Chatbot_NLP.ipynb

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  "metadata": {
    "colab": {
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      "authorship_tag": "ABX9TyOf0MlSakwGUpwQwyIj0hhx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/varsha108/python-programs/blob/master/Chatbot_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\"intents\": [\n",
        "    {\"tag\":\"greeting\",\n",
        "     \"patterns\":[\"Hi\", \"Hello\", \"How are you doing?\", \"Wassup?\", \"Yo\"],\n",
        "     \"responses\": [\"Howdy!\", \"Yo!\", \"Hello there!\", \"Hiya\"]\n",
        "     },\n",
        "    {\"tag\":\"age\",\n",
        "     \"patterns\":[\"How old are you?\", \"What is your age?\", \"When were you born?\"],\n",
        "     \"responses\": [\"I am 23\", \"I was born in 2000\", \"I'm an adult, aged 23\", \"My birth day is 4th June, and year 2000\"]\n",
        "     },\n",
        "    {\"tag\":\"daily\",\n",
        "     \"patterns\":[\"what do you do?\", \"Do you like hanging out?\", \"How is your day?\"],\n",
        "     \"responses\": [\"I am a student and I am mostly free on weekends\", \"My day goes by studing, going to classes and hanging out\", \"I do like to hangout on weekends\"]\n",
        "    },\n",
        "    {\"tag\":\"name\",\n",
        "     \"patterns\":[\"What is your name?\", \"What do I call you?\", \"Can I know your name?\"],\n",
        "     \"responses\": [\"My name is Shiro\", \"I'm Shiro, nice to make your acquaintance!\", \"I'm known as Shiro\"]\n",
        "     },\n",
        "    {\"tag\":\"Hobby\",\n",
        "     \"patterns\":[\"What do like to do in your freetime?\", \"What is your hobby?\", \"How do you spend free time?\"],\n",
        "     \"responses\": [\"I like to read books. My favourite genre is crime thriller.\", \"I'm a book worm\", \"I spend my time reading fiction novels\"]\n",
        "     },\n",
        "    {\"tag\":\"goodbye\",\n",
        "     \"patterns\":[\"bye\",\"got to go\",\"see ya\",\"adios\",\"cya\",\"ciao\"],\n",
        "     \"responses\":[\"It was nice speaking to you\",\"See you later\",\"Speak Soon\", \"Bubye\"]\n",
        "        },\n",
        " ]}\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import json\n",
        "import string\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "nltk.download(\"punkt\")\n",
        "nltk.download(\"wordnet\")\n",
        "#Lemmatization\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "words =[]\n",
        "classes=[]\n",
        "doc_x=[]\n",
        "doc_y=[]\n",
        "#Tokenization\n",
        "for intent in data[\"intents\"]:\n",
        "  for pattern in intent[\"patterns\"]:\n",
        "    tokens = nltk.word_tokenize(pattern)\n",
        "    words.extend(tokens)\n",
        "    doc_x.append(pattern)\n",
        "    doc_y.append(intent[\"tag\"])\n",
        "  if intent[\"tag\"] not in classes:\n",
        "      classes.append(intent[\"tag\"])\n",
        "#Converting to lowercase\n",
        "words = [lemmatizer.lemmatize(word.lower()) for word in words if word not in string.punctuation]\n",
        "#Sorting in alphabetic order and removing redundancy\n",
        "words = sorted(set(words))\n",
        "classes = sorted(set(classes))\n",
        "#Training data list\n",
        "training = []\n",
        "out_empty = [0]*len(classes)\n",
        "#Creating Bag of Words\n",
        "for idx, doc in enumerate(doc_x):\n",
        "    bow=[]\n",
        "    text=lemmatizer.lemmatize(doc.lower())\n",
        "    for word in words:\n",
        "        bow.append(1) if word in text else bow.append(0)\n",
        "    output_row=list(out_empty)\n",
        "    output_row[classes.index(doc_y[idx])]=1\n",
        "\n",
        "    training.append([bow, output_row])\n",
        "\n",
        "random.shuffle(training)\n",
        "train = np.array(training, dtype = object)\n",
        "train_x = np.array(list(train[:,0]))\n",
        "train_y = np.array(list(train[:,1]))\n",
        "#Model for prediction\n",
        "input_shape = (len(train_x[0]),)\n",
        "output_shape = len(train_y[0])\n",
        "epochs = 500\n",
        "#Creating sequential model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "model = Sequential()\n",
        "model.add(Dense(128, input_shape=input_shape, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation ='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(output_shape, activation = 'softmax'))\n",
        "#Creating ADAM (Adaptive moment estimation) optimizer with a given learning rate\n",
        "adam = tf.keras.optimizers.Adam(learning_rate = 0.01)\n",
        "#Compiling training model using ADAM\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer = adam,\n",
        "              metrics =['accuracy'])\n",
        "print (model.summary())\n",
        "#Fitting the model\n",
        "model.fit(x=train_x, y=train_y, epochs=500, verbose=1)\n",
        "#Defining function response to generate reply to user input\n",
        "def clean_text(text):\n",
        "  tokens = nltk.word_tokenize(text)\n",
        "  tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
