Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fca994ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\gouri\\anaconda3\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "414 documents\n",
      "40 classes ['admission', 'canteen', 'college intake', 'committee', 'computerhod', 'course', 'creator', 'document', 'event', 'extchod', 'facilities', 'fees', 'floors', 'goodbye', 'greeting', 'hod', 'hostel', 'hours', 'infrastructure', 'ithod', 'library', 'location', 'menu', 'name', 'number', 'options', 'placement', 'principal', 'ragging', 'random', 'salutaion', 'scholarship', 'sem', 'sports', 'swear', 'syllabus', 'task', 'thanks', 'uniform', 'vacation']\n",
      "263 unique lemmatized words [\"'s\", '(', ')', ',', 'a', 'about', 'ac', 'active', 'activity', 'address', 'admision', 'admission', 'against', 'ai/ml', 'allotment', 'am', 'an', 'and', 'antiragging', 'any', 'anyone', 'are', 'as', 'asshole', 'at', 'attend', 'automobile', 'available', 'average', 'awesome', 'be', 'between', 'big', 'bitch', 'book', 'boy', 'branch', 'bring', 'building', 'by', 'bye', 'cafetaria', 'call', 'called', 'campus', 'can', 'canteen', 'capacity', 'case', 'casuals', 'ce', 'chatting', 'chemical', 'civil', 'code', 'college', 'come', 'committe', 'committee', 'comp', 'company', 'computer', 'conducted', 'contact', 'could', 'course', 'create', 'created', 'creator', 'date', 'day', 'designed', 'detail', 'developer', 'different', 'distance', 'do', 'document', 'doe', 'done', 'dress', 'dresscode', 'dumb', 'during', 'each', 'eat', 'end', 'engineering', 'event', 'exam', 'extc', 'facility', 'far', 'fee', 'first', 'floor', 'food', 'for', 'fourth', 'from', 'fuck', 'fucker', 'function', 'game', 'get', 'girl', 'give', 'good', 'goodbye', 'guy', 'have', 'held', 'hell', 'hello', 'help', 'helpful', 'helping', 'here', 'hey', 'hi', 'history', 'hod', 'hola', 'holiday', 'hostel', 'hour', 'how', 'i', 'idiot', 'in', 'incident', 'info', 'information', 'infrastructure', 'intake', 'is', 'it', 'job', 'junior', 'k', 'later', 'lecture', 'library', 'list', 'located', 'location', 'long', 'love', 'made', 'many', 'marry', 'max', 'maximum', 'me', 'mechanical', 'menu', 'more', 'much', 'my', 'name', 'need', 'needed', 'next', 'nice', 'no', 'non-ac', 'number', 'of', 'offer', 'offered', 'office', 'ok', 'okay', 'okie', 'okk', 'on', 'open', 'operation', 'organised', 'package', 'per', 'phone', 'placement', 'please', 'practice', 'principal', 'process', 'provide', 'provided', 'ragging', 'reach', 'recruitment', 'required', 'room', 'saturday', 'schedule', 'scholarship', 'seat', 'second', 'see', 'sem', 'semester', 'servive', 'should', 'shut', 'size', 'something', 'sport', 'start', 'student', 'stundent', 'stupid', 'support', 'syllabus', 'take', 'taken', 'taking', 'tall', 'technology', 'telephone', 'tell', 'thank', 'thanks', 'that', 'the', 'there', 'these', 'thing', 'third', 'this', 'till', 'time', 'timetable', 'timing', 'to', 'u', 'uni', 'uniform', 'univrsity', 'up', 'use', 'vacation', 'variety', 'visit', 'we', 'wear', 'well', 'what', 'whats', 'whatv', 'when', 'where', 'wheres', 'which', 'who', 'whom', 'why', 'will', 'work', 'working', 'year', 'you', 'your']\n",
      "Training and testing data created\n",
      "WARNING:tensorflow:From C:\\Users\\gouri\\anaconda3\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "Epoch 1/200\n",
      "WARNING:tensorflow:From C:\\Users\\gouri\\anaconda3\\lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\gouri\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "83/83 [==============================] - 2s 4ms/step - loss: 3.6520 - accuracy: 0.0411\n",
      "Epoch 2/200\n",
      "83/83 [==============================] - 0s 3ms/step - loss: 3.4762 - accuracy: 0.1063\n",
      "Epoch 3/200\n",
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      "Epoch 4/200\n",
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      "Epoch 43/200\n",
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      "Epoch 44/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83/83 [==============================] - 0s 4ms/step - loss: 0.2938 - accuracy: 0.9203\n",
      "Epoch 45/200\n",
      "83/83 [==============================] - 0s 3ms/step - loss: 0.3204 - accuracy: 0.9082\n",
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      "Epoch 125/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83/83 [==============================] - 0s 3ms/step - loss: 0.1950 - accuracy: 0.9227\n",
      "Epoch 126/200\n",
      "83/83 [==============================] - 0s 3ms/step - loss: 0.1502 - accuracy: 0.9517\n",
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      "Epoch 191/200\n",
      "83/83 [==============================] - 0s 5ms/step - loss: 0.1960 - accuracy: 0.9300\n",
      "Epoch 192/200\n",
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      "Epoch 194/200\n",
      "83/83 [==============================] - 0s 5ms/step - loss: 0.1396 - accuracy: 0.9614\n",
      "Epoch 195/200\n",
      "83/83 [==============================] - 0s 4ms/step - loss: 0.1542 - accuracy: 0.9420\n",
      "Epoch 196/200\n",
      "83/83 [==============================] - 0s 5ms/step - loss: 0.1486 - accuracy: 0.9493\n",
      "Epoch 197/200\n",
      "83/83 [==============================] - 0s 4ms/step - loss: 0.2172 - accuracy: 0.9324\n",
      "Epoch 198/200\n",
      "83/83 [==============================] - 0s 5ms/step - loss: 0.1337 - accuracy: 0.9638\n",
      "Epoch 199/200\n",
