Ask_Balaram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle as pk\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import re\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import np_utils\n",
    "from keras.models import load_model\n",
    "import tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainIntentModel():\n",
    "    # Load the dataset and prepare it to the train the model\n",
    "\n",
    "    # Importing dataset and splitting into words and labels\n",
    "    dataset = pd.read_csv('datasets/intents.csv', names=[\"Query\", \"Intent\"])\n",
    "\n",
    "    X = dataset[\"Query\"]\n",
    "    y = dataset[\"Intent\"]\n",
    "\n",
    "    unique_intent_list = list(set(y))\n",
    "\n",
    "    print(\"Intent Dataset successfully loaded!\")\n",
    "    \n",
    "    # Clean and prepare the intents corpus\n",
    "    queryCorpus = []\n",
    "    ps = PorterStemmer()\n",
    "\n",
    "    for query in X:\n",
    "        query = re.sub('[^a-zA-Z]', ' ', query)\n",
    "\n",
    "        # Tokenize sentence\n",
    "        query = query.split(' ')\n",
    "\n",
    "        # Lemmatizing\n",
    "        tokenized_query = [ps.stem(word.lower()) for word in query]\n",
    "\n",
    "        # Recreate the sentence from tokens\n",
    "        tokenized_query = ' '.join(tokenized_query)\n",
    "\n",
    "        # Add to corpus\n",
    "        queryCorpus.append(tokenized_query)\n",
    "        \n",
    "    print(queryCorpus)\n",
    "    print(\"Corpus created\")\n",
    "    \n",
    "    countVectorizer= CountVectorizer(max_features=800)\n",
    "    corpus = countVectorizer.fit_transform(queryCorpus).toarray()\n",
    "    print(corpus.shape)\n",
    "    print(\"Bag of words created!\")\n",
    "    \n",
    "    # Save the CountVectorizer\n",
    "    pk.dump(countVectorizer, open(\"saved_state/IntentCountVectorizer.sav\", 'wb'))\n",
    "    print(\"Intent CountVectorizer saved!\")\n",
    "    \n",
    "    # Encode the intent classes\n",
    "    labelencoder_intent = LabelEncoder()\n",
    "    y = labelencoder_intent.fit_transform(y)\n",
    "    y = np_utils.to_categorical(y)\n",
    "    print(\"Encoded the intent classes!\")\n",
    "    print(y)\n",
    "    \n",
    "    # Return a dictionary, mapping labels to their integer values\n",
    "    res = {}\n",
    "    for cl in labelencoder_intent.classes_:\n",
    "        res.update({cl:labelencoder_intent.transform([cl])[0]})\n",
    "\n",
    "    intent_label_map = res\n",
    "    print(intent_label_map)\n",
    "    print(\"Intent Label mapping obtained!\")\n",
    "    \n",
    "    # Initialising the Aritifcial Neural Network\n",
    "    classifier = Sequential()\n",
    "\n",
    "    # Adding the input layer and the first hidden layer\n",
    "    classifier.add(Dense(units = 96, kernel_initializer = 'uniform', activation = 'relu', input_dim = 162))\n",
    "\n",
    "    # Adding the second hidden layer\n",
    "    classifier.add(Dense(units = 96, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "\n",
    "    # Adding the output layer\n",
    "    classifier.add(Dense(units = 27, kernel_initializer = 'uniform', activation = 'softmax'))\n",
    "\n",
    "    # Compiling the ANN\n",
    "    classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "    \n",
    "    # Fitting the ANN to the Training set\n",
    "    classifier.fit(corpus, y, batch_size = 10, epochs = 500)\n",
    "    \n",
    "    return classifier, intent_label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intent Dataset successfully loaded!\n",
      "['queri', 'how to grow rice ', 'i want to grow rice ', 'how much water to add for wheat ', 'best season to grow sugarcan', 'yield of wheat', 'averag yield of rice', 'what is the yield of rice in belgaum ', 'best pesticid for cotton ', 'best soil for cotton', 'how much fertil should i add to my rice crop ', 'when to sow cotton ', 'when to harvest wheat ', 'how to enhanc yield of sugarcan ', 'i want to grow paddi ', 'how to grow maiz ', 'which fertil can i use for sugarcan ', 'how is the weather today ', 'how is the weather tomorrow ', 'what s the weather in mysor thi week ', 'weather thi week ', 'weather forecast for the week ', 'how is the climat today ', 'climat condit for the upcom day ', 'weather report for the day ', 'will it rain today ', 'is it go to be sunni thi week ', 'what will the weather be like thi week ', 'can i expect it to be sunni today ', 'which is the season for grow rice ', 'season for grow wheat ', 'how much cotton seed are need for one acr of land ', 'can i grow rice in red soil ', 'best soil for grow maiz ', 'irrig for sugarcan ', 'which are the avail seed varieti for rice ', 'rice varieti ', 'differ type of wheat seed ', 'type of seed of cotton ', 'which cotton seed grow well in black soil ', 'how long doe it take to grow rice ', 'how mani month doe sugarcan take ', 'time taken to grow paddi ', 'which month to reap maiz', 'reap time of sugarcan', 'wheat reap season', 'cost of sunflow seed ', 'best place to grow cotton ', 'how much rainfal doe cotton need ', 'fertil doe for maiz ', 'attack of insect on rice ', 'protect against diseas ', 'weed control ', 'weed are grow along with my crop ', 'leav are fall off from wheat crop ', 'caterpillar are eat all leav ', 'what is the market price of rice ', 'how much is the cost of    kg of wheat ', 'what are the price of sugarcan ', 'market price of cotton in belgaum', 'attack of stem borer on sugarcan ', 'when to cultiv maiz ', 'ideal rainfal for cotton ', 'which season to grow rice in ', 'how to improv rice product ', 'market price of maiz ', 'will there be time rain ', 'type of sugarcan ', 'hello balaram ', 'hi', 'hello ', 'hey there', 'hey', 'namaskar', 'namast', 'wassup ', 'what are you do ', 'what is up with you ', 'what are you up to ', 'how is it go ', 'how are you ', 'how you do ', 'how are you do ', 'are you do well ', 'are you okay ', 'are you fine ', 'are you do okay ', 'is everyth okay ', 'are you all right ', 'you alright ', 'how wa your day ', 'how is your day go ', 'how is your day ', 'i need help ', 'need help ', 'help me pleas', 'help me', 'help', 'can you do someth for me ', 'i need you to do me a favour ', 'can you do me a favour ', 'can you help me ', 'i need your help ', 'pleas help me ', 'i need an answer ', 'answer my question ', 'help pleas', 'who is the presid ', 'who is the prime minist ', 'how mani kid do you have ', 'are you an indian citizen ', 'who is your father ', 'what is your father s name ', 'what is your mother s name ', 'do you have parent ', 'do you have sibl ', 'who is you wife ', 'what is your wife s name ', 'are you marri ', 'what is my mother s name ', 'are you a support of bjp ', 'are you activ in polit ', 'are you a god ', 'how old are you ', 'are you a man ', 'are you a bot ', 'do you have a job ', 'who is your best friend ', 'am i your friend ']\n",
      "Corpus created\n",
      "(129, 162)\n",
      "Bag of words created!\n",
      "Intent CountVectorizer saved!\n",
      "Encoded the intent classes!\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "{'AskingHelp': 0, 'BestPractice': 1, 'Cultivation': 2, 'CultivationSeason': 3, 'Diseases': 4, 'EnquireAboutDay': 5, 'Family': 6, 'Fertilizer': 7, 'Greeting': 8, 'HarvestTime': 9, 'Intent': 10, 'Irrigation': 11, 'Location': 12, 'MarketPrice': 13, 'Maturity': 14, 'OutOfScope': 15, 'Pesticide': 16, 'Rainfall': 17, 'ReapingSeason': 18, 'Sarcasm': 19, 'SeedDensity': 20, 'Soil': 21, 'Varieties': 22, 'Wassup': 23, 'Weather': 24, 'Wellness': 25, 'Yield': 26}\n",
      "Intent Label mapping obtained!\n",
      "Epoch 1/500\n",
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      "Epoch 2/500\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
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     ]
    },
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     "text": [
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      "129/129 [==============================] - 0s 294us/step - loss: 3.3804e-05 - acc: 1.0000\n",
      "Epoch 450/500\n",
      "129/129 [==============================] - 0s 333us/step - loss: 3.3513e-05 - acc: 1.0000\n",
      "Epoch 451/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 3.3270e-05 - acc: 1.0000\n",
      "Epoch 452/500\n",
      "129/129 [==============================] - 0s 271us/step - loss: 3.2957e-05 - acc: 1.0000\n",
      "Epoch 453/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 3.2686e-05 - acc: 1.0000\n",
      "Epoch 454/500\n",
      "129/129 [==============================] - 0s 263us/step - loss: 3.2424e-05 - acc: 1.0000\n",
      "Epoch 455/500\n",
      "129/129 [==============================] - 0s 263us/step - loss: 3.2161e-05 - acc: 1.0000\n",
      "Epoch 456/500\n",
      "129/129 [==============================] - 0s 287us/step - loss: 3.1924e-05 - acc: 1.0000\n",
      "Epoch 457/500\n",
      "129/129 [==============================] - 0s 263us/step - loss: 3.1635e-05 - acc: 1.0000\n",
      "Epoch 458/500\n",
      "129/129 [==============================] - 0s 232us/step - loss: 3.1371e-05 - acc: 1.0000\n",
      "Epoch 459/500\n",
      "129/129 [==============================] - 0s 240us/step - loss: 3.1137e-05 - acc: 1.0000\n",
      "Epoch 460/500\n",
      "129/129 [==============================] - 0s 240us/step - loss: 3.0888e-05 - acc: 1.0000\n",
      "Epoch 461/500\n",
