Product_Recommendation_System_BERT.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bpUYnCCEf14M"
      },
      "outputs": [],
      "source": [
        "# Product Recommendation System for e-commerce\n",
        "import argparse\n",
        "import time\n",
        "import sys\n",
        "import warnings\n",
        "import logging"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdB78R0mmgA0",
        "outputId": "74491dc2-7205-4048-e0ce-223563bf797e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.cluster import SpectralClustering\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from joblib import dump, load\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "OMZHVnDGf7Ni"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Performance metrics\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "Swt_wzu2tSME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_text\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import re\n",
        "import tensorflow_hub as hub\n",
        "!pip install tensorflow-gpu\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import tensorflow_text\n",
        "import sklearn.metrics as metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puIWtVIXgyPg",
        "outputId": "39df6e78-dc61-4763-87ac-f34ffcadd57c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "Collecting tensorflow_text\n",
            "  Downloading tensorflow_text-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.5/6.5 MB\u001b[0m \u001b[31m60.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tensorflow-hub>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_text) (0.14.0)\n",
            "Collecting tensorflow<2.14,>=2.13.0 (from tensorflow_text)\n",
            "  Downloading tensorflow-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (524.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m524.1/524.1 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (1.6.3)\n",
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            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (0.4.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (0.2.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (1.56.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (3.8.0)\n",
            "Collecting keras<2.14,>=2.13.1 (from tensorflow<2.14,>=2.13.0->tensorflow_text)\n",
            "  Downloading keras-2.13.1-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m102.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (16.0.0)\n",
            "Requirement already satisfied: numpy<=1.24.3,>=1.22 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (1.22.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (23.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (1.16.0)\n",
            "Collecting tensorboard<2.14,>=2.13 (from tensorflow<2.14,>=2.13.0->tensorflow_text)\n",
            "  Downloading tensorboard-2.13.0-py3-none-any.whl (5.6 MB)\n",
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            "\u001b[?25hCollecting tensorflow-estimator<2.14,>=2.13.0 (from tensorflow<2.14,>=2.13.0->tensorflow_text)\n",
            "  Downloading tensorflow_estimator-2.13.0-py2.py3-none-any.whl (440 kB)\n",
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            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.14,>=2.13.0->tensorflow_text) (1.14.1)\n",
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            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (1.3.1)\n",
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            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (3.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (2.1.3)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow<2.14,>=2.13.0->tensorflow_text) (3.2.2)\n",
            "Installing collected packages: typing-extensions, tensorflow-estimator, keras, tensorboard, tensorflow, tensorflow_text\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.7.1\n",
            "    Uninstalling typing_extensions-4.7.1:\n",
            "      Successfully uninstalled typing_extensions-4.7.1\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.12.0\n",
            "    Uninstalling tensorflow-estimator-2.12.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.12.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.12.0\n",
            "    Uninstalling keras-2.12.0:\n",
            "      Successfully uninstalled keras-2.12.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.12.3\n",
            "    Uninstalling tensorboard-2.12.3:\n",
            "      Successfully uninstalled tensorboard-2.12.3\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.12.0\n",
            "    Uninstalling tensorflow-2.12.0:\n",
            "      Successfully uninstalled tensorflow-2.12.0\n",
            "Successfully installed keras-2.13.1 tensorboard-2.13.0 tensorflow-2.13.0 tensorflow-estimator-2.13.0 tensorflow_text-2.13.0 typing-extensions-4.5.0\n",
            "Collecting tensorflow-gpu\n",
