Chapter 6 - Movie_Recommendation.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Q_M3zM0eXxr",
        "outputId": "80cd76c9-d589-41f6-94ac-40f72fc3e793"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, redis, huggingface-hub, transformers, sentence_transformers\n",
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          ]
        }
      ],
      "source": [
        "!pip install pandas sentence_transformers redis\n",
        "!pip install --upgrade redis\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-QYVa0_s4Hc",
        "outputId": "c7ebc434-a761-4c7d-de93-405aa3ae9567"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/archive.zip\n",
            "  inflating: IMDb_Data_final.csv     \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/archive.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import redis\n",
        "import numpy as np\n",
        "from redis.commands.search import Search\n",
        "from redis.commands.search.query import Query\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "redis_host = 'redis-18547.c305.ap-south-1-1.ec2.cloud.redislabs.com'\n",
        "redis_port = 18547\n",
        "redis_password = ''\n",
        "\n",
        "class MovieRecommender:\n",
        "    def __init__(self, redis_host, redis_port, redis_password, csv_file_path, model_name):\n",
        "        \"\"\"\n",
        "        Initialize the MovieRecommender class with the necessary parameters.\n",
        "\n",
        "        Args:\n",
        "        redis_host (str): The host address of the Redis server.\n",
        "        redis_port (int): The port number of the Redis server.\n",
        "        redis_password (str): The password to connect to the Redis server.\n",
        "        csv_file_path (str): The path to the CSV file containing movie data.\n",
        "        model_name (str): The name of the SentenceTransformer model to use for encoding.\n",
        "        \"\"\"\n",
        "        self.redis_host = redis_host\n",
        "        self.redis_port = redis_port\n",
        "        self.redis_password = redis_password\n",
        "        self.csv_file_path = csv_file_path\n",
        "        self.model_name = model_name\n",
        "        self.model = SentenceTransformer(self.model_name)\n",
        "        self.redis_client = redis.Redis(\n",
        "            host=self.redis_host,\n",
        "            port=self.redis_port,\n",
        "            password=self.redis_password\n",
        "        )\n",
        "\n",
        "    def load_data(self):\n",
        "        \"\"\"\n",
        "        Load the movie data from the CSV file and preprocess it.\n",
        "        The data is formatted and encoded into vectors using the SentenceTransformer model.\n",
        "        \"\"\"\n",
        "        self.movie_df = pd.read_csv(self.csv_file_path)\n",
        "        self.movie_df = self.movie_df.dropna(subset=['Category', 'Duration', 'Censor-board-rating'])\n",
        "        self.movie_df.reset_index(inplace=True)\n",
        "        self.movie_df.drop([\"index\"],axis=1,inplace=True)\n",
        "        self.movie_df['formatted_text'] = (\n",
        "            \"The movie \" + self.movie_df['Title'] + \" directed by \" + self.movie_df['Director'] +\n",
        "            \" starring \" + self.movie_df['Stars'] + \" has an IMDb rating of \" + self.movie_df['IMDb-Rating'].astype(str) +\n",
        "            \" under the category \" + self.movie_df['Category'] + \" with a duration of \" + self.movie_df['Duration'] +\n",
        "            \". It has a censor board rating of \" + self.movie_df['Censor-board-rating'] +\n",
        "            \" and was released in the year \" + self.movie_df['ReleaseYear'].astype(str) + \".\"\n",
        "        )\n",
        "        self.movie_df['formatted_text'] = self.movie_df['formatted_text'].apply(lambda x: x.lower())\n",
        "        self.movie_df['row_vector'] = self.movie_df['formatted_text'].apply(lambda x: self.model.encode(x))\n",
        "\n",
        "    def load_vectors(self, _df, vector_data, vector_field_name):\n",
        "        \"\"\"\n",
        "        Load the vectors into the Redis server.\n",
        "\n",
        "        Args:\n",
        "        _df (DataFrame): The DataFrame containing the movie data.\n",
        "        vector_data (list): The list of vector data.\n",
        "        vector_field_name (str): The name of the field to store the vector data.\n",
        "        \"\"\"\n",
        "        p = self.redis_client.pipeline(transaction=False)\n",
        "        for index, row in _df.iterrows():\n",
        "            key = 'title_id:' + str(index)\n",
        "            _data_mapping = {\n",
        "                \"Title\": row['Title'],\n",
        "                \"Director\": row['Director'],\n",
        "                \"Stars\": row['Stars'],\n",
        "                \"Rating\": row['IMDb-Rating'],\n",
        "                \"Category\": row['Category'],\n",
        "                \"Duration\": row['Duration'],\n",
        "                \"Censor_Rating\": row['Censor-board-rating'],\n",