      "129/129 [==============================] - 0s 240us/step - loss: 3.0620e-05 - acc: 1.0000\n",
      "Epoch 462/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 3.0376e-05 - acc: 1.0000\n",
      "Epoch 463/500\n",
      "129/129 [==============================] - 0s 217us/step - loss: 3.0126e-05 - acc: 1.0000\n",
      "Epoch 464/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 2.9883e-05 - acc: 1.0000\n",
      "Epoch 465/500\n",
      "129/129 [==============================] - 0s 248us/step - loss: 2.9644e-05 - acc: 1.0000\n",
      "Epoch 466/500\n",
      "129/129 [==============================] - 0s 240us/step - loss: 2.9418e-05 - acc: 1.0000\n",
      "Epoch 467/500\n",
      "129/129 [==============================] - 0s 217us/step - loss: 2.9162e-05 - acc: 1.0000\n",
      "Epoch 468/500\n",
      "129/129 [==============================] - 0s 240us/step - loss: 2.8932e-05 - acc: 1.0000\n",
      "Epoch 469/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 2.8756e-05 - acc: 1.0000\n",
      "Epoch 470/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 2.8468e-05 - acc: 1.0000\n",
      "Epoch 471/500\n",
      "129/129 [==============================] - 0s 209us/step - loss: 2.8232e-05 - acc: 1.0000\n",
      "Epoch 472/500\n",
      "129/129 [==============================] - 0s 263us/step - loss: 2.7989e-05 - acc: 1.0000\n",
      "Epoch 473/500\n",
      "129/129 [==============================] - 0s 209us/step - loss: 2.7777e-05 - acc: 1.0000\n",
      "Epoch 474/500\n",
      "129/129 [==============================] - 0s 240us/step - loss: 2.7558e-05 - acc: 1.0000\n",
      "Epoch 475/500\n",
      "129/129 [==============================] - 0s 256us/step - loss: 2.7358e-05 - acc: 1.0000\n",
      "Epoch 476/500\n",
      "129/129 [==============================] - 0s 194us/step - loss: 2.7096e-05 - acc: 1.0000\n",
      "Epoch 477/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 2.6910e-05 - acc: 1.0000\n",
      "Epoch 478/500\n",
      "129/129 [==============================] - 0s 232us/step - loss: 2.6672e-05 - acc: 1.0000\n",
      "Epoch 479/500\n",
      "129/129 [==============================] - 0s 217us/step - loss: 2.6464e-05 - acc: 1.0000\n",
      "Epoch 480/500\n",
      "129/129 [==============================] - 0s 287us/step - loss: 2.6245e-05 - acc: 1.0000\n",
      "Epoch 481/500\n",
      "129/129 [==============================] - 0s 271us/step - loss: 2.6062e-05 - acc: 1.0000\n",
      "Epoch 482/500\n",
      "129/129 [==============================] - 0s 473us/step - loss: 2.5831e-05 - acc: 1.0000\n",
      "Epoch 483/500\n",
      "129/129 [==============================] - 0s 465us/step - loss: 2.5631e-05 - acc: 1.0000\n",
      "Epoch 484/500\n",
      "129/129 [==============================] - 0s 395us/step - loss: 2.5418e-05 - acc: 1.0000\n",
      "Epoch 485/500\n",
      "129/129 [==============================] - 0s 597us/step - loss: 2.5221e-05 - acc: 1.0000\n",
      "Epoch 486/500\n",
      "129/129 [==============================] - 0s 256us/step - loss: 2.5020e-05 - acc: 1.0000\n",
      "Epoch 487/500\n",
      "129/129 [==============================] - 0s 515us/step - loss: 2.4814e-05 - acc: 1.0000\n",
      "Epoch 488/500\n",
      "129/129 [==============================] - 0s 232us/step - loss: 2.4635e-05 - acc: 1.0000\n",
      "Epoch 489/500\n",
      "129/129 [==============================] - 0s 325us/step - loss: 2.4412e-05 - acc: 1.0000\n",
      "Epoch 490/500\n",
      "129/129 [==============================] - 0s 225us/step - loss: 2.4237e-05 - acc: 1.0000\n",
      "Epoch 491/500\n",
      "129/129 [==============================] - 0s 333us/step - loss: 2.4040e-05 - acc: 1.0000\n",
      "Epoch 492/500\n",
      "129/129 [==============================] - 0s 403us/step - loss: 2.3827e-05 - acc: 1.0000\n",
      "Epoch 493/500\n",
      "129/129 [==============================] - 0s 511us/step - loss: 2.3653e-05 - acc: 1.0000\n",
      "Epoch 494/500\n",
      "129/129 [==============================] - 0s 364us/step - loss: 2.3467e-05 - acc: 1.0000\n",
      "Epoch 495/500\n",
      "129/129 [==============================] - 0s 597us/step - loss: 2.3267e-05 - acc: 1.0000\n",
      "Epoch 496/500\n",
      "129/129 [==============================] - 0s 294us/step - loss: 2.3106e-05 - acc: 1.0000\n",
      "Epoch 497/500\n",
      "129/129 [==============================] - 0s 364us/step - loss: 2.2906e-05 - acc: 1.0000\n",
      "Epoch 498/500\n",
      "129/129 [==============================] - 0s 221us/step - loss: 2.2723e-05 - acc: 1.0000\n",
      "Epoch 499/500\n",
      "129/129 [==============================] - 0s 473us/step - loss: 2.2544e-05 - acc: 1.0000\n",