            "  Downloading tensorflow-gpu-2.12.0.tar.gz (2.6 kB)\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk\n",
        "import nltk\n",
        "from nltk.corpus import wordnet as wn\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "from nltk import word_tokenize, pos_tag\n",
        "from collections import defaultdict\n",
        "nltk.download('averaged_perceptron_tagger')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUxGpRzZf8Mt",
        "outputId": "cdb6114a-1bcc-4f8f-de1a-429d7ea82605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.4)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.3.1)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2022.10.31)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.65.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import ast\n",
        "#to filter the specifications column"
      ],
      "metadata": {
        "id": "7wVGZv_bxUQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vgpng87Nf89R",
        "outputId": "d8284753-bd98-471c-8dea-1eaa726448da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m47.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m77.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m70.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertModel, DistilBertTokenizer\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "8pmshqHmf-Pc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DATASET_FILE = \"/content/drive/MyDrive/flipkart_com-ecommerce_sample.csv\"\n",
        "TRUE_K =12"
      ],
      "metadata": {
        "id": "Uk3BLmFKf_jv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_rows = 20000\n",
        "df = pd.read_csv(DATASET_FILE,nrows=no_rows)\n",
        "df = df.drop_duplicates(subset = [\"pid\"])\n",
        "df = df[['product_name','description','brand','discounted_price','product_specifications']]\n",
        "df2 = df['product_name']\n",
        "df = df.astype(str)\n",
        "df = df.dropna()\n",
        "df = df.applymap(lambda x: x.lower())\n",
        "# print(df['description'][:5])"
      ],
      "metadata": {
        "id": "8YY11slFgBLb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_specifications'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "-kirfvoGxYn4",
        "outputId": "abe2b937-6e4d-4584-892c-555c07f5e3d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'{\"product_specification\"=>[{\"key\"=>\"number of contents in sales package\", \"value\"=>\"pack of 3\"}, {\"key\"=>\"fabric\", \"value\"=>\"cotton lycra\"}, {\"key\"=>\"type\", \"value\"=>\"cycling shorts\"}, {\"key\"=>\"pattern\", \"value\"=>\"solid\"}, {\"key\"=>\"ideal for\", \"value\"=>\"women\\'s\"}, {\"value\"=>\"gentle machine wash in lukewarm water, do not bleach\"}, {\"key\"=>\"style code\", \"value\"=>\"altht_3p_21\"}, {\"value\"=>\"3 shorts\"}]}'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "# Function to extract keys and values from product_specifications column\n",
        "def extract_specifications(specifications):\n",
        "    pairs = re.findall(r'\"key\"=>\"(.*?)\", \"value\"=>\"(.*?)\"', specifications)\n",
        "    pairs_formatted = [f\"{key}: {value}\" for key, value in pairs]\n",
        "    return ' '.join(pairs_formatted)\n",
        "\n",
        "# Apply the function to the product_specifications column\n",
        "df['product_specifications'] = df['product_specifications'].apply(extract_specifications)\n",
        "\n",
        "print(df['product_specifications'][0])\n",
        "\n",
        "# df[\"product_specifications\"][0]\n",
        "\n",
        "import re\n",
        "\n",
        "def remove_colors(brand):\n",
        "    # Define a list of colors to remove\n",
        "    colors = [\n",
        "    'amber', 'aquamarine', 'aqua', 'beige', 'black', 'blue', 'bronze',\n",
        "    'brown', 'burgundy', 'caramel', 'chartreuse', 'charcoal', 'coral',\n",
        "    'crimson', 'cyan', 'eggplant', 'emerald', 'forest green', 'fuchsia',\n",
        "    'gold', 'golden', 'gray', 'green', 'indigo', 'ivory', 'jade', 'khaki',\n",
        "    'lavender', 'lemon', 'lime', 'lilac', 'magenta', 'maroon', 'mint',\n",
        "    'mint green', 'moss green', 'mustard', 'navy', 'neon amber',\n",
        "    'neon blue', 'neon coral', 'neon crimson', 'neon fuchsia', 'neon gold',\n",
        "    'neon green', 'neon indigo', 'neon lime', 'neon magenta', 'neon orange',\n",
        "    'neon pink', 'neon platinum', 'neon purple', 'neon red', 'neon silver',\n",
        "    'neon teal', 'neon turquoise', 'neon violet', 'olive', 'olive green',\n",
        "    'orange', 'orchid', 'pearl', 'peach', 'peacock blue', 'peacock green',\n",
        "    'periwinkle', 'pink', 'platinum', 'purple', 'red', 'rose', 'rose gold',\n",
        "    'ruby', 'ruby red', 'rust', 'salmon', 'salmon pink', 'sapphire',\n",
        "    'silver', 'sky blue', 'steel blue', 'tan', 'tangerine', 'taupe', 'teal',\n",
        "    'topaz', 'turquoise', 'violet', 'white', 'yellow', 'baby pink', 'baby blue'\n",
        "    ]\n",
