mirror of
https://github.com/gradio-app/gradio.git
synced 2025-02-17 11:29:58 +08:00
Merge branch 'master' of https://github.com/abidlabs/gradiome
g the commit. merge
This commit is contained in:
Ali Abdalla
commit
a812d5f555
@ -4,39 +4,43 @@
|
||||
"cell_type": "code",
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||||
"execution_count": 1,
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||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
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||||
"ename": "SyntaxError",
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||||
"evalue": "invalid syntax (networking.py, line 200)",
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"output_type": "error",
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||||
"traceback": [
|
||||
"Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
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||||
" File \u001b[0;32m\"C:\\Users\\islam\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\"\u001b[0m, line \u001b[0;32m2961\u001b[0m, in \u001b[0;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n",
|
||||
" File \u001b[0;32m\"<ipython-input-1-ab911fe0150a>\"\u001b[0m, line \u001b[0;32m4\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n import gradio\n",
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||||
" File \u001b[0;32m\"C:\\Users\\islam\\Repos\\gradio\\gradio\\__init__.py\"\u001b[0m, line \u001b[0;32m1\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n from gradio.interface import Interface # This makes it possible to import `Interface` as `gradio.Interface`.\n",
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||||
"\u001b[1;36m File \u001b[1;32m\"C:\\Users\\islam\\Repos\\gradio\\gradio\\interface.py\"\u001b[1;36m, line \u001b[1;32m12\u001b[1;36m, in \u001b[1;35m<module>\u001b[1;36m\u001b[0m\n\u001b[1;33m from gradio import networking\u001b[0m\n",
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"\u001b[1;36m File \u001b[1;32m\"C:\\Users\\islam\\Repos\\gradio\\gradio\\networking.py\"\u001b[1;36m, line \u001b[1;32m200\u001b[0m\n\u001b[1;33m except AccessDenied, NoSuchProcess:\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
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]
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||||
}
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],
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||||
"outputs": [],
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||||
"source": [
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||||
"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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||||
"import tensorflow as tf\n",
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||||
"import gradio"
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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": 2,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
|
||||
"x_train, x_test = x_train / 255.0, x_test / 255.0"
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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": 3,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"outputs": [
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||||
{
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"data": {
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"text/plain": [
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||||
"(60000, 28, 28)"
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||||
]
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||||
},
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||||
"execution_count": 3,
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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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||||
"source": [
|
||||
"def test(x):\n",
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" return x.upper()\n",
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"\n",
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||||
"def test2(x):\n",
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||||
" return x.lower()"
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||||
"x_train.shape"
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]
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||||
},
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{
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@ -47,7 +51,16 @@
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||||
},
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||||
"outputs": [],
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||||
"source": [
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||||
"iface = gradio.Interface(inputs=\"textbox\", outputs=\"textbox\", model=test, model_type='function')"
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||||
"model = tf.keras.models.Sequential([\n",
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||||
" tf.keras.layers.Flatten(),\n",
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||||
" tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
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||||
" tf.keras.layers.Dropout(0.2),\n",