      "Epoch 500/500\n",
      "129/129 [==============================] - 0s 535us/step - loss: 2.2372e-05 - acc: 1.0000\n",
      "Intent model saved!\n"
     ]
    }
   ],
   "source": [
    "intent_model, intent_label_map = trainIntentModel()\n",
    "\n",
    "# Save the Intent model\n",
    "intent_model.save('saved_state/intent_model.h5')\n",
    "print(\"Intent model saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainEntityModel():\n",
    "    # Importing dataset and splitting into words and labels\n",
    "    dataset = pd.read_csv('datasets/data-tags.csv')\n",
    "    X = dataset.iloc[:, :-1].values\n",
    "    y = dataset.iloc[:, 1].values\n",
    "#     X = X.reshape(630,)\n",
    "    print(X)\n",
    "    print(\"Entity Dataset successfully loaded!\")\n",
    "\n",
    "    entityCorpus=[]\n",
    "    ps = PorterStemmer()\n",
    "\n",
    "    # Stem words in X\n",
    "    for word in X:\n",
    "        word = [ps.stem(word[0])]\n",
    "        entityCorpus.append(word)\n",
    "    \n",
    "    print(entityCorpus)\n",
    "    X = entityCorpus\n",
    "    from numpy import array\n",
    "    X = array(X)\n",
    "    X = X.reshape(542,)\n",
    "    \n",
    "    # Create a bag of words model for words\n",
    "    from sklearn.feature_extraction.text import CountVectorizer\n",
    "    cv = CountVectorizer(max_features=1500)\n",
    "#     X = cv.fit_transform(X.astype('U')).toarray()\n",
    "    X = cv.fit_transform(X).toarray()\n",
    "    print(\"Entity Bag of words created!\")\n",
    "    \n",
    "    # Save CountVectorizer state\n",
    "    pk.dump(cv, open('saved_state/EntityCountVectorizer.sav', 'wb'))\n",
    "    print(\"Entity CountVectorizer state saved!\")\n",
    "    \n",
    "    # Encoding categorical data of labels\n",
    "    labelencoder_y = LabelEncoder()\n",
    "    y = labelencoder_y.fit_transform(y.astype(str))\n",
    "    print(\"Encoded the entity classes!\")\n",
    "    \n",
    "    # Return a dict mapping labels to their integer values\n",
    "    res = {}\n",
    "    for cl in labelencoder_y.classes_:\n",
    "        res.update({cl:labelencoder_y.transform([cl])[0]})\n",
    "    entity_label_map = res\n",
    "    print(\"Entity Label mapping obtained!\")\n",
    "    \n",
    "    # Fit classifier to dataset\n",
    "    classifier = GaussianNB()\n",
    "    classifier.fit(X, y)\n",
    "    print(\"Entity Model trained successfully!\")\n",
    "    \n",
    "    # Save the entity classifier model\n",
    "    pk.dump(classifier, open('saved_state/entity_model.sav', 'wb'))\n",
    "    print(\"Trained entity model saved!\")\n",
    "    \n",
    "    return entity_label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['how']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['rice']\n",
      " ['jowar']\n",
      " ['how']\n",
      " ['much']\n",
      " ['water']\n",
      " ['to']\n",
      " ['add']\n",
      " ['for']\n",
      " ['wheat']\n",
      " ['i']\n",
      " ['best']\n",
      " ['season']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['sugarcane']\n",
      " ['what']\n",
      " ['is']\n",
      " ['the']\n",
      " ['yield']\n",
      " ['of']\n",
      " ['rice']\n",
      " ['in']\n",
      " ['belgaum']\n",
      " ['best']\n",
      " ['pesticide']\n",
      " ['for']\n",
      " ['cotton']\n",
      " ['best']\n",
      " ['soil']\n",
      " ['for']\n",
      " ['cotton']\n",
      " ['how']\n",
      " ['much']\n",
      " ['fertilizer']\n",
      " ['should']\n",
      " ['i']\n",
      " ['add']\n",
      " ['to']\n",
      " ['my']\n",
      " ['rice']\n",
      " ['crop']\n",
      " ['when']\n",
      " ['to']\n",
      " ['sow']\n",
      " ['cotton']\n",
      " ['when']\n",
      " ['to']\n",
      " ['harvest']\n",
      " ['wheat']\n",
      " ['how']\n",
      " ['to']\n",
      " ['enhance']\n",
      " ['yield']\n",
      " ['of']\n",
      " ['sugarcane']\n",
      " ['i']\n",
      " ['want']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['paddy']\n",
      " ['how']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['maize']\n",
      " ['which']\n",
      " ['fertilizer']\n",
      " ['can']\n",
      " ['i']\n",
      " ['use']\n",
      " ['for']\n",
      " ['sugarcane']\n",
      " ['how']\n",
      " ['is']\n",
      " ['the']\n",
      " ['weather']\n",
      " ['today']\n",
      " ['how']\n",
      " ['is']\n",
      " ['the']\n",
      " ['weather']\n",
      " ['tomorrow']\n",
      " ['what']\n",
      " ['is']\n",
      " ['the']\n",
      " ['weather']\n",
      " ['in']\n",
      " ['mysore']\n",
      " ['this']\n",
      " ['week']\n",
      " ['weather']\n",
