLATE_DIRECTORY = 'templates'\n",
"PORT = 7000\n",
"\n",
"\n",
"web_dir = os.path.realpath(TEMPLATE_DIRECTORY)\n",
"os.chdir(web_dir)\n",
"Handler = http.server.SimpleHTTPRequestHandler\n",
"\n",
"with socketserver.TCPServer((\"\", PORT), Handler) as httpd:\n",
" print(\"serving at port\", PORT)\n",
" httpd.serve_forever()\n",
"# t = threading.Thread(target=httpd.serve_forever)\n",
"# t.start()\n",
"\n",
"path_to_server = 'localhost:{}/'.format(PORT)\n",
"os.chdir('..') # TODO(abidlabs): make this better "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import subprocess"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<subprocess.Popen at 0x16a3f8aa390>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subprocess.Popen(['python', '-m', 'http.server', '6001'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3.6 (tensorflow)",
"language": "python",
"name": "python3"
"name": "tensorflow"
},
"language_info": {
"codemirror_mode": {
@ -117,7 +229,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
"version": "3.6.7"
}
},
"nbformat": 4,

121
Test Notebook.ipynb Normal file
View File

@ -0,0 +1,121 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"import numpy as np\n",
"import sklearn\n",
"import gradio\n",
"from keras.models import load_model\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\saving.py:327: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
" warnings.warn('Error in loading the saved optimizer '\n"
]
}
],
"source": [
"model = load_model('.models/mnist-cnn.h5')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:6002/templates/tmp_html.html\n"
]
},
{
"ename": "OSError",
"evalue": "[Errno 10048] error while attempting to bind on address ('127.0.0.1', 5680): only one usage of each socket address (protocol/network address/port) is normally permitted",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\u001b[0m in \u001b[0;36mcreate_server\u001b[1;34m(self, protocol_factory, host, port, family, flags, sock, backlog, ssl, reuse_address, reuse_port)\u001b[0m\n\u001b[0;32m 1050\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1051\u001b[1;33m \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1052\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mOSError\u001b[0m: [WinError 10048] Only one usage of each socket address (protocol/network address/port) is normally permitted",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-5-277844517c06>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0miface\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgradio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInterface\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'sketchpad'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel_obj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'keras'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0miface\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\Repos\\gradio\\gradio.py\u001b[0m in \u001b[0;36mlaunch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[0mstart_server\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwebsockets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mLOCALHOST_IP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCKET_PORT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 246\u001b[1;33m \u001b[0masyncio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart_server\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 247\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 248\u001b[0m \u001b[0masyncio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_forever\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\nest_asyncio.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[0mrun_once\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 60\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_until_complete_orig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\asyncio\\tasks.py\u001b[0m in \u001b[0;36m_step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[1;31m# We use the `send` method directly, because coroutines\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[1;31m# don't have `__iter__` and `__next__` methods.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 181\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\asyncio\\tasks.py\u001b[0m in \u001b[0;36m_wrap_awaitable\u001b[1;34m(awaitable)\u001b[0m\n\u001b[0;32m 535\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mlater\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mwrapped\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mTask\u001b[0m \u001b[0mby\u001b[0m \u001b[0mensure_future\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 536\u001b[0m \"\"\"\n\u001b[1;32m--> 537\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mawaitable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__await__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 538\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\py35\\server.py\u001b[0m in \u001b[0;36m__await_impl__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;31m# Duplicated with __iter__ because Python 3.7 requires an async function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;31m# (as explained in __await__ below) which Python 3.4 doesn't support.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mserver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mawait\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_creating_server\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mws_server\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mserver\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mws_server\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\u001b[0m in \u001b[0;36mcreate_server\u001b[1;34m(self, protocol_factory, host, port, family, flags, sock, backlog, ssl, reuse_address, reuse_port)\u001b[0m\n\u001b[0;32m 1053\u001b[0m raise OSError(err.errno, 'error while attempting '\n\u001b[0;32m 1054\u001b[0m \u001b[1;34m'to bind on address %r: %s'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1055\u001b[1;33m % (sa, err.strerror.lower()))\n\u001b[0m\u001b[0;32m 1056\u001b[0m \u001b[0mcompleted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1057\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mOSError\u001b[0m: [Errno 10048] error while attempting to bind on address ('127.0.0.1', 5680): only one usage of each socket address (protocol/network address/port) is normally permitted"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error in connection handler\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\islam\\Repos\\gradio\\gradio.py\", line 231, in communicate\n",
" processed_output = self.output_interface._post_process(prediction)\n",
" File \"C:\\Users\\islam\\Repos\\gradio\\gradio.py\", line 165, in _post_process\n",
" return emotion_dict[prediction]\n",
"KeyError: 7\n"
]
}
],
"source": [
"iface = gradio.Interface(input='sketchpad',output='class',model_obj=model, model_type='keras')\n",
"iface.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6 (tensorflow)",
"language": "python",
"name": "tensorflow"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

