STP_Machathon_2_0_Chatbot_Message_Classification.ipynb

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      "name": "STP-Machathon-2.0-Chatbot_Message_Classification",
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      "source": [
        "# Chatbot Message Classification Project\n",
        "\n",
        "## Table of Content\n",
        "<ul>\n",
        "    <li><a href=\"#sec1\">Installing the nescessery Libraries.</a></li>\n",
        "    <li><a href=\"#sec2\">Data Wrangling.</a></li>\n",
        "    <li><a href=\"#sec3\">Pre-processing.</a></li>\n",
        "    <li><a href=\"#sec4\">Deep Learning Model.</a></li>\n",
        "    <li><a href=\"#sec5\">Model Traning.</a></li>\n",
        "    <li><a href=\"#sec6\">Pre-processing for the test data.</a></li>\n",
        "    <li><a href=\"#sec7\">Evaluate on the splitted test data.</a></li>\n",
        "</ul>"
      ],
      "metadata": {
        "id": "DXj_r-k7OIJh"
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    {
      "cell_type": "markdown",
      "source": [
        "<a id='sec1'></a>\n",
        "## Installing the nescessery Libraries."
      ],
      "metadata": {
        "id": "wCh8rZyEPmhO"
      }
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          "text": [
            "Collecting pyarabic\n",
            "  Downloading PyArabic-0.6.14-py3-none-any.whl (126 kB)\n",
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            "\u001b[?25hRequirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from pyarabic) (1.15.0)\n",
            "Installing collected packages: pyarabic\n",
            "Successfully installed pyarabic-0.6.14\n"
          ]
        }
      ],
      "source": [
        "pip install pyarabic"
      ]
    },
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        "pip install qalsadi"
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          "text": [
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            "\u001b[?25hRequirement already satisfied: pyarabic>=0.6.7 in /usr/local/lib/python3.7/dist-packages (from qalsadi) (0.6.14)\n",
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            "\u001b[?25hBuilding wheels for collected packages: pickledb\n",
            "  Building wheel for pickledb (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pickledb: filename=pickleDB-0.9.2-py3-none-any.whl size=4271 sha256=ef7ab5a748ae0aa8aa5b221243f16bb5ee3632ead4de0e192193d91db5e389cf\n",
            "  Stored in directory: /root/.cache/pip/wheels/08/34/42/9a7f94099208ce3d32638d98586a5a50f821db2fc75a3bdaae\n",
            "Successfully built pickledb\n",
            "Installing collected packages: tashaphyne, libqutrub, pickledb, naftawayh, arramooz-pysqlite, Arabic-Stopwords, alyahmor, qalsadi\n",
            "Successfully installed Arabic-Stopwords-0.3 alyahmor-0.1.2 arramooz-pysqlite-0.3 libqutrub-1.2.4.1 naftawayh-0.4 pickledb-0.9.2 qalsadi-0.4.4 tashaphyne-0.3.6\n"
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      "source": [
        "<a id='sec2'></a>\n",
        "## Data Wrangling"
      ],
      "metadata": {
        "id": "ha_0uxzuPxGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pyarabic.araby as araby\n",
        "\n",
        "import random\n",
        "import json\n",
        "import pickle\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input, Bidirectional, GRU\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras.models import load_model"
      ],
      "metadata": {
        "id": "Ifld2HONLwcV"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import qalsadi.lemmatizer\n",
        "\n",
        "lemmer = qalsadi.lemmatizer.Lemmatizer()"
      ],
      "metadata": {
        "id": "ckdxFF5pLymk"
      },
      "execution_count": 4,
      "outputs": []
    },
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      "source": [
        "df = pd.read_csv(r\"train_ara.csv\")\n",
        "df.head()"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>intent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>كم عدد مستشفيات العزل فى مصر وما هى اماكنها</td>\n",
              "      <td>business location</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ومخاصمك</td>\n",
              "      <td>nothing identified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>متي ينتهي كورونا؟</td>\n",
              "      <td>the evolution of the virus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>انا اسف</td>\n",
              "      <td>nothing identified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>كام عدد الوفيات النهارده</td>\n",
              "      <td>infected cases</td>\n",
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              "                                          text                      intent\n",
              "0  كم عدد مستشفيات العزل فى مصر وما هى اماكنها           business location\n",
              "1                                      ومخاصمك          nothing identified\n",
              "2                            متي ينتهي كورونا؟  the evolution of the virus\n",
              "3                                      انا اسف          nothing identified\n",
              "4                     كام عدد الوفيات النهارده              infected cases"
            ]
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          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "path = r\"list.txt\"\n",
        "stop_words = []\n",
        "with open(path, \"r\", encoding=\"utf-8\", errors=\"ignore\") as myfile:\n",
        "    stop_words = myfile.readlines()\n",
        "stopWords = [word.strip() for word in stop_words]"
      ],
      "metadata": {
        "id": "lSxRQc2BL8Hj"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a id='sec3'></a>\n",
        "## Pre-processing"
      ],
      "metadata": {
        "id": "bQk0aYxAP7tZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "words = []\n",
        "classes = []\n",
        "documents = []\n",
        "ignore_letters = ['!', '?', ',', '.']\n",
        "\n",
        "for i in range (df['text'].count()):\n",
        "    word = araby.tokenize(df['text'][i])\n",
        "    words.extend(word)\n",
        "    documents.append((word, df['intent'][i]))\n",
        "    if df['intent'][i] not in classes:\n",
        "        classes.append(df['intent'][i])"
      ],
      "metadata": {