      " ['this']\n",
      " ['week']\n",
      " ['weather']\n",
      " ['forecast']\n",
      " ['for']\n",
      " ['the']\n",
      " ['week']\n",
      " ['how']\n",
      " ['is']\n",
      " ['the']\n",
      " ['climate']\n",
      " ['today']\n",
      " ['climate']\n",
      " ['conditions']\n",
      " ['for']\n",
      " ['the']\n",
      " ['upcoming']\n",
      " ['days']\n",
      " ['weather']\n",
      " ['report']\n",
      " ['for']\n",
      " ['the']\n",
      " ['day']\n",
      " ['will']\n",
      " ['it']\n",
      " ['rain']\n",
      " ['today']\n",
      " ['is']\n",
      " ['it']\n",
      " ['going']\n",
      " ['to']\n",
      " ['be']\n",
      " ['sunny']\n",
      " ['this']\n",
      " ['week']\n",
      " ['what']\n",
      " ['will']\n",
      " ['the']\n",
      " ['weather']\n",
      " ['be']\n",
      " ['like']\n",
      " ['this']\n",
      " ['week']\n",
      " ['can']\n",
      " ['i']\n",
      " ['expect']\n",
      " ['it']\n",
      " ['to']\n",
      " ['be']\n",
      " ['sunny']\n",
      " ['today']\n",
      " ['which']\n",
      " ['is']\n",
      " ['the']\n",
      " ['season']\n",
      " ['for']\n",
      " ['growing']\n",
      " ['rice']\n",
      " ['season']\n",
      " ['for']\n",
      " ['growing']\n",
      " ['wheat']\n",
      " ['how']\n",
      " ['much']\n",
      " ['cotton']\n",
      " ['seeds']\n",
      " ['are']\n",
      " ['needed']\n",
      " ['for']\n",
      " ['one']\n",
      " ['acre']\n",
      " ['of']\n",
      " ['land']\n",
      " ['can']\n",
      " ['i']\n",
      " ['grow']\n",
      " ['rice']\n",
      " ['in']\n",
      " ['red']\n",
      " ['soil']\n",
      " ['best']\n",
      " ['soil']\n",
      " ['for']\n",
      " ['growing']\n",
      " ['maize']\n",
      " ['irrigation']\n",
      " ['for']\n",
      " ['sugarcane']\n",
      " ['which']\n",
      " ['are']\n",
      " ['the']\n",
      " ['available']\n",
      " ['seed']\n",
      " ['varieties']\n",
      " ['for']\n",
      " ['rice']\n",
      " ['rice']\n",
      " ['varieties']\n",
      " ['different']\n",
      " ['types']\n",
      " ['of']\n",
      " ['wheat']\n",
      " ['seeds']\n",
      " ['types']\n",
      " ['of']\n",
      " ['seeds']\n",
      " ['of']\n",
      " ['cotton']\n",
      " ['which']\n",
      " ['cotton']\n",
      " ['seed']\n",
      " ['grows']\n",
      " ['well']\n",
      " ['in']\n",
      " ['black']\n",
      " ['soil']\n",
      " ['how']\n",
      " ['long']\n",
      " ['does']\n",
      " ['it']\n",
      " ['take']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['rice']\n",
      " ['how']\n",
      " ['many']\n",
      " ['months']\n",
      " ['does']\n",
      " ['sugarcane']\n",
      " ['take']\n",
      " ['time']\n",
      " ['taken']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['paddy']\n",
      " ['which']\n",
      " ['month']\n",
      " ['to']\n",
      " ['reap']\n",
      " ['maize']\n",
      " ['cost']\n",
      " ['of']\n",
      " ['sunflower']\n",
      " ['seeds']\n",
      " ['best']\n",
      " ['places']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['cotton']\n",
      " ['how']\n",
      " ['much']\n",
      " ['rainfall']\n",
      " ['does']\n",
      " ['cotton']\n",
      " ['need']\n",
      " ['fertilizer']\n",
      " ['dose']\n",
      " ['for']\n",
      " ['maize']\n",
      " ['attack']\n",
      " ['of']\n",
      " ['insects']\n",
      " ['on']\n",
      " ['rice']\n",
      " ['protect']\n",
      " ['against']\n",
      " ['diseases']\n",
      " ['weeds']\n",
      " ['control']\n",
      " ['weeds']\n",
      " ['are']\n",
      " ['growing']\n",
      " ['along']\n",
      " ['with']\n",
      " ['my']\n",
      " ['crop']\n",
      " ['leaves']\n",
      " ['are']\n",
      " ['falling']\n",
      " ['off']\n",
      " ['from']\n",
      " ['wheat']\n",
      " ['crop']\n",
      " ['caterpillars']\n",
      " ['are']\n",
      " ['eating']\n",
      " ['all']\n",
      " ['leaves']\n",
      " ['what']\n",
      " ['is']\n",
      " ['the']\n",
      " ['market']\n",
      " ['price']\n",
      " ['of']\n",
      " ['rice']\n",
      " ['how']\n",
      " ['much']\n",
      " ['is']\n",
      " ['the']\n",
      " ['cost']\n",
      " ['of']\n",
      " ['kg']\n",
      " ['of']\n",
      " ['wheat']\n",
      " ['what']\n",
      " ['are']\n",
      " ['the']\n",
      " ['price']\n",
      " ['of']\n",
      " ['sugarcane']\n",
      " ['market']\n",
      " ['price']\n",
      " ['of']\n",
      " ['cotton']\n",
      " ['in']\n",
      " ['belgaum']\n",
      " ['attack']\n",
      " ['of']\n",
      " ['Stemborer']\n",
      " ['on']\n",
      " ['sugarcane']\n",