        "  return tokens\n",
        "\n",
        "def bag_of_words(text, vocab):\n",
        "  tokens = clean_text(text)\n",
        "  bow = [0]*len(vocab)\n",
        "  for w in tokens:\n",
        "    for idx, word in enumerate(vocab):\n",
        "      if word==w:\n",
        "        bow[idx]=1\n",
        "  return np.array(bow)\n",
        "def predict_class(text, vocab, labels):\n",
        "  bow = bag_of_words(text, vocab)\n",
        "  output = model.predict(np.array([bow]))[0]\n",
        "  threshold = 0.2\n",
        "  y_predict = [[idx, res] for idx, res in enumerate(output) if res>threshold]\n",
        "\n",
        "  y_predict.sort(key = lambda x:x[1], reverse = True)\n",
        "  output_list = []\n",
        "  for r in y_predict:\n",
        "    output_list.append(labels[r[0]])\n",
        "  return output_list\n",
        "\n",
        "def get_response (intents_list, intents_json):\n",
        "    tag = intents_list[0]\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",
        "#Running the Chatbot\n",
        "while True:\n",
        "  message = input(\"\")\n",
        "  intents = predict_class(message,words,classes)\n",
        "  result = get_response(intents,data)\n",
        "  print (result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "068NleGrIkeA",
        "outputId": "139d6a87-d40e-4c95-f1c6-eab762892650"
      },
      "execution_count": 2,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 128)               5376      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14022 (54.77 KB)\n",
            "Trainable params: 14022 (54.77 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "None\n",
            "Epoch 1/500\n",
            "1/1 [==============================] - 1s 815ms/step - loss: 1.7869 - accuracy: 0.3478\n",
            "Epoch 2/500\n",
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            "Epoch 3/500\n",
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            "Epoch 7/500\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 1.0691 - accuracy: 0.6957\n",
            "Epoch 9/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.0004 - accuracy: 0.6957\n",
            "Epoch 10/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.8215 - accuracy: 0.7391\n",
            "Epoch 11/500\n",
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            "Epoch 12/500\n",
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            "Epoch 13/500\n",
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            "Epoch 15/500\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 3.7943e-04 - accuracy: 1.0000\n",
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            "1/1 [==============================] - 0s 11ms/step - loss: 7.5505e-04 - accuracy: 1.0000\n",
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            "1/1 [==============================] - 0s 13ms/step - loss: 0.0026 - accuracy: 1.0000\n",
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            "1/1 [==============================] - 0s 14ms/step - loss: 8.4635e-05 - accuracy: 1.0000\n",
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            "Epoch 100/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.2587e-04 - accuracy: 1.0000\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 2.8260e-05 - accuracy: 1.0000\n",
            "Epoch 454/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.2133e-05 - accuracy: 1.0000\n",
            "Epoch 455/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 456/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.1565e-05 - accuracy: 1.0000\n",
            "Epoch 457/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0348 - accuracy: 0.9565\n",
            "Epoch 458/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0109 - accuracy: 1.0000\n",
            "Epoch 459/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.3413e-04 - accuracy: 1.0000\n",
            "Epoch 460/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.0921 - accuracy: 0.9565\n",
            "Epoch 461/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.0320 - accuracy: 1.0000\n",
            "Epoch 462/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.3313e-05 - accuracy: 1.0000\n",
            "Epoch 463/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.0165e-04 - accuracy: 1.0000\n",
            "Epoch 464/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.9707e-06 - accuracy: 1.0000\n",
            "Epoch 465/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.9394e-05 - accuracy: 1.0000\n",
            "Epoch 466/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.3731e-05 - accuracy: 1.0000\n",
            "Epoch 467/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.0060 - accuracy: 1.0000\n",
            "Epoch 468/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.1494e-04 - accuracy: 1.0000\n",
            "Epoch 469/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0013 - accuracy: 1.0000\n",
            "Epoch 470/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.1705e-04 - accuracy: 1.0000\n",