        "                \"ReleaseYear\": row['ReleaseYear'],\n",
        "                vector_field_name: vector_data[index].astype(np.float32).tobytes()\n",
        "            }\n",
        "            p.hset(key, mapping=_data_mapping)\n",
        "        p.execute()\n",
        "\n",
        "    def create_hnsw_index(self, vector_field_name, number_of_vectors, vector_dimensions, distance_metric='COSINE', M=40, EF=200, EF_RUNTIME=100, EPSILON=0.2):\n",
        "        \"\"\"\n",
        "        Create the HNSW index in the Redis server.\n",
        "\n",
        "        Args:\n",
        "        vector_field_name (str): The name of the field to store the vector data.\n",
        "        number_of_vectors (int): The number of vectors to store in the index.\n",
        "        vector_dimensions (int): The number of dimensions of the vectors.\n",
        "        distance_metric (str): The distance metric to use for the index.\n",
        "        M (int): The M parameter for the HNSW index.\n",
        "        EF (int): The EF parameter for the HNSW index.\n",
        "        EF_RUNTIME (int): The EF_RUNTIME parameter for the HNSW index.\n",
        "        EPSILON (float): The EPSILON parameter for the HNSW index.\n",
        "        \"\"\"\n",
        "        create_command = [\n",
        "            \"FT.CREATE\", \"idx\", \"SCHEMA\",\n",
        "            \"Title\", \"TAG\",\n",
        "            \"Director\", \"TEXT\",\n",
        "            \"Stars\", \"TEXT\",\n",
        "            \"Rating\", \"NUMERIC\",\n",
        "            \"Category\", \"TEXT\",\n",
        "            \"Duration\", \"TAG\",\n",
        "            \"Censor_Rating\", \"TAG\",\n",
        "            \"ReleaseYear\", \"NUMERIC\",\n",
        "            vector_field_name, \"VECTOR\", \"HNSW\",\"16\",\"DIM\", str(vector_dimensions),\n",
        "            \"TYPE\", \"FLOAT32\",\n",
        "            \"DISTANCE_METRIC\", str(distance_metric),\n",
        "            \"INITIAL_CAP\", str(number_of_vectors),\n",
        "            \"M\", M,\n",
        "            \"EF_CONSTRUCTION\", EF,\n",
        "            \"EF_RUNTIME\", EF_RUNTIME,\n",
        "            \"EPSILON\", EPSILON\n",
        "        ]\n",
        "        self.redis_client.execute_command(*create_command)\n",
        "\n",
        "    def load_and_index_data(self, num_movies=800, vector_field_name='movie_vector', distance_metric='COSINE', dimensions=384):\n",
        "        \"\"\"\n",
        "        Load the data and index it in the Redis server.\n",
        "\n",
        "        Args:\n",
        "        num_movies (int): The number of movies to load and index.\n",
        "        vector_field_name (str): The name of the field to store the vector data.\n",
        "        distance_metric (str): The distance metric to use for the index.\n",
        "        dimensions (int): The number of dimensions of the vectors.\n",
        "        \"\"\"\n",
        "        self.redis_client.flushall()\n",
        "        self.create_hnsw_index(vector_field_name, num_movies, dimensions, distance_metric)\n",
        "        movie_vec = self.movie_df['row_vector'].tolist()\n",
        "        self.load_vectors(self.movie_df.head(num_movies), movie_vec[0:num_movies], vector_field_name)\n",
        "\n",
        "    def search(self, user_query, top_k=4, dist_field_name=\"__movie_vector_score\"):\n",
        "        \"\"\"\n",
        "        Search the index for the most similar movies to the user's query.\n",
        "\n",
        "        Args:\n",
        "        user_query (str): The user's query.\n",
        "        top_k (int): The number of results to return.\n",
        "        dist_field_name (str): The name of the field to store the distance scores.\n",
        "\n",
        "        Returns:\n",
        "        docs (list): The list of most similar movies.\n",
        "        \"\"\"\n",
        "        e = self.model.encode(user_query.lower())\n",
        "        q = (\n",
        "            Query(f'*=>[KNN $K @movie_vector $BLOB AS {dist_field_name}]')\n",
        "            .return_fields('Title','Director','Stars','Rating', 'Category','Duration','Censor_Rating','ReleaseYear')\n",
        "            .paging(0, top_k)\n",
        "            .sort_by(dist_field_name)\n",
        "            .dialect(2)\n",
        "        )\n",
        "        params_dict = {\"K\": top_k + 1, \"BLOB\": e.astype(np.float32).tobytes()}\n",
        "        docs = self.redis_client.ft().search(q, params_dict)\n",
        "        return docs\n",
        "\n",
        "# Data Loading and Indexing\n",
        "def load_and_index():\n",
        "    # Note: Replace these values with your actual Redis server details and CSV file path\n",
        "    # redis_host = 'redis-17820.c301.ap-south-1-1.ec2.cloud.redislabs.com'\n",
        "    # redis_port = 17820\n",
        "    # redis_password = ''\n",
        "    csv_file_path = '/content/IMDb_Data_final.csv'\n",
        "    model_name = 'sentence-transformers/all-MiniLM-L12-v1'\n",