      " ['when']\n",
      " ['to']\n",
      " ['cultivate']\n",
      " ['maize']\n",
      " ['ideal']\n",
      " ['rainfall']\n",
      " ['for']\n",
      " ['cotton']\n",
      " ['which']\n",
      " ['season']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['rice']\n",
      " ['in']\n",
      " ['how']\n",
      " ['to']\n",
      " ['improve']\n",
      " ['rice']\n",
      " ['production']\n",
      " ['market']\n",
      " ['price']\n",
      " ['of']\n",
      " ['maize']\n",
      " ['will']\n",
      " ['there']\n",
      " ['be']\n",
      " ['timely']\n",
      " ['rain']\n",
      " ['types']\n",
      " ['of']\n",
      " ['sugarcane']\n",
      " ['cost']\n",
      " ['of']\n",
      " ['wheat']\n",
      " ['in']\n",
      " ['market']\n",
      " ['crops']\n",
      " ['which']\n",
      " ['need']\n",
      " ['low']\n",
      " ['rainfall']\n",
      " ['amount']\n",
      " ['of']\n",
      " ['rainfall']\n",
      " ['paddy']\n",
      " ['needs']\n",
      " ['high']\n",
      " ['rainfall']\n",
      " ['crops']\n",
      " ['does']\n",
      " ['sugarcane']\n",
      " ['grow']\n",
      " ['in']\n",
      " ['low']\n",
      " ['rain']\n",
      " ['fertilizer']\n",
      " ['dose']\n",
      " ['for']\n",
      " ['ragi']\n",
      " ['which']\n",
      " ['pesticide']\n",
      " ['to']\n",
      " ['use']\n",
      " ['for']\n",
      " ['cotton']\n",
      " ['can']\n",
      " ['i']\n",
      " ['grow']\n",
      " ['wheat']\n",
      " ['in']\n",
      " ['red']\n",
      " ['soil']\n",
      " ['jowar']\n",
      " ['best']\n",
      " ['soil']\n",
      " ['for']\n",
      " ['growing']\n",
      " ['sunflower']\n",
      " ['irrigation']\n",
      " ['for']\n",
      " ['maize']\n",
      " ['which']\n",
      " ['are']\n",
      " ['the']\n",
      " ['available']\n",
      " ['seed']\n",
      " ['varieties']\n",
      " ['for']\n",
      " ['paddy']\n",
      " ['best']\n",
      " ['fertilizer']\n",
      " ['for']\n",
      " ['ragi']\n",
      " ['varieties']\n",
      " ['different']\n",
      " ['types']\n",
      " ['of']\n",
      " ['wheat']\n",
      " ['seeds']\n",
      " ['fertilizer']\n",
      " ['and']\n",
      " ['pesticide']\n",
      " ['for']\n",
      " ['rice']\n",
      " ['rice']\n",
      " ['seed']\n",
      " ['quantity']\n",
      " ['for']\n",
      " ['land']\n",
      " ['how']\n",
      " ['much']\n",
      " ['maize']\n",
      " ['seeds']\n",
      " ['to']\n",
      " ['use']\n",
      " ['for']\n",
      " ['one']\n",
      " ['hector']\n",
      " ['ideal']\n",
      " ['weather']\n",
      " ['for']\n",
      " ['sugarcane']\n",
      " ['best']\n",
      " ['weather']\n",
      " ['for']\n",
      " ['rice']\n",
      " ['which']\n",
      " ['is']\n",
      " ['the']\n",
      " ['best']\n",
      " ['rice']\n",
      " ['variety']\n",
      " ['for']\n",
      " ['red']\n",
      " ['soil']\n",
      " ['which']\n",
      " ['maize']\n",
      " ['seed']\n",
      " ['variety']\n",
      " ['has']\n",
      " ['highest']\n",
      " ['yield']\n",
      " ['which']\n",
      " ['crop']\n",
      " ['has']\n",
      " ['high']\n",
      " ['yield']\n",
      " ['how']\n",
      " ['to']\n",
      " ['improve']\n",
      " ['sugarcane']\n",
      " ['production']\n",
      " ['how']\n",
      " ['to']\n",
      " ['get']\n",
      " ['more']\n",
      " ['cotton']\n",
      " ['yield']\n",
      " ['cotton']\n",
      " ['variety']\n",
      " ['with']\n",
      " ['highest']\n",
      " ['yield']\n",
      " ['month']\n",
      " ['to']\n",
      " ['grow']\n",
      " ['maize']\n",
      " ['how']\n",
      " ['is']\n",
      " ['the']\n",
      " ['weather']\n",
      " ['will']\n",
      " ['it']\n",
      " ['rain']\n",
      " ['this']\n",
      " ['weekend']\n",
      " ['cashew']\n",
      " ['coconut']\n",
      " ['arecanuts']\n",
      " ['chilly']\n",
      " ['tobacco']\n",
      " ['soyabean']\n",
      " ['cardamom']\n",
      " ['pesticide']\n",
      " ['to']\n",
      " ['use']\n",
      " ['for']\n",
      " ['maize']\n",
      " ['fertilizer']\n",
      " ['for']\n",
      " ['ragi']\n",
      " ['field']\n",
      " ['required']\n",
      " ['rainfall']\n",
      " ['for']\n",
      " ['sugarcane']\n",
      " ['weather']\n",
      " ['this']\n",
      " ['week']\n",
      " ['my']\n",
      " ['name']\n",
      " ['is']\n",
      " ['ram']\n",
      " ['hi']\n",
      " ['i']\n",
      " ['am']\n",
      " ['vaishnavi']\n",
      " ['hey']\n",
      " ['hello']\n",
      " ['namaste']\n",
      " ['heya']\n",
      " ['wassup']\n",
      " ['cashew']\n",
      " ['coconut']\n",
      " ['arecanuts']\n",
      " ['chilly']\n",
