diff --git a/Chat/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/Chat/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..990cdd3
--- /dev/null
+++ b/Chat/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,1056 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Imports\n",
+    "import nltk\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "import json\n",
+    "import pickle\n",
+    "from collections import Counter\n",
+    "#Loading Data\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from sklearn import preprocessing\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.layers import Dense,Dropout\n",
+    "from tensorflow.keras import Model,Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"intents.json\") as file:\n",
+    "    data = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Initializing empty lists\n",
+    "words = []\n",
+    "labels = []\n",
+    "docs_x = []\n",
+    "docs_y = []\n",
+    "\n",
+    "#Looping through our data\n",
+    "for intent in data['intents']:\n",
+    "    for pattern in intent['patterns']:\n",
+    "        pattern = pattern.lower()\n",
+    "        docs_x.append(pattern)\n",
+    "        docs_y.append(intent['tag'])\n",
+    "\n",
+    "    if intent['tag'] not in labels:\n",
+    "        labels.append(intent['tag'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['hi',\n",
+       " 'hey',\n",
+       " 'how are you',\n",
+       " 'is anyone there ?',\n",
+       " 'hello',\n",
+       " 'good day',\n",
+       " 'whats up',\n",
+       " 'book a table',\n",
+       " 'can i book a table ?',\n",
+       " 'i want to book a table',\n",
+       " 'book seat',\n",
+       " 'i want to book a seat',\n",
+       " 'can i book a seat ?',\n",
+       " 'how many seats are available ?',\n",
+       " 'available seats',\n",
+       " 'how many tables are available ?',\n",
+       " 'available tables',\n",
+       " 'cya',\n",
+       " 'see you later',\n",
+       " 'goodbye',\n",
+       " 'i am leaving',\n",
+       " 'have a good day',\n",
+       " 'cya later',\n",
+       " 'i gotta go now',\n",
+       " 'i gotta rush now',\n",
+       " 'how old',\n",
+       " 'how old is dexter',\n",
+       " 'what is your age',\n",
+       " 'how old are you',\n",
+       " 'age ?',\n",
+       " 'what is your name',\n",
+       " 'what should i call you',\n",
+       " 'whats your name ?',\n",
+       " 'when are you guys open',\n",
+       " 'what are your hours',\n",
+       " 'hours of operation',\n",
+       " 'hours',\n",
+       " 'what is the timing',\n",
+       " 'id like to order something',\n",
+       " 'whats on the menu',\n",
+       " 'what do you reccommend ?',\n",
+       " 'could i get something to eat',\n",
+       " 'im hungry',\n",
+       " 'contact information',\n",
+       " 'contact us',\n",
+       " 'how can i contact you',\n",
+       " 'can i get the contact details',\n",
+       " 'i wanna give some feedback',\n",
+       " 'how can i give some feedback ?',\n",
+       " 'what is the location ?',\n",
+       " 'whats the location',\n",
+       " 'where are you locatated ?',\n",
+       " 'where is the restaurant located ?',\n",
+       " 'address',\n",
+       " 'whats the address ?']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_counter = Counter()\n",
+    "\n",
+    "for sentence in docs_x:\n",
+    "    my_counter.update(sentence.split())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_dict = dict(my_counter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'hi': 1,\n",
+       " 'hey': 1,\n",
+       " 'how': 8,\n",
+       " 'are': 7,\n",
+       " 'you': 8,\n",
+       " 'is': 7,\n",
+       " 'anyone': 1,\n",
+       " 'there': 1,\n",
+       " '?': 13,\n",
+       " 'hello': 1,\n",
+       " 'good': 2,\n",
+       " 'day': 2,\n",
+       " 'whats': 5,\n",
+       " 'up': 1,\n",
+       " 'book': 6,\n",
+       " 'a': 6,\n",
+       " 'table': 3,\n",
+       " 'can': 5,\n",
+       " 'i': 13,\n",
+       " 'want': 2,\n",
+       " 'to': 4,\n",
+       " 'seat': 3,\n",
+       " 'many': 2,\n",
+       " 'seats': 2,\n",
+       " 'available': 4,\n",
+       " 'tables': 2,\n",
+       " 'cya': 2,\n",
+       " 'see': 1,\n",
+       " 'later': 2,\n",
+       " 'goodbye': 1,\n",
+       " 'am': 1,\n",
+       " 'leaving': 1,\n",
+       " 'have': 1,\n",
+       " 'gotta': 2,\n",
+       " 'go': 1,\n",
+       " 'now': 2,\n",
+       " 'rush': 1,\n",
+       " 'old': 3,\n",
+       " 'dexter': 1,\n",
+       " 'what': 7,\n",
+       " 'your': 4,\n",
+       " 'age': 2,\n",
+       " 'name': 2,\n",
+       " 'should': 1,\n",
+       " 'call': 1,\n",
+       " 'when': 1,\n",
+       " 'guys': 1,\n",
+       " 'open': 1,\n",
+       " 'hours': 3,\n",
+       " 'of': 1,\n",
+       " 'operation': 1,\n",
+       " 'the': 7,\n",
+       " 'timing': 1,\n",
+       " 'id': 1,\n",
+       " 'like': 1,\n",
+       " 'order': 1,\n",
+       " 'something': 2,\n",
+       " 'on': 1,\n",
+       " 'menu': 1,\n",
+       " 'do': 1,\n",
+       " 'reccommend': 1,\n",
+       " 'could': 1,\n",
+       " 'get': 2,\n",
+       " 'eat': 1,\n",
+       " 'im': 1,\n",
+       " 'hungry': 1,\n",
+       " 'contact': 4,\n",
+       " 'information': 1,\n",
+       " 'us': 1,\n",
+       " 'details': 1,\n",
+       " 'wanna': 1,\n",
+       " 'give': 2,\n",
+       " 'some': 2,\n",
+       " 'feedback': 2,\n",
+       " 'location': 2,\n",
+       " 'where': 2,\n",
+       " 'locatated': 1,\n",
+       " 'restaurant': 1,\n",
+       " 'located': 1,\n",
+       " 'address': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_count_dict = dict(sorted(count_dict.items(), key=lambda kv: kv[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorizer = CountVectorizer(vocabulary=list(sorted_count_dict.keys()), lowercase=False, binary=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CountVectorizer(binary=True, lowercase=False,\n",
+       "                vocabulary=['hi', 'hey', 'anyone', 'there', 'hello', 'up',\n",
+       "                            'see', 'goodbye', 'am', 'leaving', 'have', 'go',\n",
+       "                            'rush', 'dexter', 'should', 'call', 'when', 'guys',\n",
+       "                            'open', 'of', 'operation', 'timing', 'id', 'like',\n",
+       "                            'order', 'on', 'menu', 'do', 'reccommend', 'could', ...])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vectorizer.fit(docs_x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"vectorizer\", 'wb') as fout:\n",
+    "     pickle.dump(vectorizer,fout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('vectorizer', 'rb') as f:\n",
+    "    vectorizer = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ohe = vectorizer.transform(docs_x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<55x80 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 168 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le = preprocessing.LabelEncoder()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_y = le.fit_transform(docs_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 5, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 1, 1, 1, 1, 1, 9, 9, 9, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 4,\n",
+       "       4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['address', 'age', 'available_tables', 'book_table', 'contact',\n",
+       "       'goodbye', 'greeting', 'hours', 'menu', 'name'], dtype='<U16')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_input = Input(shape=(80,))\n",
+    "dense_layer_1 = Dense(32,activation='relu',kernel_initializer='glorot_normal')(doc_input)\n",
+    "dense_layer_2 = Dense(64,activation='relu',kernel_initializer='glorot_normal')(dense_layer_1)\n",
+    "drop_out = Dropout(0.6)(dense_layer_2)\n",
+    "output=Dense(len(labels), activation='softmax')(drop_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_y = to_categorical(docs_y,num_classes=len(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model(doc_input,output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"functional_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         [(None, 80)]              0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 32)                2592      \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 64)                2112      \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 10)                650       \n",
+      "=================================================================\n",
+      "Total params: 5,354\n",
+      "Trainable params: 5,354\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.3488 - accuracy: 0.1273\n",
+      "Epoch 2/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2752 - accuracy: 0.1091\n",
+      "Epoch 3/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.3118 - accuracy: 0.1273\n",
+      "Epoch 4/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.3107 - accuracy: 0.1091\n",
+      "Epoch 5/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 2.3213 - accuracy: 0.0545\n",
+      "Epoch 6/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 2.2539 - accuracy: 0.0909\n",
+      "Epoch 7/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.2811 - accuracy: 0.1273\n",
+      "Epoch 8/200\n",
+      "1/1 [==============================] - 0s 995us/step - loss: 2.2750 - accuracy: 0.0727\n",
+      "Epoch 9/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 2.2621 - accuracy: 0.2182\n",
+      "Epoch 10/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 2.2687 - accuracy: 0.1273\n",
+      "Epoch 11/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.2625 - accuracy: 0.1636\n",
+      "Epoch 12/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.2697 - accuracy: 0.0909\n",
+      "Epoch 13/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 2.2599 - accuracy: 0.2000\n",
+      "Epoch 14/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.2246 - accuracy: 0.1818\n",
+      "Epoch 15/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.2283 - accuracy: 0.2182\n",