        "id": "BN5qfv0fMVTc"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = [lemmer.lemmatize(w.lower()) for w in words if w not in ignore_letters and w not in stopWords]\n",
        "words = sorted(list(set(words)))\n",
        "\n",
        "classes = sorted(list(set(classes)))"
      ],
      "metadata": {
        "id": "ouWVaKiUMjlm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training = []\n",
        "output_empty = [0] * len(classes)\n",
        "\n",
        "for doc in documents:\n",
        "    bag = []\n",
        "    word_patterns = doc[0]\n",
        "    word_patterns = [lemmer.lemmatize(word.lower()) for word in word_patterns]\n",
        "    for word in words:\n",
        "        bag.append(1) if word in word_patterns else bag.append(0)\n",
        "\n",
        "        output_row = list(output_empty)\n",
        "        output_row[classes.index(doc[1])] = 1\n",
        "        training.append([bag, output_row])\n",
        "\n",
        "random.shuffle(training)\n",
        "training = np.array(training)\n",
        "\n",
        "train_x = list(training[:, 0])\n",
        "train_y = list(training[:, 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKh8VYK3Mshy",
        "outputId": "292f9072-0347-48bf-b39c-20894a51c604"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  app.launch_new_instance()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size = 0.2, random_state = 42, shuffle=True)"
      ],
      "metadata": {
        "id": "vF-EfiW2MxwQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a id='sec4'></a>\n",
        "## Deep Learning Model"
      ],
      "metadata": {
        "id": "OFqFbv4oQBG9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "\n",
        "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ZBPU_wSYNN3a",
        "outputId": "28fca174-6f84-42d6-8177-0c2de34769b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "WcyBotJFNRTR",
        "outputId": "3b5c3fda-152e-487e-9dae-d4d5a0bbd8ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 128)               125440    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 40)                2600      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 136,296\n",
            "Trainable params: 136,296\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a id='sec5'></a>\n",
        "## Model Training "
      ],
      "metadata": {
        "id": "jqTQVaeFQGIS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(np.array(X_train), np.array(y_train), epochs=3, batch_size=5, verbose=2)"
      ],
      "metadata": {
        "id": "5HIehtvmNTe8",
        "outputId": "e4e319f4-dc82-475c-a451-ceb3adba85c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "123903/123903 - 263s - loss: 0.1467 - accuracy: 0.9607 - 263s/epoch - 2ms/step\n",
            "Epoch 2/3\n",
            "123903/123903 - 236s - loss: 0.1875 - accuracy: 0.9638 - 236s/epoch - 2ms/step\n",
            "Epoch 3/3\n",
            "123903/123903 - 251s - loss: 0.2750 - accuracy: 0.9506 - 251s/epoch - 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6dd4347890>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a id='sec6'></a>\n",
        "## Pre-processing for the test data"
      ],
      "metadata": {
        "id": "4H6nn054NpiJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def _clean_up_sentence(sentence):\n",
        "    sentence_words = araby.tokenize(sentence)\n",
        "    sentence_words = [lemmer.lemmatize(word.lower()) for word in sentence_words]\n",
        "    return sentence_words"
      ],
      "metadata": {
        "id": "337vHiBdNYHw"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def _bag_of_words(sentence, words):\n",
        "    sentence_words = _clean_up_sentence(sentence)\n",
        "    bag = [0] * len(words)\n",
        "    for s in sentence_words:\n",
        "        for i, word in enumerate(words):\n",
        "            if word == s:\n",
        "                bag[i] = 1\n",
        "    return np.array(bag)"
      ],
      "metadata": {
        "id": "Mm25vId9Njzv"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def _predict_class(sentence):\n",
        "    p = _bag_of_words(sentence, words)\n",
        "    res = model.predict(np.array([p]))[0]\n",
        "    ERROR_THRESHOLD = 0.1\n",
        "    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]\n",
        "    \n",
        "    results.sort(key=lambda x: x[1], reverse=True)\n",
        "    return_list = \"\"\n",
        "    for r in results:\n",
        "        return_list = classes[r[0]]\n",
        "    return return_list"
      ],
      "metadata": {
        "id": "ZEBa_--2Nk3s"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### importing and predecting on the test data"
      ],
      "metadata": {
        "id": "8hsd2pBHNuLV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfTest = pd.read_csv(r\"test.csv\")\n",
        "output = []\n",
        "for i in range(dfTest['text'].count()):\n",
        "    x = _predict_class(dfTest['text'][i])\n",
        "    output.append(x)\n",
        "output[:10]"
      ],
      "metadata": {
        "id": "NhSrh5VENmB5",
        "outputId": "132fc50c-aa43-4ea5-d7a8-3f3f81ab4ce3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['infected cases',\n",
              " 'infected cases',\n",
              " 'yes',\n",
              " 'treatment',\n",
              " 'infected cases',\n",
              " 'infected cases',\n",
              " 'infected cases',\n",
              " 'infected cases',\n",
              " 'the evolution of the virus',\n",
              " 'infected cases']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a id='sec7'></a>\n",
        "## Evaluate on the splitted test data"
      ],
      "metadata": {
        "id": "NU0EAHJRRmgK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(np.array(X_test), np.array(y_test))"
      ],
      "metadata": {
        "id": "M3kgcaFfOBJF",
        "outputId": "fc7f51a5-1244-4fa4-aef4-7399155f869f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4840/4840 [==============================] - 16s 3ms/step - loss: 0.0494 - accuracy: 0.9862\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.04943576455116272, 0.9862343072891235]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "McfHdmOeRZYu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}