            "Epoch 471/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.1381e-04 - accuracy: 1.0000\n",
            "Epoch 472/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.0037 - accuracy: 1.0000\n",
            "Epoch 473/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.0324 - accuracy: 1.0000\n",
            "Epoch 474/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.0041 - accuracy: 1.0000\n",
            "Epoch 475/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0078 - accuracy: 1.0000\n",
            "Epoch 476/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.6093e-05 - accuracy: 1.0000\n",
            "Epoch 477/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.7329e-04 - accuracy: 1.0000\n",
            "Epoch 478/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0769 - accuracy: 0.9565\n",
            "Epoch 479/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0542 - accuracy: 0.9565\n",
            "Epoch 480/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.5376e-04 - accuracy: 1.0000\n",
            "Epoch 481/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.3221e-05 - accuracy: 1.0000\n",
            "Epoch 482/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2018 - accuracy: 0.9565\n",
            "Epoch 483/500\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 484/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9476e-05 - accuracy: 1.0000\n",
            "Epoch 485/500\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 4.2906e-04 - accuracy: 1.0000\n",
            "Epoch 486/500\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 487/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0466 - accuracy: 0.9565\n",
            "Epoch 488/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2416 - accuracy: 0.9565\n",
            "Epoch 489/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.0437 - accuracy: 1.0000\n",
            "Epoch 490/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 491/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0190 - accuracy: 1.0000\n",
            "Epoch 492/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.7798e-05 - accuracy: 1.0000\n",
            "Epoch 493/500\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 2.5790e-04 - accuracy: 1.0000\n",
            "Epoch 494/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0024 - accuracy: 1.0000\n",
            "Epoch 495/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0014 - accuracy: 1.0000\n",
            "Epoch 496/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9016e-04 - accuracy: 1.0000\n",
            "Epoch 497/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.0026e-05 - accuracy: 1.0000\n",
            "Epoch 498/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 1.0000\n",
            "Epoch 499/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.4427e-05 - accuracy: 1.0000\n",
            "Epoch 500/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.2851e-05 - accuracy: 1.0000\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "Yo!\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "I'm known as Shiro\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "Hello there!\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "My day goes by studing, going to classes and hanging out\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "It was nice speaking to you\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "I'm an adult, aged 23\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "I was born in 2000\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "I spend my time reading fiction novels\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "I am 23\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "I am 23\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "See you later\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Yo!\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "My name is Shiro\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "I'm an adult, aged 23\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "My birth day is 4th June, and year 2000\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "See you later\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "Bubye\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Howdy!\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "I spend my time reading fiction novels\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Yo!\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-a77e785b93b2>\u001b[0m in \u001b[0;36m<cell line: 138>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;31m#Running the Chatbot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m   \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m   \u001b[0mintents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m   \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintents\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    849\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m             )\n\u001b[0;32m--> 851\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    852\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ]
    }
  ]
}