+      "Epoch 16/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.2315 - accuracy: 0.2000\n",
+      "Epoch 17/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.2205 - accuracy: 0.2182\n",
+      "Epoch 18/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.2112 - accuracy: 0.2182\n",
+      "Epoch 19/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 2.2277 - accuracy: 0.2364\n",
+      "Epoch 20/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 2.1807 - accuracy: 0.2364\n",
+      "Epoch 21/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2124 - accuracy: 0.2000\n",
+      "Epoch 22/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.1807 - accuracy: 0.3091\n",
+      "Epoch 23/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.1868 - accuracy: 0.2182\n",
+      "Epoch 24/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.1669 - accuracy: 0.2909\n",
+      "Epoch 25/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.1716 - accuracy: 0.3091\n",
+      "Epoch 26/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.1660 - accuracy: 0.2182\n",
+      "Epoch 27/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.1468 - accuracy: 0.3455\n",
+      "Epoch 28/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.1192 - accuracy: 0.2727\n",
+      "Epoch 29/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.1367 - accuracy: 0.2727\n",
+      "Epoch 30/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.1202 - accuracy: 0.3455\n",
+      "Epoch 31/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0909 - accuracy: 0.4727\n",
+      "Epoch 32/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.1145 - accuracy: 0.4182\n",
+      "Epoch 33/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.1067 - accuracy: 0.4182\n",
+      "Epoch 34/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.1052 - accuracy: 0.3091\n",
+      "Epoch 35/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0609 - accuracy: 0.4545\n",
+      "Epoch 36/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0308 - accuracy: 0.5818\n",
+      "Epoch 37/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 2.0645 - accuracy: 0.4909\n",
+      "Epoch 38/200\n",
+      "1/1 [==============================] - ETA: 0s - loss: 2.0730 - accuracy: 0.38 - 0s 3ms/step - loss: 2.0730 - accuracy: 0.3818\n",
+      "Epoch 39/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 2.0744 - accuracy: 0.3818\n",
+      "Epoch 40/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0446 - accuracy: 0.4182\n",
+      "Epoch 41/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.0726 - accuracy: 0.4364\n",
+      "Epoch 42/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.9887 - accuracy: 0.4727\n",
+      "Epoch 43/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.0433 - accuracy: 0.4545\n",
+      "Epoch 44/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0417 - accuracy: 0.4727\n",
+      "Epoch 45/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.0592 - accuracy: 0.3636\n",
+      "Epoch 46/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0019 - accuracy: 0.4545\n",
+      "Epoch 47/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9455 - accuracy: 0.6000\n",
+      "Epoch 48/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.9715 - accuracy: 0.5091\n",
+      "Epoch 49/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 2.0259 - accuracy: 0.4727\n",
+      "Epoch 50/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9374 - accuracy: 0.4545\n",
+      "Epoch 51/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 1.9805 - accuracy: 0.5091\n",
+      "Epoch 52/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.9626 - accuracy: 0.5091\n",
+      "Epoch 53/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.8938 - accuracy: 0.5636\n",
+      "Epoch 54/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.8878 - accuracy: 0.5636\n",
+      "Epoch 55/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.8453 - accuracy: 0.6364\n",
+      "Epoch 56/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.8920 - accuracy: 0.5091\n",
+      "Epoch 57/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.8603 - accuracy: 0.5636\n",
+      "Epoch 58/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.8432 - accuracy: 0.5636\n",
+      "Epoch 59/200\n",
+      "1/1 [==============================] - 0s 995us/step - loss: 1.8628 - accuracy: 0.5636\n",
+      "Epoch 60/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 1.9101 - accuracy: 0.5455\n",
+      "Epoch 61/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.8620 - accuracy: 0.6000\n",
+      "Epoch 62/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.8342 - accuracy: 0.4909\n",
+      "Epoch 63/200\n",
+      "1/1 [==============================] - 0s 994us/step - loss: 1.8425 - accuracy: 0.6000\n",
+      "Epoch 64/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.7947 - accuracy: 0.6000\n",
+      "Epoch 65/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.7213 - accuracy: 0.7818\n",
+      "Epoch 66/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.7606 - accuracy: 0.6364\n",
+      "Epoch 67/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.7814 - accuracy: 0.6000\n",
+      "Epoch 68/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.7750 - accuracy: 0.6364\n",
+      "Epoch 69/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.7368 - accuracy: 0.5818\n",
+      "Epoch 70/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.7072 - accuracy: 0.6545\n",
+      "Epoch 71/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.7798 - accuracy: 0.5818\n",
+      "Epoch 72/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.6649 - accuracy: 0.6364\n",
+      "Epoch 73/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.7060 - accuracy: 0.6909\n",
+      "Epoch 74/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.7074 - accuracy: 0.6182\n",
+      "Epoch 75/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.6318 - accuracy: 0.6909\n",
+      "Epoch 76/200\n",
+      "1/1 [==============================] - 0s 994us/step - loss: 1.6531 - accuracy: 0.7091\n",
+      "Epoch 77/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.6231 - accuracy: 0.6909\n",
+      "Epoch 78/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.6955 - accuracy: 0.5818\n",
+      "Epoch 79/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.6765 - accuracy: 0.6909\n",
+      "Epoch 80/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.6634 - accuracy: 0.6909\n",
+      "Epoch 81/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 1.6266 - accuracy: 0.6364\n",
+      "Epoch 82/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 1.5144 - accuracy: 0.7455\n",
+      "Epoch 83/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 1.4505 - accuracy: 0.8182\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 84/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.5071 - accuracy: 0.6909\n",
+      "Epoch 85/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.5455 - accuracy: 0.6545\n",
+      "Epoch 86/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.4236 - accuracy: 0.7636\n",
+      "Epoch 87/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.4558 - accuracy: 0.7455\n",
+      "Epoch 88/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.4824 - accuracy: 0.7636\n",
+      "Epoch 89/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.4959 - accuracy: 0.6364\n",
+      "Epoch 90/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.4413 - accuracy: 0.6909\n",
+      "Epoch 91/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.4384 - accuracy: 0.7455\n",
+      "Epoch 92/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.4207 - accuracy: 0.7455\n",
+      "Epoch 93/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 1.4137 - accuracy: 0.7273\n",
+      "Epoch 94/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.3860 - accuracy: 0.6727\n",
+      "Epoch 95/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.3977 - accuracy: 0.7636\n",
+      "Epoch 96/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.2275 - accuracy: 0.7455\n",
+      "Epoch 97/200\n",
+      "1/1 [==============================] - 0s 1000us/step - loss: 1.3195 - accuracy: 0.8364\n",
+      "Epoch 98/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.4080 - accuracy: 0.7273\n",
+      "Epoch 99/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.2826 - accuracy: 0.7636\n",
+      "Epoch 100/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.3435 - accuracy: 0.7818\n",
+      "Epoch 101/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.3476 - accuracy: 0.7636\n",
+      "Epoch 102/200\n",
+      "1/1 [==============================] - 0s 7ms/step - loss: 1.2524 - accuracy: 0.7455\n",
+      "Epoch 103/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.1721 - accuracy: 0.8000\n",
+      "Epoch 104/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.1647 - accuracy: 0.8000\n",
+      "Epoch 105/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.1975 - accuracy: 0.7818\n",
+      "Epoch 106/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.2668 - accuracy: 0.6545\n",
+      "Epoch 107/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.1775 - accuracy: 0.8000\n",
+      "Epoch 108/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.2627 - accuracy: 0.7818\n",
+      "Epoch 109/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 1.1405 - accuracy: 0.7091\n",
+      "Epoch 110/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.0879 - accuracy: 0.8000\n",
+      "Epoch 111/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.0532 - accuracy: 0.8364\n",
+      "Epoch 112/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.2143 - accuracy: 0.7091\n",
+      "Epoch 113/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.0874 - accuracy: 0.8182\n",
+      "Epoch 114/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 0.9892 - accuracy: 0.8364\n",
+      "Epoch 115/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.1242 - accuracy: 0.7636\n",
