{
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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WaYYbq814jEh"
      },
      "outputs": [],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Embedding\n"
      ],
      "metadata": {
        "id": "m1lkAOKh4nc_"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alphabet = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n",
        "\n",
        "# Convert alphabet to integers\n",
        "char_to_int = dict((c, i) for i, c in enumerate(alphabet))\n",
        "int_to_char = dict((i, c) for i, c in enumerate(alphabet))\n",
        "\n",
        "# Prepare dataset\n",
        "seq_length = 1\n",
        "dataX = []\n",
        "dataY = []\n",
        "for i in range(0, len(alphabet) - seq_length, 1):\n",
        "    seq_in = alphabet[i:i + seq_length]\n",
        "    seq_out = alphabet[i + seq_length]\n",
        "    dataX.append([char_to_int[char] for char in seq_in])\n",
        "    dataY.append(char_to_int[seq_out])\n",
        "\n",
        "X = np.reshape(dataX, (len(dataX), seq_length, 1))\n",
        "y = tf.keras.utils.to_categorical(dataY)\n"
      ],
      "metadata": {
        "id": "kjFJxMNV4oPv"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2])))\n",
        "model.add(Dense(y.shape[1], activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam')\n"
      ],
      "metadata": {
        "id": "P9e2hWnD4pFY"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X, y, epochs=500, batch_size=1, verbose=2)\n"
      ],
      "metadata": {
        "id": "PO31MxKH4qGb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for pattern in dataX:\n",
        "    x = np.reshape(pattern, (1, len(pattern), 1))\n",
        "    prediction = model.predict(x, verbose=0)\n",
        "    index = np.argmax(prediction)\n",
        "    result = int_to_char[index]\n",
        "    seq_in = [int_to_char[value] for value in pattern]\n",
        "    print(seq_in, \"->\", result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bozl3EuF4q8k",
        "outputId": "2cc54eed-8af5-4f06-d2c5-79d3ea2380f3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A'] -> B\n",
            "['B'] -> C\n",
            "['C'] -> D\n",
            "['D'] -> E\n",
            "['E'] -> F\n",
            "['F'] -> G\n",
            "['G'] -> H\n",
            "['H'] -> I\n",
            "['I'] -> J\n",
            "['J'] -> K\n",
            "['K'] -> L\n",
            "['L'] -> M\n",
            "['M'] -> N\n",
            "['N'] -> O\n",
            "['O'] -> O\n",
            "['P'] -> P\n",
            "['Q'] -> R\n",
            "['R'] -> T\n",
            "['S'] -> T\n",
            "['T'] -> V\n",
            "['U'] -> V\n",
            "['V'] -> X\n",
            "['W'] -> Z\n",
            "['X'] -> Z\n",
            "['Y'] -> Z\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('alphabet_model.h5')\n",
        "from google.colab import files\n",
        "files.download('alphabet_model.h5')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "FUCsjNyY8ZCs",
        "outputId": "496fd160-13f1-4af1-9ae3-ea6c89513adb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_10038af2-1923-4b7b-afba-d12d0f08f61e\", \"alphabet_model.h5\", 91488)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}