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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KizMan-23/recommendations/blob/main/recommender_on_transformer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WidMZqHlHn-A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "d739bf58-2914-4414-8e12-704a4f32ad13"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-070b792f-501e-4a39-a0ad-c4bf59f770b2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-070b792f-501e-4a39-a0ad-c4bf59f770b2\">\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": [
            "Saving movies.csv to movies.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "upload = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import zipfile\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import regex as re\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "TLiZk8GlIOmf"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies = pd.read_csv('movies.csv')"
      ],
      "metadata": {
        "id": "Jd3fI1oqI0vi"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "49UnhH0cI5AR",
        "outputId": "7a8e7623-939a-469a-bbb2-1c650fa4ff2d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "5        6                         Heat (1995)   \n",
              "6        7                      Sabrina (1995)   \n",
              "7        8                 Tom and Huck (1995)   \n",
              "8        9                 Sudden Death (1995)   \n",
              "9       10                    GoldenEye (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  \n",
              "5                        Action|Crime|Thriller  \n",
              "6                               Comedy|Romance  \n",
              "7                           Adventure|Children  \n",
              "8                                       Action  \n",
              "9                    Action|Adventure|Thriller  "
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movies",
              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 62423,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63264,\n        \"min\": 1,\n        \"max\": 209171,\n        \"num_unique_values\": 62423,\n        \"samples\": [\n          4990,\n          116698,\n          125517\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 62325,\n        \"samples\": [\n          \"So Dear to My Heart (1949)\",\n          \"Give Me Liberty (2019)\",\n          \"Vice (2015)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1639,\n        \"samples\": [\n          \"Action|Adventure|Crime|Western\",\n          \"Action|Comedy|Drama|Romance|Thriller\",\n          \"Animation|Documentary|Drama|Fantasy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    return re.sub(r'[^a-zA-Z0-9 ]', \"\", text.lower())"
      ],
      "metadata": {
        "id": "8wMA5xrnJE8_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies['clean_title'] = movies['title'].apply(clean_text)\n",
        "\n",
        "movies['tags'] = movies['clean_title'] +\" \" + movies['genres'].apply(clean_text)"
      ],
      "metadata": {
        "id": "lc_QFqsWJJ2A"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWozaJscJQhS",
        "outputId": "a4bb130f-1372-4c6a-b132-62ae3e11dce3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.3.1-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.46.3)\n",
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            "Requirement already satisfied: huggingface-hub>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.26.5)\n",
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            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.13.1)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2024.9.11)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2024.8.30)\n",
            "Downloading sentence_transformers-3.3.1-py3-none-any.whl (268 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers\n",
            "  Attempting uninstall: sentence-transformers\n",
            "    Found existing installation: sentence-transformers 3.2.1\n",
            "    Uninstalling sentence-transformers-3.2.1:\n",
            "      Successfully uninstalled sentence-transformers-3.2.1\n",
            "Successfully installed sentence-transformers-3.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hugging Face Sentence-Transformer\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500,
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        },
        "id": "kf0J0nntJSUH",
        "outputId": "f676d7c7-2892-4317-cf0d-a9b874e491a9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
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              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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            ],
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          "metadata": {}
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        {
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tags_embeddings = model.encode(movies['tags'].tolist())"
      ],
      "metadata": {
        "id": "lVKTbdMMJSIA"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "title_embeddings = model.encode(movies['clean_title'].tolist())"
      ],
      "metadata": {
        "id": "Jpw-PsVYKkv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_recommendations(title):\n",
        "  title = clean_text(query, embeddings, df, top_n =7)\n",
        "  query_embedding = model.encode([title])\n",
        "  similarities = cosine_similarity(query_embedding, embeddings)\n",
        "  top_indices = similarities.argsort()[0][-top_n:][::-1]\n",
        "  return df.iloc[top_indices]\n"
      ],
      "metadata": {
        "id": "R73Fm44IJ_TT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"Harry Potter\"\n",
        "recommendations = get_recommendations(query, title_embeddings, movies)\n",
        "print(recommendations)"
      ],
      "metadata": {
        "id": "zlQGZTU9KB0M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wmvDZsJ6Lx32"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9QymO3ofLyUS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "import torch\n",
        "import torch.nn.functional as F"
      ],
      "metadata": {
        "id": "laa4FgdwKBpS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_pooling(model_output, attention_mask):\n",
        "    token_embeddings = model_output[0]\n",
        "    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddigs.size()).float()\n",
        "    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min = 1e-9)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "model = AutoModel.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "\n",
        "def get_embeddings(sentences):\n",
        "    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n",
        "\n",
        "    with torch.no_grad():\n",
        "        model_output = model(**encoded_input)\n",
        "\n",
        "    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n",
        "    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)\n",
        "\n",
        "    return sentence_embeddings"
      ],
      "metadata": {
        "id": "4wDvDvMjKIZf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}