chatbotBasedOnIntents.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.upload_file('intents.json')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "6rLpvqj7OUma",
        "outputId": "3a55f54e-927f-4996-9e08-99e2f9d2ee2d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4ad19274-d74b-4962-ac16-e3602e8dee23\" name=\"files[]\"  disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-4ad19274-d74b-4962-ac16-e3602e8dee23\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved intents.json to /content/intents.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hk0hgcv56aF6",
        "outputId": "01a8ea5c-ce13-4718-d882-3b877c38eda5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tflearn\n",
            "  Downloading tflearn-0.5.0.tar.gz (107 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.3/107.3 KB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from tflearn) (1.22.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from tflearn) (1.15.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.9/dist-packages (from tflearn) (8.4.0)\n",
            "Building wheels for collected packages: tflearn\n",
            "  Building wheel for tflearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tflearn: filename=tflearn-0.5.0-py3-none-any.whl size=127299 sha256=a1e0708d08d9ac5dce9ad04b7ad9ba0a58e7e8a56c26e03c2bce4494f6880da5\n",
            "  Stored in directory: /root/.cache/pip/wheels/4a/d5/f8/9585b4a100c0fd73da204ee785457d67c85e1b9050f009a849\n",
            "Successfully built tflearn\n",
            "Installing collected packages: tflearn\n",
            "Successfully installed tflearn-0.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tflearn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9Sim35NwFwb",
        "outputId": "9eb49d96-0219-45bc-b2dd-3c607b15b661"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tflearn\n",
        "import tensorflow\n",
        "import random\n",
        "import json\n",
        "import pickle"
      ],
      "metadata": {
        "id": "MkO7x_yxv-Ee"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.lancaster import LancasterStemmer\n",
        "stem = LancasterStemmer() # stem of words instance"
      ],
      "metadata": {
        "id": "OmNHDssWNfjh"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"/content/intents.json\") as file:\n",
        "\tdata = json.load(file)"
      ],
      "metadata": {
        "id": "qEQYfr84TMxA"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EFsVoXab6LV",
        "outputId": "eb8fd300-9ca6-40fd-ef9f-cf46ad121169"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'intents': [{'tag': 'bienvenida', 'patterns': ['hola', 'que tal', 'como estas'], 'responses': ['hola tu', 'ando bien']}, {'tag': 'tutorial', 'patterns': ['necesito ayuda', 'como empiezo?'], 'responses': ['hola, este es un tutorial', 'epale ya te ayudo']}, {'tag': 'adios', 'patterns': ['ya es tarde, me voy', 'bai, te escribo luego'], 'responses': ['bai bai']}, {'tag': 'lectura', 'patterns': ['estoy leyendo sobre bukouswy', 'ando leyendo'], 'responses': ['que lees?', 'suena interasante']}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words = []\n",
        "labels = []\n",
        "X = []\n",
        "Y = []"
      ],
      "metadata": {
        "id": "mDggfflWOJjS"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for intent in data[\"intents\"]:\n",
        "    for pattern in intent[\"patterns\"]:\n",
        "      words_aux = nltk.word_tokenize(pattern)\n",
        "      words.extend(words_aux)\n",
        "      X.append(words_aux)\n",
        "      Y.append(intent[\"tag\"])\n",
        "\n",
        "    if intent[\"tag\"] not in labels:\n",
        "      labels.append(intent[\"tag\"])\n"
      ],
      "metadata": {
        "id": "vkSzP1e4SQ1n"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = [stem.stem(w.lower()) for w in words if w != '?']\n",
        "words = sorted(list(set(words)))\n",
        "\n",
        "print(words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTapTNpbTPdK",
        "outputId": "3dbf01be-b1ae-419d-b875-ed9caa040993"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[',', 'ando', 'ayud', 'bai', 'bukouswy', 'como', 'empiezo', 'es', 'escribo', 'esta', 'estoy', 'hol', 'leyendo', 'luego', 'me', 'necesito', 'que', 'sobr', 'tal', 'tard', 'te', 'voy', 'ya']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = sorted(labels)\n",
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63UWvrQcTmDv",
        "outputId": "3f34802e-5caf-4ecb-b62c-d12bfc2b903f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['adios', 'bienvenida', 'lectura', 'tutorial']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training = []\n",
        "output = []"
      ],
      "metadata": {
        "id": "y90qa5bETtwu"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out_empty = [0 for _ in range(len(labels))]\n",
        "print(out_empty)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QacTpWV0TyDb",
        "outputId": "4b8bbbfb-f841-47d7-a4e3-26694cee6567"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x, pattern in enumerate(X):\n",
        "    bag = []\n",
        "\n",
        "    wrds = [stem.stem(w) for w in pattern]\n",
        "\n",
        "    for w in words:\n",
        "      if w in wrds:\n",
        "        bag.append(1)\n",
        "      else:\n",
        "        bag.append(0)\n",
        "    \n",
        "    output_row = out_empty[:]\n",
        "    output_row[labels.index(Y[x])] = 1\n",
        "\n",
        "    training.append(bag)\n",