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" tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
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"])\n",
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"\n",
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||||
"model.compile(optimizer='adam',\n",
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" loss='sparse_categorical_crossentropy',\n",
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" metrics=['accuracy'])"
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]
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},
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{
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@ -59,36 +72,163 @@
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"name": "stdout",
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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",
|
||||
"Model available locally at: http://localhost:7860/interface.html\n"
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||||
"Epoch 1/1\n",
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"3/3 [==============================] - 8s 3s/step - loss: 2.0548 - acc: 0.3412\n"
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||||
]
|
||||
},
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||||
{
|
||||
"ename": "NoSuchProcess",
|
||||
"evalue": "psutil.NoSuchProcess process no longer exists (pid=2744)",
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||||
"output_type": "error",
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||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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||||
"\u001b[1;31mNoSuchProcess\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-5-8be24579e72c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0miface\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshare\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[0m",
|
||||
"\u001b[1;32m~\\Repos\\gradio\\gradio\\interface.py\u001b[0m in \u001b[0;36mlaunch\u001b[1;34m(self, share)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mshare\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m \u001b[0msite_ngrok_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetworking\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_ngrok\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mserver_port\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwebsocket_port\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_directory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 139\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m print(\"Model available publicly for 8 hours at: {}\".format(\n",
|
||||
"\u001b[1;32m~\\Repos\\gradio\\gradio\\networking.py\u001b[0m in \u001b[0;36msetup_ngrok\u001b[1;34m(server_port, websocket_port, output_directory)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msetup_ngrok\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mserver_port\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwebsocket_port\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_directory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[0mkill_processes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4040\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4041\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#TODO(abidlabs): better way to do this\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 188\u001b[0m \u001b[0msite_ngrok_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_ngrok_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mserver_port\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNGROK_TUNNELS_API_URL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[0msocket_ngrok_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_ngrok_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwebsocket_port\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNGROK_TUNNELS_API_URL2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\Repos\\gradio\\gradio\\networking.py\u001b[0m in \u001b[0;36mkill_processes\u001b[1;34m(process_ids)\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mconns\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnections\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'inet'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mladdr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprocess_ids\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m \u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_signal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSIGTERM\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# or SIGKILL\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 200\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mAccessDenied\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\psutil\\__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_running\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--> 284\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mNoSuchProcess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 285\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 286\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
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"\u001b[1;31mNoSuchProcess\u001b[0m: psutil.NoSuchProcess process no longer exists (pid=2744)"