      "83/83 [==============================] - 0s 4ms/step - loss: 0.1197 - accuracy: 0.9614\n",
      "Epoch 200/200\n",
      "83/83 [==============================] - 0s 4ms/step - loss: 0.1277 - accuracy: 0.9565\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gouri\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras.optimizers import SGD\n",
    "import random\n",
    "words=[]\n",
    "classes = []\n",
    "documents = []\n",
    "ignore_words = ['?', '!']\n",
    "data_file = open(\"C:\\\\Users\\\\gouri\\\\OneDrive\\\\Desktop\\\\mom\\\\data.json\").read()\n",
    "intents = json.loads(data_file)\n",
    "for intent in intents['intents']:\n",
    "    for pattern in intent['patterns']:\n",
    "\n",
    "        #tokenize each word\n",
    "        w = nltk.word_tokenize(pattern)\n",
    "        words.extend(w)\n",
    "        #add documents in the corpus\n",
    "        documents.append((w, intent['tag']))\n",
    "\n",
    "        # add to our classes list\n",
    "        if intent['tag'] not in classes:\n",
    "            classes.append(intent['tag'])\n",
    "# lemmaztize and lower each word and remove duplicates\n",
    "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
    "words = sorted(list(set(words)))\n",
    "# sort classes\n",
    "classes = sorted(list(set(classes)))\n",
    "# documents = combination between patterns and intents\n",
    "print (len(documents), \"documents\")\n",
    "# classes = intents\n",
    "print (len(classes), \"classes\", classes)\n",
    "# words = all words, vocabulary\n",
    "print (len(words), \"unique lemmatized words\", words)\n",
    "pickle.dump(words,open('texts.pkl','wb'))\n",
    "pickle.dump(classes,open('labels.pkl','wb'))\n",
    "# create our training data\n",
    "training = []\n",
    "# create an empty array for our output\n",
    "output_empty = [0] * len(classes)\n",
    "# training set, bag of words for each sentence\n",
    "for doc in documents:\n",
    "    # initialize our bag of words\n",
    "    bag = []\n",
    "    # list of tokenized words for the pattern\n",
    "    pattern_words = doc[0]\n",
    "    # lemmatize each word - create base word, in attempt to represent related words\n",
    "    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]\n",
    "    # create our bag of words array with 1, if word match found in current pattern\n",
    "    for w in words:\n",
    "        bag.append(1) if w in pattern_words else bag.append(0)\n",
    "    \n",
    "    # output is a '0' for each tag and '1' for current tag (for each pattern)\n",
    "    output_row = list(output_empty)\n",
    "    output_row[classes.index(doc[1])] = 1\n",
    "    \n",
    "    training.append([bag, output_row])\n",
    "import random\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assuming you have a list of pairs (features, labels)\n",
    "# Example data format: training = [(feature1, label1), (feature2, label2), ...]\n",
    "\n",
    "# Shuffle the data\n",
    "random.shuffle(training)\n",
    "\n",
    "# Separate features and labels\n",
    "features, labels = zip(*training)\n",
    "\n",
    "# Convert to NumPy arrays\n",
    "train_x = np.array(features)\n",
    "train_y = np.array(labels)\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Training and testing data created\")\n",
    "# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons\n",
    "# equal to number of intents to predict output intent with softmax\n",
    "model = Sequential()\n",
    "model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(len(train_y[0]), activation='softmax'))\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "# Define the learning rate\n",
    "learning_rate = 0.01\n",
    "\n",
    "# Create the SGD optimizer with Nesterov momentum\n",
    "sgd = SGD(learning_rate=learning_rate, momentum=0.9, nesterov=True)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "# Rest of your code for model training and saving remains unchanged.\n",
    "hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)\n",
    "model.save('model.h5', hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a78e47a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading collection 'popular'\n",
      "[nltk_data]    | \n",
      "[nltk_data]    | Downloading package cmudict to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package cmudict is already up-to-date!\n",
      "[nltk_data]    | Downloading package gazetteers to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package gazetteers is already up-to-date!\n",
      "[nltk_data]    | Downloading package genesis to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package genesis is already up-to-date!\n",
      "[nltk_data]    | Downloading package gutenberg to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package gutenberg is already up-to-date!\n",
      "[nltk_data]    | Downloading package inaugural to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package inaugural is already up-to-date!\n",
      "[nltk_data]    | Downloading package movie_reviews to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package movie_reviews is already up-to-date!\n",
      "[nltk_data]    | Downloading package names to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package names is already up-to-date!\n",
      "[nltk_data]    | Downloading package shakespeare to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package shakespeare is already up-to-date!\n",
