Assignment2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssDoGNdO885G",
        "outputId": "ea952f88-aae2-49d2-8081-c58efd3e7168"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data:\n",
            "        atis_flight  \\\n",
            "0       atis_flight   \n",
            "1  atis_flight_time   \n",
            "2      atis_airfare   \n",
            "3      atis_airfare   \n",
            "4       atis_flight   \n",
            "\n",
            "   i want to fly from boston at 838 am and arrive in denver at 1110 in the morning  \n",
            "0   what flights are available from pittsburgh to...                                \n",
            "1   what is the arrival time in san francisco for...                                \n",
            "2            cheapest airfare from tacoma to orlando                                \n",
            "3   round trip fares from pittsburgh to philadelp...                                \n",
            "4   i need a flight tomorrow from columbus to min...                                \n",
            "\n",
            "Testing Data:\n",
            "    atis_flight  \\\n",
            "0  atis_airfare   \n",
            "1   atis_flight   \n",
            "2   atis_flight   \n",
            "3   atis_flight   \n",
            "4   atis_flight   \n",
            "\n",
            "   i would like to find a flight from charlotte to las vegas that makes a stop in st. louis  \n",
            "0   on april first i need a ticket from tacoma to...                                         \n",
            "1   on april first i need a flight going from pho...                                         \n",
            "2   i would like a flight traveling one way from ...                                         \n",
            "3   i would like a flight from orlando to salt la...                                         \n",
            "4   i need a flight from toronto to newark one wa...                                         \n"
          ]
        }
      ],
      "source": [
        "# Import required libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Load the dataset files\n",
        "train_data = pd.read_csv('/content/drive/MyDrive/IDL/Assignment#2/atis_intents_train.csv')\n",
        "test_data = pd.read_csv('/content/drive/MyDrive/IDL/Assignment#2/atis_intents_test.csv')\n",
        "full_data = pd.read_csv('/content/drive/MyDrive/IDL/Assignment#2/atis_intents.csv')\n",
        "\n",
        "# Display the first few rows of each dataset to check the structure\n",
        "print(\"Training Data:\")\n",
        "print(train_data.head())\n",
        "\n",
        "print(\"\\nTesting Data:\")\n",
        "print(test_data.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display column names in the training and testing datasets\n",
        "print(\"Training Data Columns:\", train_data.columns)\n",
        "print(\"Testing Data Columns:\", test_data.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mTat55eKzyo",
        "outputId": "116446bd-74a1-4536-d3dc-283c327f2461"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Columns: Index(['atis_flight', ' i want to fly from boston at 838 am and arrive in denver at 1110 in the morning'], dtype='object')\n",
            "Testing Data Columns: Index(['atis_flight', ' i would like to find a flight from charlotte to las vegas that makes a stop in st. louis'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Extract columns explicitly\n",
        "X_train = train_data.iloc[:, 1]  # Queries (2nd column, index 1)\n",
        "y_train = train_data.iloc[:, 0]  # Intents (1st column, index 0)\n",
        "\n",
        "X_test = test_data.iloc[:, 1]  # Queries (2nd column, index 1)\n",
        "y_test = test_data.iloc[:, 0]  # Intents (1st column, index 0)\n",
        "\n",
        "# Label encoding for intents\n",
        "label_encoder = LabelEncoder()\n",
        "y_train = label_encoder.fit_transform(y_train)\n",
        "y_test = label_encoder.transform(y_test)\n",
        "\n",
        "# Tokenize and convert text to sequences\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X_train)  # Fit tokenizer on training data only\n",
        "\n",
        "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
        "X_test_seq = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "# Pad sequences for uniform input size\n",
        "X_train_pad = pad_sequences(X_train_seq, padding='post')\n",
        "X_test_pad = pad_sequences(X_test_seq, padding='post')\n",
        "\n",
        "# Display shapes\n",
        "print(f\"Training data shape: {X_train_pad.shape}\")\n",
        "print(f\"Test data shape: {X_test_pad.shape}\")\n",
        "print(f\"Number of unique intents: {len(label_encoder.classes_)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Org9TnAgIuvT",
        "outputId": "a2378153-3119-4571-c1eb-7cc29ac5e389"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (4833, 46)\n",
            "Test data shape: (799, 30)\n",
            "Number of unique intents: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional\n",
        "\n",
        "# Define model parameters\n",
        "vocab_size = len(tokenizer.word_index) + 1  # Total vocabulary size (+1 for padding token)\n",
        "max_len = X_train_pad.shape[1]  # Sequence length from padded data\n",
        "num_classes = len(label_encoder.classes_)  # Number of unique intents\n",
        "\n",
        "# Verify parameters\n",
        "print(f\"Vocab Size: {vocab_size}, Max Sequence Length: {max_len}, Number of Classes: {num_classes}\")\n",
        "\n",
        "# Build the model\n",
        "model = Sequential([\n",
        "    Embedding(input_dim=vocab_size, output_dim=128, input_length=max_len),\n",
        "    Bidirectional(LSTM(64, return_sequences=True)),\n",
        "    Dropout(0.5),\n",
        "    Bidirectional(LSTM(32)),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(num_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.build(input_shape=(None, max_len))  # Explicitly define the input shape\n",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# Display the model summary\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "hNghblPsK6Cv",