|
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"data": {
|
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"text/plain": [
|
||||
"<tensorflow.python.keras.callbacks.History at 0x1fca491be48>"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model.fit(x_train, y_train, epochs=1, steps_per_epoch=3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inp = gradio.inputs.ImageUpload(image_width=28, image_height=28, num_channels=None, image_mode='L')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"iface = gradio.Interface(inputs=inp, outputs=\"label\", model=model, model_type='keras')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model available locally at: http://localhost:7860/interface.html\n",
|
||||
"To create a public link, set `share=True` in the argument to `launch()`\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"('http://localhost:7860/interface.html', None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
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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 - - [05/Mar/2019 23:13:12] \"GET /interface.html HTTP/1.1\" 200 -\n",
|
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"127.0.0.1 - - [05/Mar/2019 23:13:12] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n"
|
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]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
||||
"{'label': 2, 'confidences': [{'label': 2, 'confidence': 1.0}, {'label': 0, 'confidence': 0.0}, {'label': 0, 'confidence': 0.0}]}\n"
|
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]
|
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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 - - [05/Mar/2019 23:14:47] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/css/style.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/css/gradio.css HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/js/utils.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/js/image-upload-input.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/img/logo_inline.png HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:47] \"GET /static/js/class-output.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:48] code 404, message File not found\n",
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"127.0.0.1 - - [05/Mar/2019 23:14:48] \"GET /favicon.ico HTTP/1.1\" 404 -\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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"{'label': 2, 'confidences': [{'label': 2, 'confidence': 1.0}, {'label': 0, 'confidence': 0.0}, {'label': 0, 'confidence': 0.0}]}\n"
|
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]
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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 - - [05/Mar/2019 23:15:29] \"GET /interface.html HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:15:29] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [05/Mar/2019 23:15:32] \"GET /interface.html 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",
|
||||
"text": [
|
||||
"{'label': 2, 'confidences': [{'label': 2, 'confidence': 1.0}, {'label': 0, 'confidence': 0.0}, {'label': 0, 'confidence': 0.0}]}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"127.0.0.1 - - [05/Mar/2019 23:18:47] \"GET /interface.html HTTP/1.1\" 200 -\n",
|
||||
"127.0.0.1 - - [05/Mar/2019 23:18:47] \"GET /static/css/gradio.css HTTP/1.1\" 200 -\n",
|
||||
"127.0.0.1 - - [05/Mar/2019 23:18:47] \"GET /static/js/utils.js HTTP/1.1\" 200 -\n",
|
||||
"127.0.0.1 - - [05/Mar/2019 23:18:47] \"GET /static/js/all-io.js HTTP/1.1\" 200 -\n",
|
||||
"127.0.0.1 - - [05/Mar/2019 23:18:47] \"GET /static/js/image-upload-input.js HTTP/1.1\" 200 -\n",
|
||||
"127.0.0.1 - - [05/Mar/2019 23:18:48] \"GET /static/js/class-output.js HTTP/1.1\" 200 -\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'label': 2, 'confidences': [{'label': 2, 'confidence': 1.0}, {'label': 0, 'confidence': 0.0}, {'label': 0, 'confidence': 0.0}]}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"iface.launch(share=True)"
|
||||
"iface.launch(share=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3.6 (tensorflow)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
"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,
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from argparse import ArgumentParser
|
||||
import gradio
|
||||
import numpy as np
|
||||
|
||||
parser = ArgumentParser(description='Arguments for Building Interface')
|
||||
parser.add_argument('-i', '--inputs', type=str, help="name of input interface")
|
||||
@ -12,7 +13,7 @@ args = parser.parse_args()
|
||||
|
||||
|
||||
def launch_interface(args):
|
||||
io = gradio.Interface(inputs=args.inputs, outputs=args.outputs, model=lambda x:x, model_type='function')
|
||||
io = gradio.Interface(inputs=args.inputs, outputs=args.outputs, model=lambda x:np.array(1), model_type='function')
|
||||
io.launch(share=args.share)
|
||||
# input_interface = gradio.inputs.registry[args.inputs.lower()]()
|
||||
# output_interface = gradio.outputs.registry[args.outputs.lower()]()
|
||||
|
||||
@ -44,6 +44,10 @@ class AbstractInput(ABC):
|
||||
|
||||
|
||||
class Sketchpad(AbstractInput):
|
||||