        "    # Create a regular expression pattern to match the colors\n",
        "    pattern = r'\\b(?:{})\\b'.format('|'.join(colors))\n",
        "\n",
        "    # Remove the colors from the brand name using regex substitution\n",
        "    brand = re.sub(pattern, '', brand, flags=re.IGNORECASE).strip()\n",
        "\n",
        "    return brand\n",
        "\n",
        "# Apply the remove_colors function to the 'brand' column\n",
        "df['brand'] = df['brand'].apply(remove_colors)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "EltMyaBcx1gX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8842a340-1885-491a-8919-3045f6785a9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of contents in sales package: pack of 3 fabric: cotton lycra type: cycling shorts pattern: solid ideal for: women's style code: altht_3p_21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(df['product_category_tree'][0])"
      ],
      "metadata": {
        "id": "NiN6VUQu2-pf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df['product_category_tree'] = df['product_category_tree'].str.replace(\"[\", \"\").str.replace(\"]\", \"\").str.replace(\"\\\"\", \"\")\n",
        "\n",
        "# print(df['product_category_tree'][0])"
      ],
      "metadata": {
        "id": "X6i9L95wx3jJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.applymap(lambda x: re.sub('[^A-Za-z0-9]+', ' ', str(x)))\n",
        "\n",
        "\n",
        "# df['combined_text'] = df['product_name'] + ' ' + df['description'] + ' ' + df['brand'] #1\n",
        "df['combined_text'] = df['product_name'] + ' ' + df['description'] + ' ' + df['product_specifications'] #2\n",
        "# df['combined_text'] = df['description'] + ' ' + df['brand'] + ' ' + df['product_specifications']  #3\n",
        "# df['combined_text'] = df['product_name'] + ' ' + df['description'] + ' ' + df['brand'] + ' ' + df['product_specifications'] #4\n",
        "# df['combined_text'] = df['product_name'] #5\n",
        "\n",
        "\n",
        "# df['combined_text'] = df['combined_text'].str.lower()\n",
        "# df['combined_text'] = df['combined_text'].apply(lambda x: re.sub('[^A-Za-z0-9]+', ' ', str(x)))\n"
      ],
      "metadata": {
        "id": "iQImdITIx43W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['combined_text'][0]"
      ],
      "metadata": {
        "id": "vfgzcPM1x4p1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "a614baa5-dab2-4316-d612-0de052174ee5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'alisha solid women s cycling shorts key features of alisha solid women s cycling shorts cotton lycra navy red navy specifications of alisha solid women s cycling shorts shorts details number of contents in sales package pack of 3 fabric cotton lycra type cycling shorts general details pattern solid ideal for women s fabric care gentle machine wash in lukewarm water do not bleach additional details style code altht 3p 21 in the box 3 shorts number of contents in sales package pack of 3 fabric cotton lycra type cycling shorts pattern solid ideal for women s style code altht 3p 21'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_transformers\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "bert = SentenceTransformer('bert-base-nli-mean-tokens')"
      ],
      "metadata": {
        "id": "8tjmD9-Ix4Vw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "17481037-c26e-458e-8af3-8836552c4a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence_transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m3.8 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: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.30.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.65.0)\n",
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            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.15.2+cu118)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.10.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (3.8.1)\n",
            "Collecting sentencepiece (from sentence_transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.16.4)\n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.3.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (3.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence_transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence_transformers\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125926 sha256=68fb86807fd1485f8e12420d32e1c2415154003063036a4fda48aa4c87402822\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence_transformers\n",
            "Installing collected packages: sentencepiece, sentence_transformers\n",
            "Successfully installed sentence_transformers-2.2.2 sentencepiece-0.1.99\n"
          ]
        },
        {
          "output_type": "display_data",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Get embeddings\n",
        "sentence_embeddings = bert.encode(df['combined_text'].tolist())"
      ],
      "metadata": {
        "id": "yGbezQZAGYGj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "# Define the file path where you want to save the embeddings\n",