+      "Epoch 116/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.0731 - accuracy: 0.7818\n",
+      "Epoch 117/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 1.0388 - accuracy: 0.8364\n",
+      "Epoch 118/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.9465 - accuracy: 0.9455\n",
+      "Epoch 119/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.0408 - accuracy: 0.7818\n",
+      "Epoch 120/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 1.0438 - accuracy: 0.7818\n",
+      "Epoch 121/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 0.9892 - accuracy: 0.8545\n",
+      "Epoch 122/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8735 - accuracy: 0.8727\n",
+      "Epoch 123/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.9525 - accuracy: 0.8364\n",
+      "Epoch 124/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8590 - accuracy: 0.8000\n",
+      "Epoch 125/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8964 - accuracy: 0.8545\n",
+      "Epoch 126/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.9115 - accuracy: 0.8727\n",
+      "Epoch 127/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 0.9257 - accuracy: 0.8364\n",
+      "Epoch 128/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 0.8895 - accuracy: 0.8182\n",
+      "Epoch 129/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8258 - accuracy: 0.9091\n",
+      "Epoch 130/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.8422 - accuracy: 0.8545\n",
+      "Epoch 131/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8570 - accuracy: 0.8727\n",
+      "Epoch 132/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.9280 - accuracy: 0.8182\n",
+      "Epoch 133/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.8768 - accuracy: 0.9091\n",
+      "Epoch 134/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 0.7916 - accuracy: 0.8545\n",
+      "Epoch 135/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.9022 - accuracy: 0.7636\n",
+      "Epoch 136/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.8050 - accuracy: 0.8545\n",
+      "Epoch 137/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.7312 - accuracy: 0.9091\n",
+      "Epoch 138/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.6224 - accuracy: 0.8727\n",
+      "Epoch 139/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.7029 - accuracy: 0.9455\n",
+      "Epoch 140/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.7591 - accuracy: 0.9091\n",
+      "Epoch 141/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 0.6590 - accuracy: 0.8545\n",
+      "Epoch 142/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.7492 - accuracy: 0.8364\n",
+      "Epoch 143/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.7123 - accuracy: 0.8909\n",
+      "Epoch 144/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.6112 - accuracy: 0.8909\n",
+      "Epoch 145/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.6760 - accuracy: 0.8364\n",
+      "Epoch 146/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.6241 - accuracy: 0.9273\n",
+      "Epoch 147/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.6769 - accuracy: 0.8545\n",
+      "Epoch 148/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.5956 - accuracy: 0.8727\n",
+      "Epoch 149/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.5396 - accuracy: 0.9091\n",
+      "Epoch 150/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.6019 - accuracy: 0.8727\n",
+      "Epoch 151/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.7091 - accuracy: 0.8000\n",
+      "Epoch 152/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.6271 - accuracy: 0.9455\n",
+      "Epoch 153/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.5988 - accuracy: 0.8909\n",
+      "Epoch 154/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 0.5468 - accuracy: 0.9273\n",
+      "Epoch 155/200\n",
+      "1/1 [==============================] - 0s 9ms/step - loss: 0.5337 - accuracy: 0.9273\n",
+      "Epoch 156/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.5348 - accuracy: 0.9455\n",
+      "Epoch 157/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 0.5658 - accuracy: 0.8545\n",
+      "Epoch 158/200\n",
+      "1/1 [==============================] - 0s 9ms/step - loss: 0.5239 - accuracy: 0.9273\n",
+      "Epoch 159/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.5505 - accuracy: 0.8727\n",
+      "Epoch 160/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.5561 - accuracy: 0.9273\n",
+      "Epoch 161/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 0.5194 - accuracy: 0.9455\n",
+      "Epoch 162/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.5017 - accuracy: 0.9636\n",
+      "Epoch 163/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.5041 - accuracy: 0.9091\n",
+      "Epoch 164/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.5311 - accuracy: 0.9273\n",
+      "Epoch 165/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4635 - accuracy: 0.9455\n",
+      "Epoch 166/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.4812 - accuracy: 0.9091\n",
+      "Epoch 167/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.5606 - accuracy: 0.9091\n",
+      "Epoch 168/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.5370 - accuracy: 0.9455\n",
+      "Epoch 169/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 0.4952 - accuracy: 0.9455\n",
+      "Epoch 170/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.4518 - accuracy: 0.9455\n",
+      "Epoch 171/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.5011 - accuracy: 0.8909\n",
+      "Epoch 172/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.4265 - accuracy: 0.9455\n",
+      "Epoch 173/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 0.3844 - accuracy: 0.9273\n",
+      "Epoch 174/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.4409 - accuracy: 0.9636\n",
+      "Epoch 175/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4034 - accuracy: 0.9273\n",
+      "Epoch 176/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3786 - accuracy: 0.9636\n",
+      "Epoch 177/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4194 - accuracy: 0.9091\n",
+      "Epoch 178/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.4346 - accuracy: 0.9455\n",
+      "Epoch 179/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3776 - accuracy: 0.9636\n",
+      "Epoch 180/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.3744 - accuracy: 0.9273\n",
+      "Epoch 181/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.4474 - accuracy: 0.9636\n",
+      "Epoch 182/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3930 - accuracy: 0.9455\n",
+      "Epoch 183/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.5146 - accuracy: 0.8000\n",
+      "Epoch 184/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 0.3287 - accuracy: 0.9818\n",
+      "Epoch 185/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.3525 - accuracy: 0.9273\n",
+      "Epoch 186/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.3340 - accuracy: 0.9273\n",
+      "Epoch 187/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.9455\n",
+      "Epoch 188/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 0.2784 - accuracy: 0.9818\n",
+      "Epoch 189/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.4008 - accuracy: 0.9455\n",
+      "Epoch 190/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.2825 - accuracy: 0.9818\n",
+      "Epoch 191/200\n",
+      "1/1 [==============================] - 0s 6ms/step - loss: 0.3314 - accuracy: 0.9273\n",
+      "Epoch 192/200\n",
+      "1/1 [==============================] - 0s 5ms/step - loss: 0.3743 - accuracy: 0.9818\n",
+      "Epoch 193/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 0.2750 - accuracy: 0.9455\n",
+      "Epoch 194/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.3535 - accuracy: 0.9455\n",
+      "Epoch 195/200\n",
+      "1/1 [==============================] - 0s 7ms/step - loss: 0.3691 - accuracy: 0.9273\n",
+      "Epoch 196/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3098 - accuracy: 0.9273\n",
+      "Epoch 197/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3344 - accuracy: 0.9455\n",
+      "Epoch 198/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.3553 - accuracy: 0.9273\n",
+      "Epoch 199/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3072 - accuracy: 1.0000\n",
+      "Epoch 200/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.2800 - accuracy: 0.9636\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x15cb477fb50>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(ohe,doc_y,epochs=200,batch_size=400,verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6], dtype=int64)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(model.predict(ohe[0]),-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = [\"Can you recommend something ?\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Iterable over raw text documents expected, string object received.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-26-5a423243fc6c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvectorizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\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[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\feature_extraction\\text.py\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(self, raw_documents)\u001b[0m\n\u001b[0;32m   1241\u001b[0m         \"\"\"\n\u001b[0;32m   1242\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mraw_documents\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\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-> 1243\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m   1244\u001b[0m                 \u001b[1;34m\"Iterable over raw text documents expected, \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1245\u001b[0m                 \"string object received.\")\n",
+      "\u001b[1;31mValueError\u001b[0m: Iterable over raw text documents expected, string object received."