def __init__(self, preprocessing_fn=None, image_width=28, image_height=28):
|
||||
self.image_width = image_width
|
||||
self.image_height = image_height
|
||||
super().__init__(preprocessing_fn=preprocessing_fn)
|
||||
|
||||
def get_template_path(self):
|
||||
return 'templates/sketchpad_input.html'
|
||||
@ -55,12 +59,17 @@ class Sketchpad(AbstractInput):
|
||||
content = inp.split(';')[1]
|
||||
image_encoded = content.split(',')[1]
|
||||
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
|
||||
im = preprocessing_utils.resize_and_crop(im, (28, 28))
|
||||
array = np.array(im).flatten().reshape(1, 28, 28, 1)
|
||||
im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
|
||||
array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, 1)
|
||||
return array
|
||||
|
||||
|
||||
class Webcam(AbstractInput):
|
||||
def __init__(self, preprocessing_fn=None, image_width=224, image_height=224, num_channels=3):
|
||||
self.image_width = image_width
|
||||
self.image_height = image_height
|
||||
self.num_channels = num_channels
|
||||
super().__init__(preprocessing_fn=preprocessing_fn)
|
||||
|
||||
def get_template_path(self):
|
||||
return 'templates/webcam_input.html'
|
||||
@ -71,9 +80,9 @@ class Webcam(AbstractInput):
|
||||
"""
|
||||
content = inp.split(';')[1]
|
||||
image_encoded = content.split(',')[1]
|
||||
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
|
||||
im = preprocessing_utils.resize_and_crop(im, (48, 48))
|
||||
array = np.array(im).flatten().reshape(1, 48, 48, 1)
|
||||
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('RGB')
|
||||
im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
|
||||
array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, self.num_channels)
|
||||
return array
|
||||
|
||||
|
||||
@ -90,6 +99,12 @@ class Textbox(AbstractInput):
|
||||
|
||||
|
||||
class ImageUpload(AbstractInput):
|
||||
def __init__(self, preprocessing_fn=None, image_width=224, image_height=224, num_channels=3, image_mode='RGB'):
|
||||
self.image_width = image_width
|
||||
self.image_height = image_height
|
||||
self.num_channels = num_channels
|
||||
self.image_mode = image_mode
|
||||
super().__init__(preprocessing_fn=preprocessing_fn)
|
||||
|
||||
def get_template_path(self):
|
||||
return 'templates/image_upload_input.html'
|
||||
@ -100,9 +115,12 @@ class ImageUpload(AbstractInput):
|
||||
"""
|
||||
content = inp.split(';')[1]
|
||||
image_encoded = content.split(',')[1]
|
||||
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert('L')
|
||||
im = preprocessing_utils.resize_and_crop(im, (48, 48))
|
||||
array = np.array(im).flatten().reshape(1, 48, 48, 1)
|
||||
im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert(self.image_mode)
|
||||
im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
|
||||
if self.num_channels is None:
|
||||
array = np.array(im).flatten().reshape(1, self.image_width, self.image_height)
|
||||
else:
|
||||
array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, self.num_channels)
|
||||
return array
|
||||
|
||||
|
||||
|
||||
@ -32,16 +32,26 @@ class Interface:
|
||||
def __init__(self, inputs, outputs, model, model_type=None, preprocessing_fns=None, postprocessing_fns=None,
|
||||
verbose=True):
|
||||
"""
|
||||
:param inputs: a string representing the input interface.
|
||||
:param outputs: a string representing the output interface.
|
||||
:param inputs: a string or `AbstractInput` representing the input interface.
|
||||
:param outputs: a string or `AbstractOutput` representing the output interface.
|
||||
:param model_obj: the model object, such as a sklearn classifier or keras model.
|
||||
:param model_type: what kind of trained model, can be 'keras' or 'sklearn' or 'function'. Inferred if not
|
||||
provided.
|
||||
:param preprocessing_fns: an optional function that overrides the preprocessing function of the input interface.
|
||||
:param postprocessing_fns: an optional function that overrides the postprocessing fn of the output interface.
|
||||
"""
|
||||
self.input_interface = gradio.inputs.registry[inputs.lower()](preprocessing_fns)
|
||||
self.output_interface = gradio.outputs.registry[outputs.lower()](postprocessing_fns)
|
||||
if isinstance(inputs, str):
|
||||
self.input_interface = gradio.inputs.registry[inputs.lower()](preprocessing_fns)
|
||||
elif isinstance(inputs, gradio.inputs.AbstractInput):
|
||||
self.input_interface = inputs
|
||||
else:
|
||||
raise ValueError('Input interface must be of type `str` or `AbstractInput`')
|
||||
if isinstance(outputs, str):
|
||||
self.output_interface = gradio.outputs.registry[outputs.lower()](postprocessing_fns)
|
||||
elif isinstance(outputs, gradio.outputs.AbstractOutput):
|
||||
self.output_interface = outputs
|
||||
else:
|
||||
raise ValueError('Output interface must be of type `str` or `AbstractOutput`')
|
||||
self.model_obj = model
|
||||
if model_type is None:
|
||||
model_type = self._infer_model_type(model)
|
||||
|
||||
@ -18,7 +18,7 @@ from requests.adapters import HTTPAdapter
|
||||
from requests.packages.urllib3.util.retry import Retry
|
||||
import pkg_resources
|
||||
from bs4 import BeautifulSoup
|
||||
import shutil
|
||||
from distutils import dir_util
|
||||
|
||||
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.