        "    output.append(output_row)"
      ],
      "metadata": {
        "id": "9f-4A6vyT-s3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training = np.array(training)\n",
        "output = np.array(output)\n",
        "\n",
        "print(training.shape, output.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5iFgvu_Vddp",
        "outputId": "458d63d8-a64a-415a-d458-875c0bc71fa7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9, 23) (9, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensorflow.compat.v1.reset_default_graph()"
      ],
      "metadata": {
        "id": "cNglp1fZVihM"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net = tflearn.input_data(shape=[None, len(training[0])])\n",
        "net = tflearn.fully_connected(net, 8)\n",
        "net = tflearn.fully_connected(net, 8)\n",
        "net = tflearn.fully_connected(net, len(output[0]), activation=\"softmax\")\n",
        "net = tflearn.regression(net)\n",
        "\n",
        "model = tflearn.DNN(net)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ro6CV6fVokp",
        "outputId": "ff1fb315-879f-40d2-8106-1e1d4b0d6f48"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.9/dist-packages/tflearn/initializations.py:164: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "\tmodel.load(\"model.tflearn\")\n",
        "except:\n",
        "\tmodel = tflearn.DNN(net)\n",
        "\tmodel.fit(training, output, n_epoch=1000, batch_size=32, show_metric=True)\n",
        "\tmodel.save(\"model.tflearn\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCex1qTmWWkz",
        "outputId": "8babc100-821a-47cb-dfdd-6d3b0a9fc1bc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------\n",
            "Run id: 4CX4Y2\n",
            "Log directory: /tmp/tflearn_logs/\n",
            "---------------------------------\n",
            "Training samples: 9\n",
            "Validation samples: 0\n",
            "--\n",
            "Training Step: 1  | time: 3.251s\n",
            "| Adam | epoch: 001 | loss: 0.00000 - acc: 0.0000 -- iter: 9/9\n",
            "--\n",
            "Training Step: 2  | time: 0.007s\n",
            "| Adam | epoch: 002 | loss: 0.00000 - acc: 0.2000 -- iter: 9/9\n",
            "--\n",
            "Training Step: 3  | time: 0.003s\n",
            "| Adam | epoch: 003 | loss: 0.00000 - acc: 0.3091 -- iter: 9/9\n",
            "--\n",
            "Training Step: 4  | time: 0.005s\n",
            "| Adam | epoch: 004 | loss: 0.00000 - acc: 0.3273 -- iter: 9/9\n",
            "--\n",
            "Training Step: 5  | time: 0.005s\n",
            "| Adam | epoch: 005 | loss: 0.00000 - acc: 0.3315 -- iter: 9/9\n",
            "--\n",
            "Training Step: 6  | time: 0.005s\n",
            "| Adam | epoch: 006 | loss: 0.00000 - acc: 0.3327 -- iter: 9/9\n",
            "--\n",
            "Training Step: 7  | time: 0.006s\n",
            "| Adam | epoch: 007 | loss: 0.00000 - acc: 0.3331 -- iter: 9/9\n",
            "--\n",
            "Training Step: 8  | time: 0.005s\n",
            "| Adam | epoch: 008 | loss: 0.00000 - acc: 0.3332 -- iter: 9/9\n",
            "--\n",
            "Training Step: 9  | time: 0.005s\n",
            "| Adam | epoch: 009 | loss: 0.00000 - acc: 0.3333 -- iter: 9/9\n",
            "--\n",
            "Training Step: 10  | time: 0.004s\n",
            "| Adam | epoch: 010 | loss: 0.00000 - acc: 0.3333 -- iter: 9/9\n",
            "--\n",
            "Training Step: 11  | time: 0.005s\n",
            "| Adam | epoch: 011 | loss: 0.00000 - acc: 0.3333 -- iter: 9/9\n",
            "--\n",
            "Training Step: 12  | time: 0.003s\n",
            "| Adam | epoch: 012 | loss: 0.00000 - acc: 0.3333 -- iter: 9/9\n",
            "--\n",
            "Training Step: 13  | time: 0.016s\n",
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            "--\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_bag_of_words(inp, words):\n",
        "  bag = [0 for _ in range(len(words))]\n",
        "\n",
        "  word_t = nltk.word_tokenize(inp)\n",
        "  word_t = [stem.stem(word.lower()) for word in word_t]\n",
        "\n",
        "  for se in word_t:\n",
        "    for i, w in enumerate(words):\n",
        "      if w == se:\n",
        "        bag[i] = 1\n",
        "\n",
        "  return np.array(bag)\n",
        "\n"
      ],
      "metadata": {
        "id": "bFq1BExiW0xX"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def chatBot(message):\n",
        "  results = model.predict([create_bag_of_words(message,words)])[0]\n",
        "  results_index = np.argmax(results)\n",
        "  tag = labels[results_index]\n",
        "  if results[results_index] > 0.5:\n",
        "    for tg in data[\"intents\"]:\n",
        "      if tg['tag'] == tag:\n",
        "        responses = tg['responses']\n",
        "    print(random.choice(responses))\n",
        "    print(\"\\n\")\n",
        "  else:\n",
        "    print(\"No entiendo lo que dices sorry\")\n"
      ],
      "metadata": {
        "id": "fScYExIeX-Oz"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chatBot(\"hola amigo mio\")\n",
        "print(create_bag_of_words(\"hola\", words))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_w4zFXwpgL1U",
        "outputId": "15ebe61f-e252-404c-fb79-9cb1ed9581e6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ando bien\n",
            "\n",
            "\n",
            "[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IfI9x79sgO1h"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}