+     ]
+    }
+   ],
+   "source": [
+    "np.argmax(model.predict(vectorizer.transform(query)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['address', 'age', 'available_tables', 'book_table', 'contact',\n",
+       "       'goodbye', 'greeting', 'hours', 'menu', 'name'], dtype='<U16')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save(\"final_model.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_input(query):\n",
+    "    inp = vectorizer.transform([query])\n",
+    "    return inp"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chat/Untitled.ipynb b/Chat/Untitled.ipynb
new file mode 100644
index 0000000..349617e
--- /dev/null
+++ b/Chat/Untitled.ipynb
@@ -0,0 +1,1139 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Imports\n",
+    "import nltk\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "import json\n",
+    "import pickle\n",
+    "from collections import Counter\n",
+    "#Loading Data\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from sklearn import preprocessing\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.layers import Dense,Dropout\n",
+    "from tensorflow.keras import Model,Input\n",
+    "import joblib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"intents.json\") as file:\n",
+    "    data = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Initializing empty lists\n",
+    "words = []\n",
+    "labels = []\n",
+    "docs_x = []\n",
+    "docs_y = []\n",
+    "\n",
+    "#Looping through our data\n",
+    "for intent in data['intents']:\n",
+    "    for pattern in intent['patterns']:\n",
+    "        pattern = pattern.lower()\n",
+    "        docs_x.append(pattern)\n",
+    "        docs_y.append(intent['tag'])\n",
+    "\n",
+    "    if intent['tag'] not in labels:\n",
+    "        labels.append(intent['tag'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['hi',\n",
+       " 'hey',\n",
+       " 'how are you',\n",
+       " 'is anyone there ?',\n",
+       " 'hello',\n",
+       " 'good day',\n",
+       " 'whats up',\n",
+       " 'book a table',\n",
+       " 'can i book a table ?',\n",
+       " 'i want to book a table',\n",
+       " 'book seat',\n",
+       " 'i want to book a seat',\n",
+       " 'can i book a seat ?',\n",
+       " 'how many seats are available ?',\n",
+       " 'available seats',\n",
+       " 'how many tables are available ?',\n",
+       " 'available tables',\n",
+       " 'cya',\n",
+       " 'see you later',\n",
+       " 'goodbye',\n",
+       " 'i am leaving',\n",
+       " 'have a good day',\n",
+       " 'cya later',\n",
+       " 'i gotta go now',\n",
+       " 'i gotta rush now',\n",
+       " 'how old',\n",
+       " 'how old is dexter',\n",
+       " 'what is your age',\n",
+       " 'how old are you',\n",
+       " 'age ?',\n",
+       " 'what is your name',\n",
+       " 'what should i call you',\n",
+       " 'whats your name ?',\n",
+       " 'when are you guys open',\n",
+       " 'what are your hours',\n",
+       " 'hours of operation',\n",
+       " 'hours',\n",
+       " 'what is the timing',\n",
+       " 'id like to order something',\n",
+       " 'whats on the menu',\n",
+       " 'what do you reccommend ?',\n",
+       " 'could i get something to eat',\n",
+       " 'im hungry',\n",
+       " 'contact information',\n",
+       " 'contact us',\n",
+       " 'how can i contact you',\n",
+       " 'can i get the contact details',\n",
+       " 'i wanna give some feedback',\n",
+       " 'how can i give some feedback ?',\n",
+       " 'what is the location ?',\n",
+       " 'whats the location',\n",
+       " 'where are you locatated ?',\n",
+       " 'where is the restaurant located ?',\n",
+       " 'address',\n",
+       " 'whats the address ?']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_counter = Counter()\n",
+    "\n",
+    "for sentence in docs_x:\n",
+    "    my_counter.update(sentence.split())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_dict = dict(my_counter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'hi': 1,\n",
+       " 'hey': 1,\n",
+       " 'how': 8,\n",
+       " 'are': 7,\n",
+       " 'you': 8,\n",
+       " 'is': 7,\n",
+       " 'anyone': 1,\n",
+       " 'there': 1,\n",
+       " '?': 13,\n",
+       " 'hello': 1,\n",
+       " 'good': 2,\n",
+       " 'day': 2,\n",
+       " 'whats': 5,\n",
+       " 'up': 1,\n",
+       " 'book': 6,\n",
+       " 'a': 6,\n",
+       " 'table': 3,\n",
+       " 'can': 5,\n",
+       " 'i': 13,\n",
+       " 'want': 2,\n",
+       " 'to': 4,\n",
+       " 'seat': 3,\n",
+       " 'many': 2,\n",
+       " 'seats': 2,\n",
+       " 'available': 4,\n",
+       " 'tables': 2,\n",
+       " 'cya': 2,\n",
+       " 'see': 1,\n",
+       " 'later': 2,\n",
+       " 'goodbye': 1,\n",
+       " 'am': 1,\n",
+       " 'leaving': 1,\n",
+       " 'have': 1,\n",
+       " 'gotta': 2,\n",
+       " 'go': 1,\n",
+       " 'now': 2,\n",
+       " 'rush': 1,\n",
+       " 'old': 3,\n",
+       " 'dexter': 1,\n",
+       " 'what': 7,\n",
+       " 'your': 4,\n",
+       " 'age': 2,\n",
+       " 'name': 2,\n",
+       " 'should': 1,\n",
+       " 'call': 1,\n",
+       " 'when': 1,\n",
+       " 'guys': 1,\n",
+       " 'open': 1,\n",
+       " 'hours': 3,\n",
+       " 'of': 1,\n",
+       " 'operation': 1,\n",
+       " 'the': 7,\n",
+       " 'timing': 1,\n",
+       " 'id': 1,\n",
+       " 'like': 1,\n",
+       " 'order': 1,\n",
+       " 'something': 2,\n",
+       " 'on': 1,\n",
+       " 'menu': 1,\n",
+       " 'do': 1,\n",
+       " 'reccommend': 1,\n",
+       " 'could': 1,\n",
+       " 'get': 2,\n",
+       " 'eat': 1,\n",
+       " 'im': 1,\n",
+       " 'hungry': 1,\n",
+       " 'contact': 4,\n",
+       " 'information': 1,\n",
+       " 'us': 1,\n",
+       " 'details': 1,\n",
+       " 'wanna': 1,\n",
+       " 'give': 2,\n",
+       " 'some': 2,\n",
+       " 'feedback': 2,\n",
+       " 'location': 2,\n",
+       " 'where': 2,\n",
+       " 'locatated': 1,\n",
+       " 'restaurant': 1,\n",
+       " 'located': 1,\n",
+       " 'address': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_count_dict = dict(sorted(count_dict.items(), key=lambda kv: kv[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorizer = CountVectorizer(vocabulary=list(sorted_count_dict.keys()), lowercase=False, binary=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CountVectorizer(binary=True, lowercase=False,\n",
+       "                vocabulary=['hi', 'hey', 'anyone', 'there', 'hello', 'up',\n",
+       "                            'see', 'goodbye', 'am', 'leaving', 'have', 'go',\n",
+       "                            'rush', 'dexter', 'should', 'call', 'when', 'guys',\n",
+       "                            'open', 'of', 'operation', 'timing', 'id', 'like',\n",
+       "                            'order', 'on', 'menu', 'do', 'reccommend', 'could', ...])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vectorizer.fit(docs_x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['vectorizer.pkl']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(vectorizer, \"vectorizer.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorizer = joblib.load(\"vectorizer.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ohe = vectorizer.transform(docs_x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<55x80 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 168 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le = preprocessing.LabelEncoder()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_y = le.fit_transform(docs_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 5, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 1, 1, 1, 1, 1, 9, 9, 9, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 4,\n",
+       "       4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['address', 'age', 'available_tables', 'book_table', 'contact',\n",
+       "       'goodbye', 'greeting', 'hours', 'menu', 'name'], dtype='<U16')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_input = Input(shape=(80,))\n",
+    "dense_layer_1 = Dense(32,activation='relu',kernel_initializer='glorot_normal')(doc_input)\n",
+    "dense_layer_2 = Dense(64,activation='relu',kernel_initializer='glorot_normal')(dense_layer_1)\n",
+    "drop_out = Dropout(0.6)(dense_layer_2)\n",
+    "output=Dense(len(labels), activation='softmax')(drop_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_y = to_categorical(docs_y,num_classes=len(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model(doc_input,output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"functional_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         [(None, 80)]              0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 32)                2592      \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 64)                2112      \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 10)                650       \n",
+      "=================================================================\n",
+      "Total params: 5,354\n",
+      "Trainable params: 5,354\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/200\n",
+      "1/1 [==============================] - 0s 4ms/step - loss: 2.3019 - accuracy: 0.0727\n",
+      "Epoch 2/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.2887 - accuracy: 0.1455\n",
+      "Epoch 3/200\n",
+      "1/1 [==============================] - 0s 979us/step - loss: 2.2704 - accuracy: 0.1273\n",
+      "Epoch 4/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.3156 - accuracy: 0.0545\n",
+      "Epoch 5/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 2.2854 - accuracy: 0.1273\n",
+      "Epoch 6/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.2874 - accuracy: 0.1455\n",
+      "Epoch 7/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2801 - accuracy: 0.1455\n",
+      "Epoch 8/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2310 - accuracy: 0.1636\n",