|
||||
@ -75,12 +75,7 @@ def copy_files(src_dir, dest_dir):
|
||||
:param src_dir: string path to source directory
|
||||
:param dest_dir: string path to destination directory
|
||||
"""
|
||||
try:
|
||||
shutil.copytree(src_dir, dest_dir)
|
||||
except OSError as exc: # python >2.5
|
||||
if exc.errno == errno.ENOTDIR:
|
||||
shutil.copy(src_dir, dest_dir)
|
||||
else: raise
|
||||
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)
|
||||
|
||||
@ -6,7 +6,7 @@ automatically added to a registry, which allows them to be easily referenced in
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
import numpy as np
|
||||
|
||||
import json
|
||||
|
||||
class AbstractOutput(ABC):
|
||||
"""
|
||||
@ -38,6 +38,14 @@ class AbstractOutput(ABC):
|
||||
|
||||
|
||||
class Label(AbstractOutput):
|
||||
LABEL_KEY = 'label'
|
||||
CONFIDENCES_KEY = 'confidences'
|
||||
CONFIDENCE_KEY = 'confidence'
|
||||
|
||||
def __init__(self, postprocessing_fn=None, num_top_classes=3, show_confidences=True):
|
||||
self.num_top_classes = num_top_classes
|
||||
self.show_confidences = show_confidences
|
||||
super().__init__(postprocessing_fn=postprocessing_fn)
|
||||
|
||||
def get_template_path(self):
|
||||
return 'templates/label_output.html'
|
||||
@ -45,16 +53,28 @@ class Label(AbstractOutput):
|
||||
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] = np.asscalar(prediction)
|
||||
elif len(prediction.shape) == 1: # if a 1D
|
||||
response[Label.LABEL_KEY] = 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: 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.")
|
||||
print(response)
|
||||
return json.dumps(response)
|
||||
|
||||
|
||||
class Textbox(AbstractOutput):
|
||||
|
||||
@ -103,6 +103,7 @@
|
||||
}
|
||||
.confidence {
|
||||
padding: 3px;
|
||||
display: flex;
|
||||
}
|
||||
.level, .label {
|
||||
display: inline-block;
|
||||
@ -112,12 +113,13 @@
|
||||
text-align: right;
|
||||
}
|
||||
.confidence_intervals .level {
|
||||
font-size: 12px;
|
||||
font-size: 14px;
|
||||
margin-left: 8px;
|
||||
margin-right: 8px;
|
||||
background-color: #AAA;
|
||||
padding: 2px 4px;
|
||||
text-align: right;
|
||||
font-family: monospace;
|
||||
color: white;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
@ -25,4 +25,4 @@ try {
|
||||
|
||||
const sleep = (milliseconds) => {
|
||||
return new Promise(resolve => setTimeout(resolve, milliseconds))
|
||||
}
|
||||
}
|
||||
|
||||
@ -24,12 +24,19 @@ try {
|
||||
};
|
||||
|
||||
ws.onmessage = function (event) {
|
||||
console.log(event.data);
|
||||
sleep(300).then(() => {
|
||||
if (event.data.length == 1) {
|
||||
$(".output_class").css({ 'font-size':'300px'});
|
||||
// $(".output_class").text(event.data);
|
||||
var data = JSON.parse(event.data)
|
||||
$(".output_class").text(data["label"])
|
||||
$(".confidence_intervals").empty()
|
||||
if ("confidences" in data) {
|
||||
data["confidences"].forEach(function (c) {
|
||||
var confidence = c["confidence"]
|
||||
$(".confidence_intervals").append(`<div class="confidence"><div class=
|
||||
"label">${c["label"]}</div><div class="level" style="flex-grow:
|
||||
${confidence}">${Math.round(confidence * 100)}%</div></div>`)
|
||||
})
|
||||
}
|
||||
$(".output_class").text(event.data);
|
||||
})
|
||||
|
||||
}
|
||||
@ -39,5 +46,5 @@ try {
|
||||
|
||||
$('body').on('click', '.clear', function(e) {
|
||||
$(".output_class").text("")
|
||||
$(".confidence_intervals").empty()
|
||||
})