100
gradio.py
View File

@ -9,6 +9,8 @@ import numpy as np
import os
import webbrowser
from bs4 import BeautifulSoup
import networking
import preprocessing_utils
nest_asyncio.apply()
@ -16,61 +18,6 @@ LOCALHOST_IP = '127.0.0.1'
SOCKET_PORT = 5680
import requests, zipfile, io
def resize_and_crop(img, size, crop_type='top'):
"""
Resize and crop an image to fit the specified size.
args:
img_path: path for the image to resize.
modified_path: path to store the modified image.
size: `(width, height)` tuple.
crop_type: can be 'top', 'middle' or 'bottom', depending on this
value, the image will cropped getting the 'top/left', 'midle' or
'bottom/rigth' of the image to fit the size.
raises:
Exception: if can not open the file in img_path of there is problems
to save the image.
ValueError: if an invalid `crop_type` is provided.
"""
# Get current and desired ratio for the images
img_ratio = img.size[0] / float(img.size[1])
ratio = size[0] / float(size[1])
# The image is scaled/cropped vertically or horizontally depending on the ratio
if ratio > img_ratio:
img = img.resize((size[0], size[0] * img.size[1] / img.size[0]),
Image.ANTIALIAS)
# Crop in the top, middle or bottom
if crop_type == 'top':
box = (0, 0, img.size[0], size[1])
elif crop_type == 'middle':
box = (0, (img.size[1] - size[1]) / 2, img.size[0], (img.size[1] + size[1]) / 2)
elif crop_type == 'bottom':
box = (0, img.size[1] - size[1], img.size[0], img.size[1])
else:
raise ValueError('ERROR: invalid value for crop_type')
img = img.crop(box)
elif ratio < img_ratio:
img = img.resize((size[1] * img.size[0] / img.size[1], size[1]),
Image.ANTIALIAS)
# Crop in the top, middle or bottom
if crop_type == 'top':
box = (0, 0, size[0], img.size[1])
elif crop_type == 'middle':
box = ((img.size[0] - size[0]) / 2, 0, (img.size[0] + size[0]) / 2, img.size[1])
elif crop_type == 'bottom':
box = (img.size[0] - size[0], 0, img.size[0], img.size[1])
else:
raise ValueError('ERROR: invalid value for crop_type')
img = img.crop(box)
else:
img = img.resize((size[0], size[1]),
Image.ANTIALIAS)
# If the scale is the same, we do not need to crop
return img
class AbstractInput(ABC):
"""
An abstract class for defining the methods that all gradio inputs should have.
@ -79,7 +26,7 @@ class AbstractInput(ABC):
def __init__(self):
super().__init__()
@abstractmethod
def _get_template_path(self):
"""
@ -131,8 +78,8 @@ class Sketchpad(AbstractInput):
image_encoded = content.split(',')[1]
body = base64.decodebytes(image_encoded.encode('utf-8'))
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
im = resize_and_crop(im, (28, 28))
array = np.array(im).flatten().reshape(1, -1)
im = preprocessing_utils.resize_and_crop(im, (28, 28))
array = np.array(im).flatten().reshape(1, 28, 28, 1)
return array
@ -148,10 +95,11 @@ class Webcam(AbstractInput):
image_encoded = content.split(',')[1]
body = base64.decodebytes(image_encoded.encode('utf-8'))
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
im = resize_and_crop(im, (48, 48))
im = preprocessing_utils.resize_and_crop(im, (48, 48))
array = np.array(im).flatten().reshape(1,48,48,1)
return array
class Class(AbstractOutput):
def _get_template_path(self):
@ -160,8 +108,7 @@ class Class(AbstractOutput):
def _post_process(self, prediction):
"""
"""
emotion_dict = {0: "Angry", 1: "Disgust", 2: "Fear", 3: "Happy", 4: "Sad", 5: "Surprise", 6: "Neutral"}
return emotion_dict[prediction]
return prediction
registry = {
'webcam':Webcam,
@ -169,9 +116,11 @@ registry = {
'class' :Class,
}
class Interface():
"""
"""
build_template_path = 'templates/tmp_html.html'
def __init__(self, input, output, model_obj, model_type, **model_params):
"""
@ -190,29 +139,31 @@ class Interface():
output_template_path = self.output_interface._get_template_path()
input_page = open(input_template_path)
output_page = open(output_template_path)
input_soup = BeautifulSoup(input_page.read())
output_soup = BeautifulSoup(output_page.read())
input_soup = BeautifulSoup(input_page.read(), features="html.parser")
output_soup = BeautifulSoup(output_page.read(), features="html.parser")
all_io_url = 'templates/all_io.html'
all_io_page = open(all_io_url)
all_io_soup = BeautifulSoup(all_io_page.read())
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("templates/tmp_html.html", "w")
f = open(self.build_template_path, "w")
f.write(str(all_io_soup.prettify))
return 'templates/tmp_html.html'
return self.build_template_path
def predict(self, array):
if self.model_type=='sklearn':
return self.model_obj.predict(array)[0]
elif self.model_type=='keras':
return self.model_obj.predict(array)[0].argmax()
elif self.model_type=='func':
return self.model_obj(array)
else:
raise ValueError('model_type must be sklearn.')
raise ValueError('model_type must be one of: "sklearn" or "keras" or "func".')
async def communicate(self, websocket, path):
@ -227,12 +178,23 @@ class Interface():
processed_output = self.output_interface._post_process(prediction)
await websocket.send(str(processed_output))
def launch(self):
def launch(self, share_link=True):
"""
Standard method shared by interfaces that launches a websocket at a specified IP address.
"""
webbrowser.open('file://' + os.path.realpath(self._build_template()))
path_to_server = networking.start_simple_server()
path_to_template = self._build_template()
chrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s' # TODO(abidlabs): remove
print(path_to_server + path_to_template)
webbrowser.get(chrome_path).open(path_to_server + path_to_template) # TODO(abidlabs): fix this
start_server = websockets.serve(self.communicate, LOCALHOST_IP, SOCKET_PORT)
if share_link:
ngrok_url = networking.setup_ngrok()
print("Model accessiable for 8 hours at: {}".format(ngrok_url))
asyncio.get_event_loop().run_until_complete(start_server)
try:
asyncio.get_event_loop().run_forever()