        "embeddings_path = \"/content/drive/MyDrive/Colab Notebooks/embeddings2.npy\"\n",
        "\n",
        "# np.save(embeddings_path,sentence_embeddings)\n",
        "# Save the embeddings to the file"
      ],
      "metadata": {
        "id": "8pm26XweGq5S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_brand(query):\n",
        "    query = str(query).lower()  # Convert the query to lowercase for case-insensitive matching\n",
        "    brand_names = df['brand'].str.lower()  # Convert the 'brand' column to lowercase for case-insensitive matching\n",
        "\n",
        "    for brand in brand_names:\n",
        "        if brand in query:\n",
        "            return brand\n",
        "\n",
        "    return None  # If no brand name is found in the query\n",
        "import numpy as np\n",
        "embeddings_path = \"/content/drive/MyDrive/Colab Notebooks/embeddings2.npy\"\n",
        "np.save(embeddings_path,sentence_embeddings)\n",
        "\n",
        "# userinput = input(\"enter query\") #test\n",
        "# print(extract_brand(userinput))\n",
        "# print(len(df['brand']))\n",
        "# print(len(df))"
      ],
      "metadata": {
        "id": "oilzquq41wQD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_embeddings = np.load(embeddings_path) #for future sessions\n",
        "#Compute similarity\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# user_input = str.lower(input(\"Enter your query: \"))\n",
        "\n",
        "# user_input = user_input.apply(lambda x: re.sub('[^A-Za-z0-9]+',' ',str(x))) since .apply() is for pd series / dataframes\n",
        "\n",
        "import re\n",
        "# start = time.time() #start time for recommendation\n",
        "pd.set_option('display.max_colwidth', -1) #to prevent truncation from end of product name\n",
        "\n",
        "def brand_inference(user_input,sentence_embeddings):\n",
        "  print(\"Recommendations for \\\"\",user_input,\"\\\" are: \",sep='')\n",
        "  user_input = re.sub('[^A-Za-z0-9]+', ' ', user_input)\n",
        "  start=time.time()\n",
        "  query_brand = extract_brand(user_input)\n",
        "  user_input_embedding = bert.encode([user_input])\n",
        "  # similarity=cosine_similarity(sentence_embeddings)\n",
        "  similarities = cosine_similarity(user_input_embedding, sentence_embeddings)\n",
        "  if query_brand is not None:\n",
        "    df['brand_match_weight'] = df['brand'].apply(lambda x: 1.5 if x == query_brand else 1.0)\n",
        "    df['brand_match_weight'] = df['brand_match_weight'] * df['product_name'].apply(lambda x: 1.5  if query_brand in x else 1.0) #now checking for brand name in product_name column too\n",
        "  else:\n",
        "    df['brand_match_weight'] = 1.0\n",
        "\n",
        "  similarities_with_brand = similarities*(np.array(df['brand_match_weight']))\n",
        "  num_recommendations = 10\n",
        "  top_indices = similarities_with_brand.argsort()[0][::-1][:num_recommendations]\n",
        "\n",
        "  df['product_name_origin']=df2 #to retain the original product names, since the df was preprocessed, which involved removing all symbols etc.\n",
        "  recommended_products = df.iloc[top_indices]['product_name_origin']\n",
        "  print(recommended_products)\n",
        "  end=time.time()-start\n",
        "  print(end)\n",
        "\n",
        "# input_list = [\"bluetooth headphones wired and wireless\",\"asics running shoes\",\"mast & harbour full sleeve \",\"mast & harbour womens\",\"allure auto car mat\"]\n",
        "input_list = [\"bluetooth headphones wired and wireless\",\"asics running shoes\",\"Car mat\",\"T-Shirts\", \"Bluestone solitaire diamond 18k ring necklace jewellery\"]\n",
        "\n",
        "for i in input_list:\n",
        "  brand_inference(i,sentence_embeddings)\n",
        "  print()\n",
        "\n",
        "# print(type(similarities))\n",
        "# if query_brand!=None:\n",
        "#   df['brand_match_weight'] = df['brand'].apply(lambda x: 10 if x == query_brand else 1.0)  #previously used just to match with brand column\n",
        "\n",
        "# if query_brand is not None:\n",
        "#     df['brand_match_weight'] = df['brand'].apply(lambda x: 1.5 if x == query_brand else 1.0)\n",
        "#     df['brand_match_weight'] = df['brand_match_weight'] * df['product_name'].apply(lambda x: 1.5  if query_brand in x else 1.0) #now checking for brand name in product_name column too\n",
        "# else:\n",
        "#     df['brand_match_weight'] = 1.0\n",
        "\n",
        "# print(df['brand_match_weight'][:10])\n",
        "# similarities_df = pd.DataFrame(similarities,columns = df.index)\n",
        "# similarities_np_array = np.array(similarities)\n",
        "# similarities_with_brand = similarities*(np.array(df['brand_match_weight']))\n",
        "# print(np.array(df['brand_match_weight'][:10]))\n",
        "# print(similarities[:10])\n",
        "# print(similarities_with_brand[:10])\n",