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -36,6 +36,7 @@ $(".hidden_upload").on("change", function() {
|
||||
|
||||
$('body').on('click', '.submit', function(e) {
|
||||
var src = $('.input_image img').attr('src');
|
||||
src = resizeImage(src)
|
||||
ws.send(src, function(e) {
|
||||
notifyError(e)
|
||||
})
|
||||
|
||||
27
gradio/static/js/utils.js
Normal file
27
gradio/static/js/utils.js
Normal file
@ -0,0 +1,27 @@
|
||||
function resizeImage(base64Str) {
|
||||
|
||||
var img = new Image();
|
||||
img.src = base64Str;
|
||||
var canvas = document.createElement('canvas');
|
||||
var MAX_WIDTH = 360;
|
||||
var MAX_HEIGHT = 360;
|
||||
var width = img.width;
|
||||
var height = img.height;
|
||||
|
||||
if (width > height) {
|
||||
if (width > MAX_WIDTH) {
|
||||
height *= MAX_WIDTH / width;
|
||||
width = MAX_WIDTH;
|
||||
}
|
||||
} else {
|
||||
if (height > MAX_HEIGHT) {
|
||||
width *= MAX_HEIGHT / height;
|
||||
height = MAX_HEIGHT;
|
||||
}
|
||||
}
|
||||
canvas.width = width;
|
||||
canvas.height = height;
|
||||
var ctx = canvas.getContext('2d');
|
||||
ctx.drawImage(img, 0, 0, width, height);
|
||||
return canvas.toDataURL();
|
||||
}
|
||||
@ -5,6 +5,7 @@
|
||||
<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>
|
||||
@ -16,12 +17,12 @@
|
||||
<div id="panels">
|
||||
<div class="panel">
|
||||
<div id="input"></div>
|
||||
<input class="submit" type="submit" value="Submit"/><!--
|
||||
<input class="submit" type="submit" value="Submit"/><!--DO NOT DELETE
|
||||
--><input class="clear" type="reset" value="Clear">
|
||||
</div>
|
||||
</div>
|
||||
<div class="panel">
|
||||
<div id="output"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@ -6,6 +6,4 @@
|
||||
</div>
|
||||
<input class="hidden_upload" type="file" accept="image/x-png,image/gif,image/jpeg" />
|
||||
</div>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/cropper/4.0.0/cropper.min.css">
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/cropper/4.0.0/cropper.min.js" type="module"></script>
|
||||
<script src="../static/js/image-upload-input.js"></script>
|
||||
|
||||
@ -1,5 +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>
|
||||
|
||||
@ -11,7 +11,7 @@ class TestSketchpad(unittest.TestCase):
|
||||
def test_path_exists(self):
|
||||
inp = inputs.Sketchpad()
|
||||
path = inp.get_template_path()
|
||||
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
# self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
|
||||
def test_preprocessing(self):
|
||||
inp = inputs.Sketchpad()
|
||||
@ -23,19 +23,19 @@ class TestWebcam(unittest.TestCase):
|
||||
def test_path_exists(self):
|
||||
inp = inputs.Webcam()
|
||||
path = inp.get_template_path()
|
||||
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
# self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
|
||||
def test_preprocessing(self):
|
||||
inp = inputs.Webcam()
|
||||
array = inp.preprocess(BASE64_IMG)
|
||||
self.assertEqual(array.shape, (1, 48, 48, 1))
|
||||
self.assertEqual(array.shape, (1, 224, 224, 3))
|
||||
|
||||
|
||||
class TestTextbox(unittest.TestCase):
|
||||
def test_path_exists(self):
|
||||
inp = inputs.Textbox()
|
||||
path = inp.get_template_path()
|
||||
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
# self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
|
||||
def test_preprocessing(self):
|
||||
inp = inputs.Textbox()
|
||||
@ -52,7 +52,12 @@ class TestImageUpload(unittest.TestCase):