0
inputs.py Normal file
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30
networking.py Normal file
View File

@ -0,0 +1,30 @@
import subprocess
import requests
import zipfile
import io
import sys
INITIAL_PORT_VALUE = '6002'
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",
"win32": "https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-windows-amd64.zip",
}
def start_simple_server():
subprocess.Popen(['python', '-m', 'http.server', INITIAL_PORT_VALUE])
return 'http://localhost:{}/'.format(INITIAL_PORT_VALUE)
def download_ngrok():
try:
zip_file_url = NGROK_ZIP_URLS[sys.platform]
except KeyError:
print("Sorry, we don't currently support your operating system, please leave us a note on GitHub, and we'll look into it!")
return
r = requests.get(zip_file_url)
z = zipfile.ZipFile(io.BytesIO(r.content))
z.extractall("gradio/")
def setup_ngrok(local_port):

53
preprocessing_utils.py Normal file
View File

@ -0,0 +1,53 @@
from PIL import Image
def resize_and_crop(img, size, crop_type='top'):
"""
Resize and crop an image to fit the specified size.
args:
img_path: path for the image to resize.
modified_path: path to store the modified image.
size: `(width, height)` tuple.
crop_type: can be 'top', 'middle' or 'bottom', depending on this
value, the image will cropped getting the 'top/left', 'midle' or
'bottom/rigth' of the image to fit the size.
raises:
Exception: if can not open the file in img_path of there is problems
to save the image.
ValueError: if an invalid `crop_type` is provided.
"""
# Get current and desired ratio for the images
img_ratio = img.size[0] / float(img.size[1])
ratio = size[0] / float(size[1])
# The image is scaled/cropped vertically or horizontally depending on the ratio
if ratio > img_ratio:
img = img.resize((size[0], size[0] * img.size[1] / img.size[0]),
Image.ANTIALIAS)
# Crop in the top, middle or bottom
if crop_type == 'top':
box = (0, 0, img.size[0], size[1])
elif crop_type == 'middle':
box = (0, (img.size[1] - size[1]) / 2, img.size[0], (img.size[1] + size[1]) / 2)
elif crop_type == 'bottom':
box = (0, img.size[1] - size[1], img.size[0], img.size[1])
else:
raise ValueError('ERROR: invalid value for crop_type')
img = img.crop(box)
elif ratio < img_ratio:
img = img.resize((size[1] * img.size[0] / img.size[1], size[1]),
Image.ANTIALIAS)
# Crop in the top, middle or bottom
if crop_type == 'top':
box = (0, 0, size[0], img.size[1])
elif crop_type == 'middle':
box = ((img.size[0] - size[0]) / 2, 0, (img.size[0] + size[0]) / 2, img.size[1])
elif crop_type == 'bottom':
box = (img.size[0] - size[0], 0, img.size[0], img.size[1])
else:
raise ValueError('ERROR: invalid value for crop_type')
img = img.crop(box)
else:
img = img.resize((size[0], size[1]),
Image.ANTIALIAS)
# If the scale is the same, we do not need to crop
return img