        "# print(len(df['brand_match_weight']))\n",
        "# num_recommendations = 10\n",
        "# top_indices = similarities_with_brand.argsort()[0][::-1][:num_recommendations]\n",
        "# top_indices = similarities.argsort()[0][::-1][:num_recommendations]\n",
        "\n",
        "# top_indices = np.argsort(similarities_with_brand[0])[::-1][:num_recommendations]\n",
        "# df['product_name_origin']=df2 #to retain the original product names, since the df was preprocessed, which involved removing all symbols etc.\n",
        "# recommended_products = df.iloc[top_indices]['product_name_origin']\n",
        "\n",
        "# print(recommended_products)\n",
        "# end = time.time() - start #end time\n",
        "# print(end)\n",
        "# print(df.iloc[top_indices]['product_specifications'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLP-BEya-tpX",
        "outputId": "38ee97d4-7327-4e3b-b83e-4c0d260e5089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for \"bluetooth headphones wired and wireless\" are: \n",
            "5507     Tech Yug BH-503 bluethooth headset Wireless Bluetooth Headset                                                      \n",
            "19081    Head Kik Premium Quality Solo2 S460 Wireless Bluetooth Headset Stereo Dynamic Wired & Wireless Bluetooth Headphones\n",
            "9163     Zidane 503TF_BLK Wireless Bluetooth Headset                                                                        \n",
            "7955     LIFE LIKE STN-840 4.1 WITH MIC Wireless Bluetooth Headset                                                          \n",
            "8007     LIFE LIKE OTP-200 WITH MIC Wireless Bluetooth Headset                                                              \n",
            "7930     LIFE LIKE S450 3.0 WITH MIC GOOD SOUND QUALITY Wired & Wireless Bluetooth Headphones                               \n",
            "9185     CHKOKKO Earbud Skin Wireless Bluetooth Headset                                                                     \n",
            "7588     Janhvi Bluetooth Beatz Pill F Wireless Home Audio Speaker                                                          \n",
            "8017     LIFE LIKE HBS-730 WITH MIC Wireless Bluetooth Headset                                                              \n",
            "19087    Dhhan Earphones/Handfree for Iphone 4/4s/4g/5/5c/5s Stereo Dynamic Headphone Wired Bluetooth Headphones            \n",
            "Name: product_name_origin, dtype: object\n",
            "0.11122322082519531\n",
            "\n",
            "Recommendations for \"asics running shoes\" are: \n",
            "344      Asics Gel-Kayano 22 Running Shoes                    \n",
            "341      Asics Gel-Cumulus 17 Running Shoes                   \n",
            "14573    BAAJ Running Shoes                                   \n",
            "10824    xpert Running Shoes                                  \n",
            "1140     CANMP Running Shoes                                  \n",
            "17752    Rex Monk Strap                                       \n",
            "17642    ETHICS Running Shoes                                 \n",
            "18527    Z-Support Senior Monopod Walking Polo Stick - 36 inch\n",
            "10495    Vector X Men's Track Pants                           \n",
            "3236     AMCO Running Shoes                                   \n",
            "Name: product_name_origin, dtype: object\n",
            "0.12424874305725098\n",
            "\n",
            "Recommendations for \"Car mat\" are: \n",
            "10558    Himmlisch 20503 Car Bottle Holder                                                   \n",
            "1060     D'clair Micromax Canvas Engage A091 Battery Charger                                 \n",
            "1510     ACCESSOREEZ CRTRTBL052 Cup Holder Car Tray Table                                    \n",
            "7503     Packy Poda Car Mat Mahindra Xylo                                                    \n",
            "10458    Car vastra 1pcs Car Vastra Honda Beige Backrest Massager                            \n",
            "10807    Speedwav 1 Rear View Mirror Screen, 1 4.3 Inch Screen & Camera-Renault Fluence Combo\n",
            "7502     Packy Poda Car Mat Maruti Baleno                                                    \n",
            "1122     CARBANAO Plastic Car Mat For Maruti Suzuki Swift                                    \n",
            "9912     CarReflection Black Fabric Car Pillow Cushion for Ford                              \n",
            "9914     CarReflection Black Fabric Car Pillow Cushion for Maruti Suzuki                     \n",
            "Name: product_name_origin, dtype: object\n",
            "0.11862730979919434\n",
            "\n",
            "Recommendations for \"T-Shirts\" are: \n",