+      "Epoch 9/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 2.2662 - accuracy: 0.1273\n",
+      "Epoch 10/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2293 - accuracy: 0.1636\n",
+      "Epoch 11/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.2451 - accuracy: 0.1636\n",
+      "Epoch 12/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2674 - accuracy: 0.1455\n",
+      "Epoch 13/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.2530 - accuracy: 0.1273\n",
+      "Epoch 14/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.2393 - accuracy: 0.2545\n",
+      "Epoch 15/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.2248 - accuracy: 0.1273\n",
+      "Epoch 16/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.2128 - accuracy: 0.2727\n",
+      "Epoch 17/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.1732 - accuracy: 0.3455\n",
+      "Epoch 18/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.2036 - accuracy: 0.2000\n",
+      "Epoch 19/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 2.1848 - accuracy: 0.2182\n",
+      "Epoch 20/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.2156 - accuracy: 0.2364\n",
+      "Epoch 21/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.2000 - accuracy: 0.2182\n",
+      "Epoch 22/200\n",
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+      "Epoch 23/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.1601 - accuracy: 0.3091\n",
+      "Epoch 24/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.1798 - accuracy: 0.2000\n",
+      "Epoch 25/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.1335 - accuracy: 0.3273\n",
+      "Epoch 26/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.1389 - accuracy: 0.3273\n",
+      "Epoch 27/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.1396 - accuracy: 0.3273\n",
+      "Epoch 28/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 2.1606 - accuracy: 0.2727\n",
+      "Epoch 29/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.1355 - accuracy: 0.3091\n",
+      "Epoch 30/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.1359 - accuracy: 0.2909\n",
+      "Epoch 31/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.1281 - accuracy: 0.3455\n",
+      "Epoch 32/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 2.1318 - accuracy: 0.2909\n",
+      "Epoch 33/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.1157 - accuracy: 0.2727\n",
+      "Epoch 34/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.1102 - accuracy: 0.3818\n",
+      "Epoch 35/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.1132 - accuracy: 0.2909\n",
+      "Epoch 36/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 2.0662 - accuracy: 0.3818\n",
+      "Epoch 37/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 2.1426 - accuracy: 0.2182\n",
+      "Epoch 38/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.1036 - accuracy: 0.3273\n",
+      "Epoch 39/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0558 - accuracy: 0.4364\n",
+      "Epoch 40/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.0365 - accuracy: 0.4727\n",
+      "Epoch 41/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0634 - accuracy: 0.4545\n",
+      "Epoch 42/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 2.0123 - accuracy: 0.4000\n",
+      "Epoch 43/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 2.0169 - accuracy: 0.4364\n",
+      "Epoch 44/200\n",
+      "1/1 [==============================] - 0s 993us/step - loss: 2.0426 - accuracy: 0.3818\n",
+      "Epoch 45/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.0531 - accuracy: 0.3818\n",
+      "Epoch 46/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.0530 - accuracy: 0.3636\n",
+      "Epoch 47/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 2.0013 - accuracy: 0.3636\n",
+      "Epoch 48/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9623 - accuracy: 0.5455\n",
+      "Epoch 49/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9788 - accuracy: 0.4909\n",
+      "Epoch 50/200\n",
+      "1/1 [==============================] - 0s 995us/step - loss: 2.0069 - accuracy: 0.4545\n",
+      "Epoch 51/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.9860 - accuracy: 0.4364\n",
+      "Epoch 52/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.9158 - accuracy: 0.5818\n",
+      "Epoch 53/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.9949 - accuracy: 0.3818\n",
+      "Epoch 54/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9391 - accuracy: 0.4545\n",
+      "Epoch 55/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.9403 - accuracy: 0.5636\n",
+      "Epoch 56/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.9325 - accuracy: 0.4545\n",
+      "Epoch 57/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9067 - accuracy: 0.5818\n",
+      "Epoch 58/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.9199 - accuracy: 0.4364\n",
+      "Epoch 59/200\n",
+      "1/1 [==============================] - 0s 995us/step - loss: 1.8719 - accuracy: 0.5091\n",
+      "Epoch 60/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.9276 - accuracy: 0.4000\n",
+      "Epoch 61/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.8517 - accuracy: 0.6182\n",
+      "Epoch 62/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.8788 - accuracy: 0.5818\n",
+      "Epoch 63/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.8133 - accuracy: 0.5818\n",
+      "Epoch 64/200\n",
+      "1/1 [==============================] - 0s 993us/step - loss: 1.8850 - accuracy: 0.5091\n",
+      "Epoch 65/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.8548 - accuracy: 0.5818\n",
+      "Epoch 66/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.8554 - accuracy: 0.4727\n",
+      "Epoch 67/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.7720 - accuracy: 0.6545\n",
+      "Epoch 68/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.8195 - accuracy: 0.5636\n",
+      "Epoch 69/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.7512 - accuracy: 0.6182\n",
+      "Epoch 70/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.8388 - accuracy: 0.5273\n",
+      "Epoch 71/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.7983 - accuracy: 0.4909\n",
+      "Epoch 72/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.7788 - accuracy: 0.5273\n",
+      "Epoch 73/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.7619 - accuracy: 0.6545\n",
+      "Epoch 74/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.7922 - accuracy: 0.5091\n",
+      "Epoch 75/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.7033 - accuracy: 0.6364\n",
+      "Epoch 76/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.7455 - accuracy: 0.6182\n",
+      "Epoch 77/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.6862 - accuracy: 0.5455\n",
+      "Epoch 78/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.7578 - accuracy: 0.6000\n",
+      "Epoch 79/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.6003 - accuracy: 0.7455\n",
+      "Epoch 80/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.6792 - accuracy: 0.6182\n",
+      "Epoch 81/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.6783 - accuracy: 0.5636\n",
+      "Epoch 82/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.5997 - accuracy: 0.6727\n",
+      "Epoch 83/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 998us/step - loss: 1.6671 - accuracy: 0.6364\n",
+      "Epoch 84/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.5699 - accuracy: 0.6909\n",
+      "Epoch 85/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.6662 - accuracy: 0.5455\n",
+      "Epoch 86/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.5763 - accuracy: 0.6364\n",
+      "Epoch 87/200\n",
+      "1/1 [==============================] - 0s 995us/step - loss: 1.5936 - accuracy: 0.6364\n",
+      "Epoch 88/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.5503 - accuracy: 0.6727\n",
+      "Epoch 89/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.4797 - accuracy: 0.7091\n",
+      "Epoch 90/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.5525 - accuracy: 0.6545\n",
+      "Epoch 91/200\n",
+      "1/1 [==============================] - 0s 994us/step - loss: 1.4920 - accuracy: 0.7091\n",
+      "Epoch 92/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.4954 - accuracy: 0.6909\n",
+      "Epoch 93/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.4956 - accuracy: 0.7091\n",
+      "Epoch 94/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.5134 - accuracy: 0.6909\n",
+      "Epoch 95/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.4199 - accuracy: 0.7273\n",
+      "Epoch 96/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.4902 - accuracy: 0.7273\n",
+      "Epoch 97/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.4210 - accuracy: 0.7273\n",
+      "Epoch 98/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.3348 - accuracy: 0.8000\n",
+      "Epoch 99/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.4228 - accuracy: 0.6909\n",
+      "Epoch 100/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 1.4059 - accuracy: 0.6545\n",
+      "Epoch 101/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.3381 - accuracy: 0.7273\n",
+      "Epoch 102/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.3602 - accuracy: 0.7636\n",
+      "Epoch 103/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.3567 - accuracy: 0.7091\n",
+      "Epoch 104/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.3506 - accuracy: 0.8182\n",
+      "Epoch 105/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 1.3437 - accuracy: 0.7273\n",
+      "Epoch 106/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 1.3417 - accuracy: 0.7636\n",
+      "Epoch 107/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.1817 - accuracy: 0.7636\n",
+      "Epoch 108/200\n",
+      "1/1 [==============================] - 0s 991us/step - loss: 1.1967 - accuracy: 0.7818\n",