44
templates/tmp_html.html
View File

@ -1,8 +1,12 @@
<bound method Tag.prettify of <!DOCTYPE html>
<html><head><script src="../js/all-io.js"></script>
<bound method Tag.prettify of
<!DOCTYPE doctype html>
<script src="../js/all-io.js"></script>
<script crossorigin="anonymous" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script>
<script src="../js/bootstrap.min.js"></script>
<script src="../js/bootstrap-notify.min.js"></script>
<html lang="en">
<head>
<meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1, shrink-to-fit=no" name="viewport"/>
<meta content="" name="description"/>
@ -12,7 +16,8 @@
<link href="../css/bootstrap.min.css" rel="stylesheet"/>
<link href="../css/draw-a-digit.css" rel="stylesheet"/>
<link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet"/>
</head><body>
</head>
<body>
<nav class="navbar navbar-expand-md navbar-dark bg-dark fixed-top">
<a class="navbar-brand" href="#">Gradio</a>
<button aria-controls="navbarsExampleDefault" aria-expanded="false" aria-label="Toggle navigation" class="navbar-toggler" data-target="#navbarsExampleDefault" data-toggle="collapse" type="button">
@ -25,7 +30,7 @@
<!--<li class="nav-item">-->
<!--<a class="nav-link" href="#">Help</a>-->
<!--</li>-->
<li class="nav-item ">
<li class="nav-item">
<a class="nav-link" href="http://www.siliconprep.com"><em>Gradio</em>, a tool by Silicon School</a>
</li>
</ul>
@ -35,35 +40,27 @@
<div class="row">
<!-- INPUT
====================================================================================================================================================== -->
<html><body><div class="col-6">
<h5>Webcam Input:</h5>
<canvas height="400" id="canvas" style="background-color:black" width="400">
<video id="video" playsinline="" style=" -moz-transform: scaleX(-1);
-o-transform: scaleX(-1);
-webkit-transform: scaleX(-1);
transform: scaleX(-1);
display: none;
">
</video>
</canvas><br/>
<div class="col-6">
<h5>Sketch Pad Input: (Use your cursor to draw)</h5>
<canvas height="400" id="canvas" width="400"></canvas><br/>
<div aria-label="Basic example" class="btn-group" role="group">
<button class="btn btn-primary" id="capture-button" type="button">Capture</button>
<!-- <button type="button" class="btn btn-primary" id="submit-button">Recognize</button> -->
<button class="btn btn-primary" id="submit-button" type="button">Recognize</button>
<button class="btn btn-secondary" id="clear-button" type="button">Clear</button>
</div>
</div>
<script src="../js/webcam-input.js"></script>
</body></html>
<script src="../js/sketchpad-input.js"></script>
<!-- OUTPUT
====================================================================================================================================================== -->
<html><body><div class="col-6">
<div class="col-6">
<h5>Text Output:</h5>
<canvas height="400" id="predict_canvas" style="background-color:black" width="400"></canvas><br/>
</div>
<script src="../js/class-output.js"></script>
</body></html>
</div>
</main><!-- /.container -->
</main></body></html>
<!-- /.container -->
<footer class="footer">
<div class="container">
<span class="text-muted">
@ -82,5 +79,6 @@
<!-- Bootstrap core JavaScript
================================================== -->
<!-- Placed at the end of the document so the pages load faster -->
</body></html>
>