        "\n",
        "    recommender = MovieRecommender(redis_host, redis_port, redis_password, csv_file_path, model_name)\n",
        "    recommender.load_data()\n",
        "    recommender.load_and_index_data()\n",
        "\n",
        "# Search\n",
        "def search(user_query, top_k=4):\n",
        "    # Note: Replace these values with your actual Redis server details and CSV file path\n",
        "    # redis_host = 'redis-17820.c301.ap-south-1-1.ec2.cloud.redislabs.com'\n",
        "    # redis_port = 17820\n",
        "    # redis_password = ''\n",
        "    csv_file_path = '/content/IMDb_Data_final.csv'\n",
        "    model_name = 'sentence-transformers/all-MiniLM-L12-v1'\n",
        "\n",
        "    recommender = MovieRecommender(redis_host, redis_port, redis_password, csv_file_path, model_name)\n",
        "    docs = recommender.search(user_query, top_k)\n",
        "\n",
        "    # print results\n",
        "    for doc in docs.docs:\n",
        "        print(doc)\n",
        "\n",
        "# Load and index the data\n",
        "load_and_index()\n",
        "\n",
        "# User query\n",
        "user_query=\"i want to watch movies under comedy category with imdb rating of 7 and above\"\n",
        "# Search for the most similar movies\n",
        "search(user_query, top_k=4)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3itDLPaSNAL8",
        "outputId": "e9c1a503-4919-4812-abdc-36bf415dead8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document {'id': 'title_id:528', 'payload': None, 'Title': 'The King of Comedy', 'Director': 'MartinScorsese', 'Stars': 'RobertDeNiro, JerryLewis, DiahnneAbbott, SandraBernhard', 'Rating': '7.8', 'Category': 'Comedy,Crime,Drama', 'Duration': '109min', 'Censor_Rating': 'U', 'ReleaseYear': '1982'}\n",
            "Document {'id': 'title_id:15', 'payload': None, 'Title': 'The Goonies', 'Director': 'RichardDonner', 'Stars': 'SeanAstin, JoshBrolin, JeffCohen, CoreyFeldman', 'Rating': '7.7', 'Category': 'Adventure,Comedy,Family', 'Duration': '114min', 'Censor_Rating': 'U', 'ReleaseYear': '1985'}\n",
            "Document {'id': 'title_id:79', 'payload': None, 'Title': 'The Breakfast Club', 'Director': 'JohnHughes', 'Stars': 'EmilioEstevez, JuddNelson, MollyRingwald, AllySheedy', 'Rating': '7.8', 'Category': 'Comedy,Drama', 'Duration': '97min', 'Censor_Rating': 'UA', 'ReleaseYear': '1985'}\n",
            "Document {'id': 'title_id:468', 'payload': None, 'Title': 'Being John Malkovich', 'Director': 'SpikeJonze', 'Stars': 'JohnCusack, CameronDiaz, CatherineKeener, JohnMalkovich', 'Rating': '7.8', 'Category': 'Comedy,Drama,Fantasy', 'Duration': '113min', 'Censor_Rating': 'R', 'ReleaseYear': '1999'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user_query=\"i want to watch movies under comedy category with imdb rating of 7 and above\"\n",
        "# Search for the most similar movies\n",
        "search(user_query, top_k=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7azqA-jndSb_",
        "outputId": "adaf5f33-a30d-42fe-ed2e-c399adb0d401"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document {'id': 'title_id:528', 'payload': None, 'Title': 'The King of Comedy', 'Director': 'MartinScorsese', 'Stars': 'RobertDeNiro, JerryLewis, DiahnneAbbott, SandraBernhard', 'Rating': '7.8', 'Category': 'Comedy,Crime,Drama', 'Duration': '109min', 'Censor_Rating': 'U', 'ReleaseYear': '1982'}\n",
            "Document {'id': 'title_id:15', 'payload': None, 'Title': 'The Goonies', 'Director': 'RichardDonner', 'Stars': 'SeanAstin, JoshBrolin, JeffCohen, CoreyFeldman', 'Rating': '7.7', 'Category': 'Adventure,Comedy,Family', 'Duration': '114min', 'Censor_Rating': 'U', 'ReleaseYear': '1985'}\n",
            "Document {'id': 'title_id:79', 'payload': None, 'Title': 'The Breakfast Club', 'Director': 'JohnHughes', 'Stars': 'EmilioEstevez, JuddNelson, MollyRingwald, AllySheedy', 'Rating': '7.8', 'Category': 'Comedy,Drama', 'Duration': '97min', 'Censor_Rating': 'UA', 'ReleaseYear': '1985'}\n",
            "Document {'id': 'title_id:468', 'payload': None, 'Title': 'Being John Malkovich', 'Director': 'SpikeJonze', 'Stars': 'JohnCusack, CameronDiaz, CatherineKeener, JohnMalkovich', 'Rating': '7.8', 'Category': 'Comedy,Drama,Fantasy', 'Duration': '113min', 'Censor_Rating': 'R', 'ReleaseYear': '1999'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ipywidgets\n",
        "!jupyter nbextension enable --py widgetsnbextension\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IT-QBIH4HNA0",
        "outputId": "3150895b-5bde-48d2-d878-191fa9a87ca2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.10/dist-packages (7.7.1)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (5.5.6)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (5.7.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (3.6.4)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (7.34.0)\n",
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            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (67.7.2)\n",
            "Collecting jedi>=0.16 (from ipython>=4.0.0->ipywidgets)\n",