      "[nltk_data]    | Downloading package stopwords to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package stopwords is already up-to-date!\n",
      "[nltk_data]    | Downloading package treebank to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package treebank is already up-to-date!\n",
      "[nltk_data]    | Downloading package twitter_samples to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package twitter_samples is already up-to-date!\n",
      "[nltk_data]    | Downloading package omw to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package omw is already up-to-date!\n",
      "[nltk_data]    | Downloading package omw-1.4 to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
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      "[nltk_data]    | Downloading package wordnet to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package wordnet is already up-to-date!\n",
      "[nltk_data]    | Downloading package wordnet2021 to\n",
      "[nltk_data]    |     C:\\Users\\gouri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]    |   Package wordnet2021 is already up-to-date!\n",
      "[nltk_data]    | Downloading package wordnet31 to\n",
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      "[nltk_data]    |   Package wordnet31 is already up-to-date!\n",
      "[nltk_data]    | Downloading package wordnet_ic to\n",
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      "[nltk_data]    |   Package wordnet_ic is already up-to-date!\n",
      "[nltk_data]    | Downloading package words to\n",
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      "[nltk_data]    |   Package words is already up-to-date!\n",
      "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
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      "[nltk_data]    | Downloading package snowball_data to\n",
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      "[nltk_data]    |       to-date!\n",
      "[nltk_data]    | \n",
      "[nltk_data]  Done downloading collection popular\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app '__main__'\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n",
      " * Running on http://127.0.0.1:5000\n",
      "Press CTRL+C to quit\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('popular')\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "from keras.models import load_model\n",
    "model = load_model('model.h5')\n",
    "import json\n",
    "import random\n",
    "intents = json.loads(open('data.json').read())\n",
    "words = pickle.load(open('texts.pkl','rb'))\n",
    "classes = pickle.load(open('labels.pkl','rb'))\n",
    "\n",
    "def clean_up_sentence(sentence):\n",
    "    # tokenize the pattern - split words into array\n",
    "    sentence_words = nltk.word_tokenize(sentence)\n",
    "    # stem each word - create short form for word\n",
    "    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]\n",
    "    return sentence_words\n",
    "\n",
    "# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence\n",
    "\n",
    "def bow(sentence, words, show_details=True):\n",
    "    # tokenize the pattern\n",
    "    sentence_words = clean_up_sentence(sentence)\n",
    "    # bag of words - matrix of N words, vocabulary matrix\n",
    "    bag = [0]*len(words)  \n",
    "    for s in sentence_words:\n",
    "        for i,w in enumerate(words):\n",
    "            if w == s: \n",
    "                # assign 1 if current word is in the vocabulary position\n",
    "                bag[i] = 1\n",
    "                if show_details:\n",
    "                    print (\"found in bag: %s\" % w)\n",
    "    return(np.array(bag))\n",
    "\n",
    "def predict_class(sentence, model):\n",
    "    # filter out predictions below a threshold\n",
    "    p = bow(sentence, words,show_details=False)\n",
    "    res = model.predict(np.array([p]))[0]\n",
    "    ERROR_THRESHOLD = 0.25\n",
    "    results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]\n",
    "    # sort by strength of probability\n",
    "    results.sort(key=lambda x: x[1], reverse=True)\n",
    "    return_list = []\n",
    "    for r in results:\n",
    "        return_list.append({\"intent\": classes[r[0]], \"probability\": str(r[1])})\n",
    "    return return_list\n",
    "\n",
    "def getResponse(ints, intents_json):\n",
    "    tag = ints[0]['intent']\n",
    "    list_of_intents = intents_json['intents']\n",
    "    for i in list_of_intents:\n",
    "        if(i['tag']== tag):\n",
    "            result = random.choice(i['responses'])\n",
    "            break\n",
    "    return result\n",
    "\n",
    "def chatbot_response(msg):\n",
    "    ints = predict_class(msg, model)\n",
    "    res = getResponse(ints, intents)\n",
    "    return res\n",
    "\n",
    "\n",
    "from flask import Flask, render_template, request\n",
    "\n",
    "app = Flask(__name__, template_folder='templates')\n",
    "\n",
    "app.static_folder = 'static'\n",
    "\n",
    "@app.route(\"/\")\n",
    "def home():\n",
    "    return render_template(\"index.html\")\n",
    "\n",
    "@app.route(\"/get\")\n",
    "def get_bot_response():\n",
    "    userText = request.args.get('msg')\n",
    "    return chatbot_response(userText)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    app.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de0757f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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