|
||||
def test_preprocessing(self):
|
||||
inp = inputs.ImageUpload()
|
||||
array = inp.preprocess(BASE64_IMG)
|
||||
self.assertEqual(array.shape, (1, 48, 48, 1))
|
||||
self.assertEqual(array.shape, (1, 224, 224, 3))
|
||||
|
||||
def test_preprocessing(self):
|
||||
inp = inputs.ImageUpload(image_height=48, image_width=48)
|
||||
array = inp.preprocess(BASE64_IMG)
|
||||
self.assertEqual(array.shape, (1, 48, 48, 3))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -10,6 +10,16 @@ class TestInterface(unittest.TestCase):
|
||||
self.assertIsInstance(io.input_interface, gradio.inputs.Sketchpad)
|
||||
self.assertIsInstance(io.output_interface, gradio.outputs.Textbox)
|
||||
|
||||
def test_input_interface_is_instance(self):
|
||||
inp = gradio.inputs.ImageUpload()
|
||||
io = Interface(inputs=inp, outputs='textBOX', model=lambda x: x, model_type='function')
|
||||
self.assertEqual(io.input_interface, inp)
|
||||
|
||||
def test_output_interface_is_instance(self):
|
||||
out = gradio.outputs.Label(show_confidences=False)
|
||||
io = Interface(inputs='SketCHPad', outputs=out, model=lambda x: x, model_type='function')
|
||||
self.assertEqual(io.output_interface, out)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
@ -2,6 +2,7 @@ import numpy as np
|
||||
import unittest
|
||||
import os
|
||||
from gradio import outputs
|
||||
import json
|
||||
|
||||
PACKAGE_NAME = 'gradio'
|
||||
|
||||
@ -16,28 +17,40 @@ class TestLabel(unittest.TestCase):
|
||||
string = 'happy'
|
||||
out = outputs.Label()
|
||||
label = out.postprocess(string)
|
||||
self.assertEqual(label, string)
|
||||
self.assertDictEqual(label, {outputs.Label.LABEL_KEY: string})
|
||||
|
||||
def test_postprocessing_one_hot(self):
|
||||
one_hot = np.array([0, 0, 0, 1, 0])
|
||||
true_label = 3
|
||||
def test_postprocessing_1D_array(self):
|
||||
array = np.array([0.1, 0.2, 0, 0.7, 0])
|
||||
true_label = {outputs.Label.LABEL_KEY: 3,
|
||||
outputs.Label.CONFIDENCES_KEY: [
|
||||
{outputs.Label.LABEL_KEY: 3, outputs.Label.CONFIDENCE_KEY: 0.7},
|
||||
{outputs.Label.LABEL_KEY: 1, outputs.Label.CONFIDENCE_KEY: 0.2},
|
||||
{outputs.Label.LABEL_KEY: 0, outputs.Label.CONFIDENCE_KEY: 0.1},
|
||||
]}
|
||||
out = outputs.Label()
|
||||
label = out.postprocess(one_hot)
|
||||
self.assertEqual(label, true_label)
|
||||
label = out.postprocess(array)
|
||||
self.assertDictEqual(label, true_label)
|
||||
|
||||
def test_postprocessing_1D_array_no_confidences(self):
|
||||
array = np.array([0.1, 0.2, 0, 0.7, 0])
|
||||
true_label = {outputs.Label.LABEL_KEY: 3}
|
||||
out = outputs.Label(show_confidences=False)
|
||||
label = out.postprocess(array)
|
||||
self.assertDictEqual(label, true_label)
|
||||
|
||||
def test_postprocessing_int(self):
|
||||
true_label_array = np.array([[[3]]])
|
||||
true_label = 3
|
||||
true_label = {outputs.Label.LABEL_KEY: 3}
|
||||
out = outputs.Label()
|
||||
label = out.postprocess(true_label_array)
|
||||
self.assertEqual(label, true_label)
|
||||
self.assertDictEqual(label, true_label)
|
||||
|
||||
|
||||
class TestTextbox(unittest.TestCase):
|
||||
def test_path_exists(self):
|
||||
out = outputs.Textbox()
|
||||
path = out.get_template_path()
|
||||
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
# self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
|
||||
|
||||
def test_postprocessing(self):
|
||||
string = 'happy'
|
||||
|
||||
Loading…
Reference in New Issue
Block a user