+      "Epoch 109/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 1.2297 - accuracy: 0.8000\n",
+      "Epoch 110/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.2530 - accuracy: 0.7455\n",
+      "Epoch 111/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.2017 - accuracy: 0.6909\n",
+      "Epoch 112/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.2065 - accuracy: 0.6909\n",
+      "Epoch 113/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 1.2341 - accuracy: 0.7455\n",
+      "Epoch 114/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.1569 - accuracy: 0.7091\n",
+      "Epoch 115/200\n",
+      "1/1 [==============================] - 0s 995us/step - loss: 1.1183 - accuracy: 0.8182\n",
+      "Epoch 116/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 1.1461 - accuracy: 0.8182\n",
+      "Epoch 117/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 1.0502 - accuracy: 0.8182\n",
+      "Epoch 118/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 1.1447 - accuracy: 0.8182\n",
+      "Epoch 119/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 1.1978 - accuracy: 0.7091\n",
+      "Epoch 120/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.0452 - accuracy: 0.8545\n",
+      "Epoch 121/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.0840 - accuracy: 0.7818\n",
+      "Epoch 122/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.9643 - accuracy: 0.9091\n",
+      "Epoch 123/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.9928 - accuracy: 0.7818\n",
+      "Epoch 124/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 1.0969 - accuracy: 0.7636\n",
+      "Epoch 125/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.8924 - accuracy: 0.8182\n",
+      "Epoch 126/200\n",
+      "1/1 [==============================] - 0s 1000us/step - loss: 0.9782 - accuracy: 0.8364\n",
+      "Epoch 127/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 0.9900 - accuracy: 0.8182\n",
+      "Epoch 128/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.9465 - accuracy: 0.8182\n",
+      "Epoch 129/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.9397 - accuracy: 0.8364\n",
+      "Epoch 130/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.8328 - accuracy: 0.9091\n",
+      "Epoch 131/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.8802 - accuracy: 0.8727\n",
+      "Epoch 132/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.9530 - accuracy: 0.8000\n",
+      "Epoch 133/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8403 - accuracy: 0.8545\n",
+      "Epoch 134/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 0.7768 - accuracy: 0.8727\n",
+      "Epoch 135/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.8774 - accuracy: 0.8364\n",
+      "Epoch 136/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 0.8362 - accuracy: 0.8364\n",
+      "Epoch 137/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.8122 - accuracy: 0.8727\n",
+      "Epoch 138/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.7388 - accuracy: 0.9273\n",
+      "Epoch 139/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 0.8199 - accuracy: 0.8000\n",
+      "Epoch 140/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.8146 - accuracy: 0.8364\n",
+      "Epoch 141/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.7865 - accuracy: 0.8364\n",
+      "Epoch 142/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.7912 - accuracy: 0.8545\n",
+      "Epoch 143/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.7535 - accuracy: 0.9273\n",
+      "Epoch 144/200\n",
+      "1/1 [==============================] - 0s 991us/step - loss: 0.6644 - accuracy: 0.8909\n",
+      "Epoch 145/200\n",
+      "1/1 [==============================] - 0s 990us/step - loss: 0.7469 - accuracy: 0.8182\n",
+      "Epoch 146/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.8088 - accuracy: 0.8182\n",
+      "Epoch 147/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.6817 - accuracy: 0.9455\n",
+      "Epoch 148/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.6550 - accuracy: 0.9273\n",
+      "Epoch 149/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.6255 - accuracy: 0.9636\n",
+      "Epoch 150/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.6186 - accuracy: 0.9091\n",
+      "Epoch 151/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.6207 - accuracy: 0.9636\n",
+      "Epoch 152/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.5825 - accuracy: 0.9636\n",
+      "Epoch 153/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.5734 - accuracy: 0.9455\n",
+      "Epoch 154/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.6721 - accuracy: 0.9273\n",
+      "Epoch 155/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.6375 - accuracy: 0.8909\n",
+      "Epoch 156/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 0.6002 - accuracy: 0.9455\n",
+      "Epoch 157/200\n",
+      "1/1 [==============================] - 0s 3ms/step - loss: 0.6864 - accuracy: 0.8545\n",
+      "Epoch 158/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.5669 - accuracy: 0.9273\n",
+      "Epoch 159/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 0.5347 - accuracy: 0.9455\n",
+      "Epoch 160/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.5367 - accuracy: 0.9273\n",
+      "Epoch 161/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.6885 - accuracy: 0.8909\n",
+      "Epoch 162/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.5811 - accuracy: 0.9273\n",
+      "Epoch 163/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 0.5606 - accuracy: 0.9455\n",
+      "Epoch 164/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 997us/step - loss: 0.5109 - accuracy: 0.9455\n",
+      "Epoch 165/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4637 - accuracy: 0.9818\n",
+      "Epoch 166/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.5596 - accuracy: 0.9273\n",
+      "Epoch 167/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4708 - accuracy: 0.9818\n",
+      "Epoch 168/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.4128 - accuracy: 0.9636\n",
+      "Epoch 169/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.5370 - accuracy: 0.8909\n",
+      "Epoch 170/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.5358 - accuracy: 0.9091\n",
+      "Epoch 171/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.9273\n",
+      "Epoch 172/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.4555 - accuracy: 0.9273\n",
+      "Epoch 173/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.4522 - accuracy: 0.9273\n",
+      "Epoch 174/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.4582 - accuracy: 0.9091\n",
+      "Epoch 175/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.5007 - accuracy: 0.9636\n",
+      "Epoch 176/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.9636\n",
+      "Epoch 177/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3814 - accuracy: 0.9091\n",
+      "Epoch 178/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.4500 - accuracy: 0.9091\n",
+      "Epoch 179/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.4256 - accuracy: 0.9455\n",
+      "Epoch 180/200\n",
+      "1/1 [==============================] - 0s 996us/step - loss: 0.3752 - accuracy: 0.9818\n",
+      "Epoch 181/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.4599 - accuracy: 0.9273\n",
+      "Epoch 182/200\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 0.4282 - accuracy: 0.9273\n",
+      "Epoch 183/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.3289 - accuracy: 0.9818\n",
+      "Epoch 184/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3550 - accuracy: 0.9818\n",
+      "Epoch 185/200\n",
+      "1/1 [==============================] - 0s 0s/step - loss: 0.4677 - accuracy: 0.9273\n",
+      "Epoch 186/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3669 - accuracy: 0.9818\n",
+      "Epoch 187/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.3925 - accuracy: 0.9818\n",
+      "Epoch 188/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.5201 - accuracy: 0.8364\n",
+      "Epoch 189/200\n",
+      "1/1 [==============================] - 0s 981us/step - loss: 0.4308 - accuracy: 0.9455\n",
+      "Epoch 190/200\n",
+      "1/1 [==============================] - 0s 999us/step - loss: 0.3771 - accuracy: 0.9091\n",
+      "Epoch 191/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.2759 - accuracy: 0.9818\n",
+      "Epoch 192/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3050 - accuracy: 0.9636\n",
+      "Epoch 193/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.3503 - accuracy: 0.9273\n",
+      "Epoch 194/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.3089 - accuracy: 0.9636\n",
+      "Epoch 195/200\n",
+      "1/1 [==============================] - 0s 997us/step - loss: 0.4199 - accuracy: 0.8909\n",
+      "Epoch 196/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3961 - accuracy: 0.9273\n",
+      "Epoch 197/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3610 - accuracy: 0.9636\n",
+      "Epoch 198/200\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 0.3204 - accuracy: 1.0000\n",
+      "Epoch 199/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3021 - accuracy: 0.9818\n",
+      "Epoch 200/200\n",
+      "1/1 [==============================] - 0s 998us/step - loss: 0.3350 - accuracy: 0.9636\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x18b56cbd7c0>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(ohe,doc_y,epochs=200,batch_size=400,verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6], dtype=int64)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(model.predict(ohe[0]),-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = [\"Can you recommend something ?\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(model.predict(vectorizer.transform(query)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['address', 'age', 'available_tables', 'book_table', 'contact',\n",
