ML Bot = Day 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Bot Day 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC #importing machine learning classification algorithm\n",
    "import pickle\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [[\"Is ticket for seminar available?\",1],[\"Are there tickets available for seminar?\",1],[\"I want to attend the seminar\",1],[\"Hi Good Morning\",0],[\"Hello YCCEian\",0],[\"heya\",0],[\"Goodbye\",4],['Byebye',4],['Bye',4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting it into pandas dataframe\n",
    "df = pd.DataFrame(data, columns = ['text', 'intent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df   #1 -> availability and 0 -> greeting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['text']   #seperating x from data\n",
    "y = df['intent']   # seperating y from data (intents are encoded into numbers as machine can only predict numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(x)  # X has tfidfed vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LinearSVC(max_iter =800,C=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_test = [\"Hello YCCEian\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = vectorizer.transform(text_test) #converting text data into vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(clf,open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_model=pickle.load(open('model.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "responses = {0 : {\"intent\":\"greetings\",\"response\":['Hi dear','How are you?','Watsupp dude']}, \n",
    "      1 : {\"intent\":\"availability\",\"response\":['Yes Yes it is available ','Yes tickets are available',\n",
    "                                               'Ofcourse there are tickets for you']}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.choice(responses[prediction[0]][\"response\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.choice([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def response(user_response):\n",
    "    text_test = [user_response]\n",
    "    X_test = vectorizer.transform(text_test)\n",
    "    prediction = clf.predict(X_test)\n",
    "    reply = random.choice(responses[prediction[0]][\"response\"])\n",
    "    return reply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flag=True\n",
    "print(\"YCCEbot: My name is YCCEbot. I will answer everything about myself. If you want to exit, type Bye!\")\n",
    "while(flag==True):\n",
    "    user_response = input()\n",
    "    user_response=user_response.lower()\n",
    "    if(user_response!='bye'):\n",
    "        if(user_response=='thanks' or user_response=='thank you' ):\n",
    "            flag=False\n",
    "            print(\"YCCEbot: You are welcome..\")\n",
    "        else:\n",
    "            print(\"YCCEbot: \",end=\"\")\n",
    "            print(response(user_response))\n",
    "                    \n",
    "    else:\n",
    "        flag=False\n",
    "        print(\"YCCEbot: Bye! Stay MAD Stay Creative.\")\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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