      " ['tobacco']\n",
      " ['soyabean']\n",
      " ['cardamom']\n",
      " ['groundnut']\n",
      " ['gram']\n",
      " ['peas']\n",
      " ['green']\n",
      " ['chick']\n",
      " ['yellow']\n",
      " ['black']\n",
      " ['bye']\n",
      " ['goodbye']]\n",
      "Entity Dataset successfully loaded!\n",
      "[['how'], ['to'], ['grow'], ['rice'], ['jowar'], ['how'], ['much'], ['water'], ['to'], ['add'], ['for'], ['wheat'], ['i'], ['best'], ['season'], ['to'], ['grow'], ['sugarcan'], ['what'], ['is'], ['the'], ['yield'], ['of'], ['rice'], ['in'], ['belgaum'], ['best'], ['pesticid'], ['for'], ['cotton'], ['best'], ['soil'], ['for'], ['cotton'], ['how'], ['much'], ['fertil'], ['should'], ['i'], ['add'], ['to'], ['my'], ['rice'], ['crop'], ['when'], ['to'], ['sow'], ['cotton'], ['when'], ['to'], ['harvest'], ['wheat'], ['how'], ['to'], ['enhanc'], ['yield'], ['of'], ['sugarcan'], ['i'], ['want'], ['to'], ['grow'], ['paddi'], ['how'], ['to'], ['grow'], ['maiz'], ['which'], ['fertil'], ['can'], ['i'], ['use'], ['for'], ['sugarcan'], ['how'], ['is'], ['the'], ['weather'], ['today'], ['how'], ['is'], ['the'], ['weather'], ['tomorrow'], ['what'], ['is'], ['the'], ['weather'], ['in'], ['mysor'], ['thi'], ['week'], ['weather'], ['thi'], ['week'], ['weather'], ['forecast'], ['for'], ['the'], ['week'], ['how'], ['is'], ['the'], ['climat'], ['today'], ['climat'], ['condit'], ['for'], ['the'], ['upcom'], ['day'], ['weather'], ['report'], ['for'], ['the'], ['day'], ['will'], ['it'], ['rain'], ['today'], ['is'], ['it'], ['go'], ['to'], ['be'], ['sunni'], ['thi'], ['week'], ['what'], ['will'], ['the'], ['weather'], ['be'], ['like'], ['thi'], ['week'], ['can'], ['i'], ['expect'], ['it'], ['to'], ['be'], ['sunni'], ['today'], ['which'], ['is'], ['the'], ['season'], ['for'], ['grow'], ['rice'], ['season'], ['for'], ['grow'], ['wheat'], ['how'], ['much'], ['cotton'], ['seed'], ['are'], ['need'], ['for'], ['one'], ['acr'], ['of'], ['land'], ['can'], ['i'], ['grow'], ['rice'], ['in'], ['red'], ['soil'], ['best'], ['soil'], ['for'], ['grow'], ['maiz'], ['irrig'], ['for'], ['sugarcan'], ['which'], ['are'], ['the'], ['avail'], ['seed'], ['varieti'], ['for'], ['rice'], ['rice'], ['varieti'], ['differ'], ['type'], ['of'], ['wheat'], ['seed'], ['type'], ['of'], ['seed'], ['of'], ['cotton'], ['which'], ['cotton'], ['seed'], ['grow'], ['well'], ['in'], ['black'], ['soil'], ['how'], ['long'], ['doe'], ['it'], ['take'], ['to'], ['grow'], ['rice'], ['how'], ['mani'], ['month'], ['doe'], ['sugarcan'], ['take'], ['time'], ['taken'], ['to'], ['grow'], ['paddi'], ['which'], ['month'], ['to'], ['reap'], ['maiz'], ['cost'], ['of'], ['sunflow'], ['seed'], ['best'], ['place'], ['to'], ['grow'], ['cotton'], ['how'], ['much'], ['rainfal'], ['doe'], ['cotton'], ['need'], ['fertil'], ['dose'], ['for'], ['maiz'], ['attack'], ['of'], ['insect'], ['on'], ['rice'], ['protect'], ['against'], ['diseas'], ['weed'], ['control'], ['weed'], ['are'], ['grow'], ['along'], ['with'], ['my'], ['crop'], ['leav'], ['are'], ['fall'], ['off'], ['from'], ['wheat'], ['crop'], ['caterpillar'], ['are'], ['eat'], ['all'], ['leav'], ['what'], ['is'], ['the'], ['market'], ['price'], ['of'], ['rice'], ['how'], ['much'], ['is'], ['the'], ['cost'], ['of'], ['kg'], ['of'], ['wheat'], ['what'], ['are'], ['the'], ['price'], ['of'], ['sugarcan'], ['market'], ['price'], ['of'], ['cotton'], ['in'], ['belgaum'], ['attack'], ['of'], ['stembor'], ['on'], ['sugarcan'], ['when'], ['to'], ['cultiv'], ['maiz'], ['ideal'], ['rainfal'], ['for'], ['cotton'], ['which'], ['season'], ['to'], ['grow'], ['rice'], ['in'], ['how'], ['to'], ['improv'], ['rice'], ['product'], ['market'], ['price'], ['of'], ['maiz'], ['will'], ['there'], ['be'], ['time'], ['rain'], ['type'], ['of'], ['sugarcan'], ['cost'], ['of'], ['wheat'], ['in'], ['market'], ['crop'], ['which'], ['need'], ['low'], ['rainfal'], ['amount'], ['of'], ['rainfal'], ['paddi'], ['need'], ['high'], ['rainfal'], ['crop'], ['doe'], ['sugarcan'], ['grow'], ['in'], ['low'], ['rain'], ['fertil'], ['dose'], ['for'], ['ragi'], ['which'], ['pesticid'], ['to'], ['use'], ['for'], ['cotton'], ['can'], ['i'], ['grow'], ['wheat'], ['in'], ['red'], ['soil'], ['jowar'], ['best'], ['soil'], ['for'], ['grow'], ['sunflow'], ['irrig'], ['for'], ['maiz'], ['which'], ['are'], ['the'], ['avail'], ['seed'], ['varieti'], ['for'], ['paddi'], ['best'], ['fertil'], ['for'], ['ragi'], ['varieti'], ['differ'], ['type'], ['of'], ['wheat'], ['seed'], ['fertil'], ['and'], ['pesticid'], ['for'], ['rice'], ['rice'], ['seed'], ['quantiti'], ['for'], ['land'], ['how'], ['much'], ['maiz'], ['seed'], ['to'], ['use'], ['for'], ['one'], ['hector'], ['ideal'], ['weather'], ['for'], ['sugarcan'], ['best