+       "       'goodbye', 'greeting', 'hours', 'menu', 'name'], dtype='<U16')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "cannot pickle '_thread.RLock' object",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-33-ed370105a966>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mjoblib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"final_model.pkl\"\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~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36mdump\u001b[1;34m(value, filename, compress, protocol, cache_size)\u001b[0m\n\u001b[0;32m    478\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_filename\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    479\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 480\u001b[1;33m             \u001b[0mNumpyPickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\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    481\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    482\u001b[0m         \u001b[0mNumpyPickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36mdump\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    483\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_framing\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--> 485\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    486\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSTOP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend_framing\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\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;31m# Save the reduce() output and finally memoize the object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_reduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mrv\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    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_reduce\u001b[1;34m(self, func, args, state, listitems, dictitems, state_setter, obj)\u001b[0m\n\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[0;32m    714\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstate_setter\u001b[0m \u001b[1;32mis\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[1;32m--> 715\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\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    716\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBUILD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\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[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    971\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    993\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\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    996\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_list\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    927\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 929\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_appends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    930\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_appends\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    951\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMARK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    952\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 953\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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    954\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPENDS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    955\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;31m# Save the reduce() output and finally memoize the object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_reduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mrv\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    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_reduce\u001b[1;34m(self, func, args, state, listitems, dictitems, state_setter, obj)\u001b[0m\n\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[0;32m    714\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstate_setter\u001b[0m \u001b[1;32mis\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[1;32m--> 715\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\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    716\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBUILD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\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[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    971\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    993\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\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    996\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_list\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    927\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 929\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_appends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    930\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_appends\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    954\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPENDS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    955\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 956\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\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    957\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPEND\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    958\u001b[0m             \u001b[1;31m# else tmp is empty, and we're done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;31m# Save the reduce() output and finally memoize the object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_reduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mrv\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    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_reduce\u001b[1;34m(self, func, args, state, listitems, dictitems, state_setter, obj)\u001b[0m\n\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[0;32m    714\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstate_setter\u001b[0m \u001b[1;32mis\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[1;32m--> 715\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\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    716\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBUILD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\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[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    971\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    993\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\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    996\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;31m# Save the reduce() output and finally memoize the object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_reduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mrv\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    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_reduce\u001b[1;34m(self, func, args, state, listitems, dictitems, state_setter, obj)\u001b[0m\n\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[0;32m    714\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstate_setter\u001b[0m \u001b[1;32mis\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[1;32m--> 715\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\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    716\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBUILD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\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[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    971\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    993\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\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    996\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;31m# Save the reduce() output and finally memoize the object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_reduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mrv\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    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_reduce\u001b[1;34m(self, func, args, state, listitems, dictitems, state_setter, obj)\u001b[0m\n\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[0;32m    714\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstate_setter\u001b[0m \u001b[1;32mis\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[1;32m--> 715\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\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    716\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBUILD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\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[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    971\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    993\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\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    996\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;31m# Save the reduce() output and finally memoize the object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_reduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mrv\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    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_reduce\u001b[1;34m(self, func, args, state, listitems, dictitems, state_setter, obj)\u001b[0m\n\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[0;32m    714\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstate_setter\u001b[0m \u001b[1;32mis\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[1;32m--> 715\u001b[1;33m                 \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\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    716\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBUILD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\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[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 558\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    559\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    971\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m    993\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m                     \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\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    996\u001b[0m                 \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    997\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\numpy_pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\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    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\pickle.