        "outputId": "9e4c84ca-45c1-4ac4-c003-9369a1f49fd1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab Size: 872, Max Sequence Length: 46, Number of Classes: 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_2 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │         \u001b[38;5;34m111,616\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │          \u001b[38;5;34m98,816\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m41,216\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │             \u001b[38;5;34m264\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">111,616</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">41,216</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">264</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m253,992\u001b[0m (992.16 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">253,992</span> (992.16 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m253,992\u001b[0m (992.16 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">253,992</span> (992.16 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    X_train_pad,\n",
        "    y_train,\n",
        "    validation_split=0.2,  # 20% of training data for validation\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# Save the model for future use\n",
        "model.save('intent_classification_model.keras')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmdjCmLZMXwu",
        "outputId": "3f9bab00-a572-4919-c320-f7671cf1c5e9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 123ms/step - accuracy: 0.9625 - loss: 0.1218 - val_accuracy: 0.9669 - val_loss: 0.1439\n",
            "Epoch 2/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 124ms/step - accuracy: 0.9651 - loss: 0.0987 - val_accuracy: 0.9659 - val_loss: 0.1602\n",
            "Epoch 3/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 120ms/step - accuracy: 0.9729 - loss: 0.0956 - val_accuracy: 0.9710 - val_loss: 0.1495\n",
            "Epoch 4/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 124ms/step - accuracy: 0.9722 - loss: 0.0966 - val_accuracy: 0.9741 - val_loss: 0.1586\n",
            "Epoch 5/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 122ms/step - accuracy: 0.9758 - loss: 0.0707 - val_accuracy: 0.9752 - val_loss: 0.1711\n",
            "Epoch 6/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 123ms/step - accuracy: 0.9724 - loss: 0.0982 - val_accuracy: 0.9741 - val_loss: 0.1648\n",
            "Epoch 7/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 122ms/step - accuracy: 0.9839 - loss: 0.0538 - val_accuracy: 0.9772 - val_loss: 0.2001\n",
            "Epoch 8/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 124ms/step - accuracy: 0.9809 - loss: 0.0631 - val_accuracy: 0.9804 - val_loss: 0.1873\n",
            "Epoch 9/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 124ms/step - accuracy: 0.9850 - loss: 0.0683 - val_accuracy: 0.9772 - val_loss: 0.1642\n",
            "Epoch 10/10\n",
            "\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 124ms/step - accuracy: 0.9877 - loss: 0.0457 - val_accuracy: 0.9824 - val_loss: 0.1645\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Predict on test data\n",
        "y_pred = model.predict(X_test_pad)\n",
        "y_pred_classes = y_pred.argmax(axis=1)  # Get predicted class indices\n",
        "\n",
        "# Calculate test accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred_classes)\n",
        "print(f\"Test Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# Generate classification report\n",
        "report = classification_report(\n",
        "    y_test,\n",
        "    y_pred_classes,\n",
        "    target_names=label_encoder.classes_\n",
        ")\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKolR-KvNPl3",
        "outputId": "ce26951a-ac46-4b63-cdea-eecdf5fc53b3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "Test Accuracy: 0.97\n",
            "                     precision    recall  f1-score   support\n",
            "\n",
            "  atis_abbreviation       0.94      1.00      0.97        33\n",
            "      atis_aircraft       0.80      0.89      0.84         9\n",
            "       atis_airfare       0.90      0.90      0.90        48\n",
            "       atis_airline       1.00      0.97      0.99        38\n",
            "        atis_flight       0.99      0.98      0.99       631\n",
            "   atis_flight_time       0.50      1.00      0.67         1\n",
            "atis_ground_service       1.00      1.00      1.00        36\n",
            "      atis_quantity       0.38      1.00      0.55         3\n",
            "\n",
            "           accuracy                           0.97       799\n",
            "          macro avg       0.81      0.97      0.86       799\n",
            "       weighted avg       0.98      0.97      0.98       799\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_intent(query):\n",
        "    # Preprocess the query\n",
        "    seq = tokenizer.texts_to_sequences([query])\n",
        "    pad_seq = pad_sequences(seq, maxlen=max_len, padding='post')\n",
        "\n",
        "    # Predict intent\n",
        "    prediction = model.predict(pad_seq)\n",
        "    predicted_class = prediction.argmax(axis=1)[0]\n",
        "    intent = label_encoder.inverse_transform([predicted_class])[0]\n",
        "\n",
        "    return intent\n",
        "\n",
        "# Example usage\n",
        "user_query = \"show me flights from New York to Los Angeles\"\n",
        "predicted_intent = predict_intent(user_query)\n",
        "print(f\"Predicted Intent: {predicted_intent}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sF0fcXmXNRDa",
        "outputId": "fe945438-649d-46b0-ab62-1b51ad2ec70a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
            "Predicted Intent: atis_flight\n"
          ]
        }
      ]
    }
  ]
}