gradio/Test Keras MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
    "  tf.keras.layers.Dropout(0.2),\n",
    "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "60000/60000 [==============================] - 24s 407us/step - loss: 0.2171 - acc: 0.9355\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iface = gradio.Interface(inputs=\"sketchpad\", outputs=\"label\", model=model, model_type='keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No validation samples for this interface... skipping validation.\n",
      "NOTE: Gradio is in beta stage, please report all bugs to: contact.gradio@gmail.com\n",
      "Model is running locally at: http://localhost:7860/\n",
      "To create a public link, set `share=True` in the argument to `launch()`\n"
     ]
    },
    {
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       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"500\"\n",
       "            src=\"http://localhost:7860/\"\n",
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       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
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       "(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x23bf9174b70>,\n",
       " 'http://localhost:7860/',\n",
       " None)"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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\\interface.py\", line 114, in communicate\n",
      "    msg = json.loads(await websocket.recv())\n",
      "  File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\json\\__init__.py\", line 354, in loads\n",
      "    return _default_decoder.decode(s)\n",
      "  File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\json\\decoder.py\", line 339, in decode\n",
      "    obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n",
      "  File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\json\\decoder.py\", line 357, in raw_decode\n",
      "    raise JSONDecodeError(\"Expecting value\", s, err.value) from None\n",
      "json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n"
     ]
    }
   ],
   "source": [
    "iface.launch(inline=True, share=False)"
   ]
  }
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