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[0;32m    574\u001b[0m                 \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"__reduce_ex__\"\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[0;32m    575\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\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[1;32m--> 576\u001b[1;33m                     \u001b[0mrv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\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    577\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    578\u001b[0m                     \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"__reduce__\"\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[1;31mTypeError\u001b[0m: cannot pickle '_thread.RLock' object"
+     ]
+    }
+   ],
+   "source": [
+    "joblib.dump(model,\"final_model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save(\"final_model.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_input(query):\n",
+    "    inp = vectorizer.transform([query])\n",
+    "    return inp"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chat/final_model.h5 b/Chat/final_model.h5
new file mode 100644
index 0000000..a92d015
Binary files /dev/null and b/Chat/final_model.h5 differ
diff --git a/Chat/final_model.pkl b/Chat/final_model.pkl
new file mode 100644
index 0000000..be130f0
--- /dev/null
+++ b/Chat/final_model.pkl
@@ -0,0 +1 @@
+€
\ No newline at end of file
diff --git a/Chat/intents.json b/Chat/intents.json
new file mode 100644
index 0000000..74dfe69
--- /dev/null
+++ b/Chat/intents.json
@@ -0,0 +1,53 @@
+{"intents": [
+        {"tag": "greeting",
+         "patterns": ["Hi", "Hey", "How are you", "Is anyone there ?", "Hello", "Good day", "Whats up"],
+         "responses": ["Hello !", "Good to see you again !", "Hi there, how can I help ?", "Hello ! I'm Dexter . How may I help you ?", "Hey there !"],
+         "context_set": ""
+        },
+        {"tag": "book_table",
+         "patterns": ["Book a table","Can I book a table ?", "I want to book a table", "Book seat", "I want to book a seat", "Can I book a seat ?"],
+         "responses": [""],
+         "context_set": ""
+        },
+        {"tag": "available_tables",
+         "patterns": ["How many seats are available ?", "Available seats", "How many tables are available ?", "Available tables"],
+         "responses": [""],
+         "context_set": ""
+        },
+        {"tag": "goodbye",
+         "patterns": ["cya", "See you later", "Goodbye", "I am Leaving", "Have a Good day", "cya later", "I gotta go now", "I gotta rush now"],
+         "responses": ["Sad to see you go :(", "Talk to you later", "Goodbye !"],
+         "context_set": ""
+        },
+        {"tag": "age",
+         "patterns": ["how old", "how old is Dexter", "what is your age", "how old are you", "age ?"],
+         "responses": ["My master built me just a month ago .", "Just a month old !"],
+         "context_set": ""
+        },
+        {"tag": "name",
+         "patterns": ["what is your name", "what should I call you", "whats your name ?"],
+         "responses": ["You can call me Dexter .", "I'm Dexter !", "I'm Dexter aka The Restaurant Superbot ."],
+         "context_set": ""
+        },
+        {"tag": "hours",
+         "patterns": ["when are you guys open", "what are your hours", "hours of operation", "hours", "what is the timing"],
+         "responses": ["We are open 10am-12am Monday-Friday !"],
+         "context_set": ""
+        },
+        {"tag": "menu",
+         "patterns": ["Id like to order something", "whats on the menu", "what do you reccommend ?", "could i get something to eat", "Im hungry"],
+         "responses": [""],
+         "context_set": ""
+        },
+        {"tag": "contact",
+         "patterns": ["contact information", "contact us", "how can i contact you", "can i get the contact details", "I wanna give some feedback", "how can i give some feedback ?"],
+         "responses": ["You can contact us at contact@restaurantname .com"],
+         "context_set": ""
+        },
+        {"tag": "address",
+         "patterns": ["what is the location ?","whats the location", "where are you locatated ?", "where is the restaurant located ?", "address", "whats the address ?"],
+         "responses": ["You can locate us at Taj Lands End, Bandra Bandstand, Bandra West, Mumbai, Maharashtra 400 050"],
+         "context_set": ""
+        }
+   ]
+}
\ No newline at end of file
diff --git a/Chat/sp.py b/Chat/sp.py
new file mode 100644
index 0000000..916bdff
--- /dev/null
+++ b/Chat/sp.py
@@ -0,0 +1,11 @@
+import azure.cognitiveservices.speech as speechsdk
+
+def from_file():
+    speech_config = speechsdk.SpeechConfig(subscription="<paste-your-subscription-key>", region="<paste-your-region>")
+    audio_input = speechsdk.AudioConfig(filename="your_file_name.wav")
+    speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_input)
+    
+    result = speech_recognizer.recognize_once_async().get()
+    print(result.text)
+
+from_file()
\ No newline at end of file
diff --git a/Chat/vectorizer b/Chat/vectorizer
new file mode 100644
index 0000000..b8e9d1e
Binary files /dev/null and b/Chat/vectorizer differ
diff --git a/Chat/vectorizer.pkl b/Chat/vectorizer.pkl
new file mode 100644
index 0000000..02946e0
Binary files /dev/null and b/Chat/vectorizer.pkl differ
diff --git a/request.txt b/request.txt
new file mode 100644
index 0000000..338f825
--- /dev/null
+++ b/request.txt
@@ -0,0 +1,8 @@
+Hi Mam,
+
+Please help me install pjsip over cloud as I am unable to do it.
+This project is very helpfull to me and I need some guidance for the same.
+
+Please contact me at pratyushjena.2k17@gmail.com or please provide me your email so that I can drop a mail.
+
+Thank you
diff --git a/sp.py b/sp.py
new file mode 100644
index 0000000..958f3a4
--- /dev/null
+++ b/sp.py
@@ -0,0 +1,22 @@
+import azure.cognitiveservices.speech as speechsdk
+
+def from_file():
+    speech_config = speechsdk.SpeechConfig(subscription="34c15a8a2f4342b487ce48da2e8bdd12", region="eastus")
+    audio_input = speechsdk.AudioConfig(filename="output.wav")
+    speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_input)
+    
+    result = speech_recognizer.recognize_once_async().get()
+    print(result.text)
+
+    if result.reason == speechsdk.ResultReason.RecognizedSpeech:
+        print("Recognized: {}".format(result.text))
+    elif result.reason == speechsdk.ResultReason.NoMatch:
+        print("No speech could be recognized: {}".format(result.no_match_details))
+    elif result.reason == speechsdk.ResultReason.Canceled:
+        cancellation_details = result.cancellation_details
+        print("Speech Recognition canceled: {}".format(cancellation_details.reason))
+        if cancellation_details.reason == speechsdk.CancellationReason.Error:
+            print("Error details: {}".format(cancellation_details.error_details))
+
+
+from_file()
\ No newline at end of file
diff --git a/src/chunk_15_816000.wav b/src/chunk_15_816000.wav
new file mode 100644
index 0000000..84ee1de
Binary files /dev/null and b/src/chunk_15_816000.wav differ
diff --git a/src/runclient.py b/src/runclient.py
index ac78d8b..6fa84d4 100644
--- a/src/runclient.py
+++ b/src/runclient.py
@@ -1,7 +1,7 @@
 import sys
 import pjsua as pj
 import threading
-import bothelper as bot
+#import bothelper as bot
 import time
 
 current_call = None
@@ -48,7 +48,7 @@ def on_incoming_call(self, call):
 	listen_and_respond()
 
 def listen_and_respond():
-    recorderid = lib.create_recorder("YOUR_FILE_PATH/input.wav")
+    recorderid = lib.create_recorder("input.wav")
     recorderslot = lib.recorder_get_slot(recorderid)
 
     # Connect sound device to wav record file
@@ -59,18 +59,18 @@ def listen_and_respond():
     time.sleep(8)
 
     lib.recorder_destroy(recorderid)
-    mybot = bot.BotHelper()
-    mybot.generate_response()
+    #mybot = bot.BotHelper()
+    #mybot.generate_response()
 
     # Play wav file back to user
-    playerid = lib.create_player('botresponse.wav',loop=False)
+    playerid = lib.create_player('chunk_15_816000.wav',loop=False)
     playerslot = lib.player_get_slot(playerid)
     # Connect the audio player to the call
     lib.conf_connect(playerslot,call_slot)
 
-	# Wait for the thing to be read for a few seconds then hang up
-	time.sleep(13)
-	current_call.hangup()
+    # Wait for the thing to be read for a few seconds then hang up
+    time.sleep(13)
+    current_call.hangup()
 
 class MyCallCallback(pj.CallCallback):
 
@@ -124,7 +124,7 @@ def resetAll():
     lib.start()
 
 	# Put your sIP client credentials here
-    acc = lib.create_account(pj.AccountConfig("SERVER_IP_ADDRESS", "USERNAME", "PASSWORD"))
+    acc = lib.create_account(pj.AccountConfig("sip.linphone.org", "chiku1064", "Bengaluru@96"))
 
     acc_cb = MyAccountCallback(acc)
     acc.set_callback(acc_cb)
@@ -152,7 +152,7 @@ def resetAll():
 		print "There is no call"
 		continue
 	    current_call.hangup()
-		resetAll()
+	    resetAll()
 
 	elif input == "q":
 	    break