'], ['weather'], ['for'], ['rice'], ['which'], ['is'], ['the'], ['best'], ['rice'], ['varieti'], ['for'], ['red'], ['soil'], ['which'], ['maiz'], ['seed'], ['varieti'], ['ha'], ['highest'], ['yield'], ['which'], ['crop'], ['ha'], ['high'], ['yield'], ['how'], ['to'], ['improv'], ['sugarcan'], ['product'], ['how'], ['to'], ['get'], ['more'], ['cotton'], ['yield'], ['cotton'], ['varieti'], ['with'], ['highest'], ['yield'], ['month'], ['to'], ['grow'], ['maiz'], ['how'], ['is'], ['the'], ['weather'], ['will'], ['it'], ['rain'], ['thi'], ['weekend'], ['cashew'], ['coconut'], ['arecanut'], ['chilli'], ['tobacco'], ['soyabean'], ['cardamom'], ['pesticid'], ['to'], ['use'], ['for'], ['maiz'], ['fertil'], ['for'], ['ragi'], ['field'], ['requir'], ['rainfal'], ['for'], ['sugarcan'], ['weather'], ['thi'], ['week'], ['my'], ['name'], ['is'], ['ram'], ['hi'], ['i'], ['am'], ['vaishnavi'], ['hey'], ['hello'], ['namast'], ['heya'], ['wassup'], ['cashew'], ['coconut'], ['arecanut'], ['chilli'], ['tobacco'], ['soyabean'], ['cardamom'], ['groundnut'], ['gram'], ['pea'], ['green'], ['chick'], ['yellow'], ['black'], ['bye'], ['goodby']]\n",
      "Entity Bag of words created!\n",
      "Entity CountVectorizer state saved!\n",
      "Encoded the entity classes!\n",
      "Entity Label mapping obtained!\n",
      "Entity Model trained successfully!\n",
      "Trained entity model saved!\n"
     ]
    }
   ],
   "source": [
    "# Load Entity model\n",
    "entity_label_map = trainEntityModel()\n",
    "\n",
    "loadedEntityCV = pk.load(open('saved_state/EntityCountVectorizer.sav', 'rb'))\n",
    "loadedEntityClassifier = pk.load(open('saved_state/entity_model.sav', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEntities(query):\n",
    "    query = loadedEntityCV.transform(query).toarray()\n",
    "    \n",
    "    response_tags = loadedEntityClassifier.predict(query)\n",
    "    \n",
    "    entity_list=[]\n",
    "    for tag in response_tags:\n",
    "        if tag in entity_label_map.values():\n",
    "            entity_list.append(list(entity_label_map.keys())[list(entity_label_map.values()).index(tag)])\n",
    "\n",
    "    return entity_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n",
      "Greeting\n",
      "{'GREET': 'hi'}\n",
      "namaste\n",
      "Greeting\n",
      "{'GREET': 'namast'}\n",
      "rainfall for rice\n",
      "Rainfall\n",
      "{'RAIN': 'rainfal', 'SW': 'for', 'CROP': 'rice'}\n",
      "how much to irrigate cotton\n",
      "Irrigation\n",
      "{'SW': 'to', 'IRR': 'irrig', 'CROP': 'cotton'}\n",
      "i am vaishnavi\n",
      "Wassup\n",
      "{'USR': 'i', 'SW': 'am', 'NAME': 'vaishnavi'}\n",
      "my name is vaishnavi\n",
      "OutOfScope\n",
      "{'USR': 'my', 'INT': 'name', 'SW': 'is', 'NAME': 'vaishnavi'}\n",
      "who are you?\n",
      "Wellness\n",
      "{'USR': '', 'SW': 'are'}\n",
      "what is your name?\n",
      "Family\n",
      "{'SW': 'is', 'USR': '', 'INT': 'name'}\n"
     ]
    }
   ],
   "source": [
    "# Load model to predict user result\n",
    "loadedIntentClassifier = load_model('saved_state/intent_model.h5')\n",
    "loaded_intent_CV = pk.load(open('saved_state/IntentCountVectorizer.sav', 'rb'))    \n",
    "\n",
    "USER_INTENT = \"\"\n",
    "\n",
    "while True:\n",
    "    user_query = input()\n",
    "    \n",
    "    query = re.sub('[^a-zA-Z]', ' ', user_query)\n",
    "\n",
    "    # Tokenize sentence\n",
    "    query = query.split(' ')\n",
    "\n",
    "    # Lemmatizing\n",
    "    ps = PorterStemmer()\n",
    "    tokenized_query = [ps.stem(word.lower()) for word in query]\n",
    "\n",
    "    # Recreate the sentence from tokens\n",
    "    processed_text = ' '.join(tokenized_query)\n",
    "    \n",
    "    # Transform the query using the CountVectorizer\n",
    "    processed_text = loaded_intent_CV.transform([processed_text]).toarray()\n",
    "\n",
    "    # Make the prediction\n",
    "    predicted_Intent = loadedIntentClassifier.predict(processed_text)\n",
    "#     print(predicted_Intent)\n",
    "    result = np.argmax(predicted_Intent, axis=1)\n",
    "    \n",
    "    for key, value in intent_label_map.items():\n",
    "        if value==result[0]:\n",
    "            print(key)\n",
    "            USER_INTENT = key\n",
    "            break\n",
    "            \n",
    "    # Extract entities from text\n",
    "    entities = getEntities(tokenized_query)\n",
    "    \n",
    "    # Mapping between tokens and entity tags\n",
    "    token_entity_map = dict(zip(entities, tokenized_query))\n",
    "    print(token_entity_map)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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