            "10633    Max T-Shirt                                                                                                                                                                                                                                                                                                 \n",
            "10689    Mothercare T-Shirt                                                                                                                                                                                                                                                                                          \n",
            "10659    Mothercare Printed Round Neck T-Shirt                                                                                                                                                                                                                                                                       \n",
            "2872     Glus FilletLove Women's T-Shirt Bra                                                                                                                                                                                                                                                                         \n",
            "10707    Mothercare Printed Polo T-Shirt                                                                                                                                                                                                                                                                             \n",
            "10608    Mothercare Printed Round Neck T-Shirt                                                                                                                                                                                                                                                                       \n",
            "7639     PALSUN WEARS Full Sleeve Striped Men's Sweatshirt                                                                                                                                                                                                                                                           \n",
            "16576    JDK NOVELTY Brocade, Art Silk, Organza Self Design Multi-purpose Fabric, Kurti Fabric, Blouse Material, Kurta Fabric, Dress/Top Material, Jacket Fabric, Kurta & Pallazo Material, Sherwani Fabric, Kurta & Churidar Material, Lace, Salwar Suit Material, Kurta & Patiyala Material, Lehenga Choli Material\n",
            "10623    Mothercare T-Shirt                                                                                                                                                                                                                                                                                          \n",
            "10620    Mothercare Printed Round Neck T-Shirt                                                                                                                                                                                                                                                                       \n",
            "Name: product_name_origin, dtype: object\n",
            "0.12318563461303711\n",
            "\n",
            "Recommendations for \"Bluestone solitaire diamond 18k ring necklace jewellery\" are: \n",
            "9825     BlueStone Exquisite Tulip Rose Gold Diamond 14 K Ring             \n",
            "13073    BlueStone Heavenly Sunflower Yellow Gold Diamond 18 K Ring        \n",
            "13416    BlueStone Tripartite Axis Yellow Gold Diamond 18 K Ring           \n",
            "13620    BlueStone The Aleysia Gold Diamond 18 K Ring                      \n",
            "12801    BlueStone The Cyprian Trinity Gold Diamond 18 K Ring              \n",
            "13541    BlueStone The Tahira White Gold Diamond 18 K Ring                 \n",
            "13570    BlueStone Kshitija Yellow Gold Diamond 18 K Ring                  \n",
            "13590    BlueStone The Bougainvillea White Gold Sapphire, Diamond 18 K Ring\n",
            "12977    BlueStone Priyala Ring Yellow Gold Diamond, Ruby 14 K Ring        \n",
            "13244    BlueStone The Aureus Gold Diamond 18 K Ring                       \n",
            "Name: product_name_origin, dtype: object\n",
            "0.12320685386657715\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[\"bluetooth headphones wired and wireless\",\"asics running shoes\",\"mast & harbour full sleeve \",\"mast & harbour womens\",\"allure auto car mat\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9dTujtk-rgU",
        "outputId": "d45da549-2b43-4756-999b-ffc26488af5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['bluetooth headphones wired and wireless',\n",
              " 'asics running shoes',\n",
              " 'mast & harbour full sleeve ',\n",
              " 'mast & harbour womens',\n",
              " 'allure auto car mat']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    }
  ]
}