            "  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m47.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (0.7.5)\n",
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            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (2.14.0)\n",
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            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.10/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets) (6.4.8)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.1.2)\n",
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            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.8.2)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.17.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets) (0.2.6)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.6.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->jupyter-client->ipykernel>=4.5.1->ipywidgets) (1.16.0)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (21.2.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.1.3)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (4.9.3)\n",
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            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (6.0.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.4)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.2.2)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.8.4)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.8.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (23.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.5.0)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.2.1)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.17.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (4.3.3)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (23.1.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.19.3)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.15.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.4.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.21)\n",
            "Installing collected packages: jedi\n",
            "Successfully installed jedi-0.18.2\n",
            "Enabling notebook extension jupyter-js-widgets/extension...\n",
            "Paths used for configuration of notebook: \n",
            "    \t/root/.jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook: \n",
            "    \t\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook: \n",
            "    \t/root/.jupyter/nbconfig/notebook.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ipywidgets import interact_manual\n",
        "\n",
        "# Initialize the recommender system\n",
        "# load_and_index()\n",
        "\n",
        "def interactive_search():\n",
        "    @interact_manual\n",
        "    def get_input(user_query='Enter your preference here...', top_k=4):\n",
        "        results = search(user_query, top_k)\n",
        "        print(results)\n",
        "        # for result in results.docs:\n",
        "        #     print(result)\n",
        "\n",
        "interactive_search()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "8b712411f4254f6489aabce17bd58af9",
            "8da0f186a7a4456980e392d60f9b393e",
            "52534279ad4f483293eab2f8a4cf99ba",
            "0c9071fec7054da78077676dbca01362",
            "5303c62efab34bbcbcc34927bf3aeda6",
            "72cb28a402e947f497eb04dbb68cf747",
            "9df4b327c1b740a597b0619ffe549cf0",
            "200743da001d4d2ba1f30f4a5e68285d",
            "4508e23736ae4848a9c8927ec3380de2",
            "2d24d317f2a8424da1dcccac211132a8",
            "c4dda352f91e4a6d85cdbd450777b614",
            "0684bb057e884de88b680605bcf693b6",
            "4b04e259ca86474993805fae8a692c2d"
          ]
        },
        "id": "5Ng6rK8CanUY",
        "outputId": "a72170ca-4061-4f5f-8b0d-98768ca738ca"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Text(value='Enter your preference here...', description='user_query'), IntSlider(value=4…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b712411f4254f6489aabce17bd58af9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vXaY_PU4a-VQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "8b712411f4254f6489aabce17bd58af9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8da0f186a7a4456980e392d60f9b393e",
              "IPY_MODEL_52534279ad4f483293eab2f8a4cf99ba",
              "IPY_MODEL_0c9071fec7054da78077676dbca01362",
              "IPY_MODEL_5303c62efab34bbcbcc34927bf3aeda6"
            ],
            "layout": "IPY_MODEL_72cb28a402e947f497eb04dbb68cf747"
          }
        },
        "8da0f186a7a4456980e392d60f9b393e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "TextModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "TextModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "TextView",
            "continuous_update": true,
            "description": "user_query",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_9df4b327c1b740a597b0619ffe549cf0",
            "placeholder": "​",
            "style": "IPY_MODEL_200743da001d4d2ba1f30f4a5e68285d",
            "value": "i want to watch movies under comedy category with imdb rating of 8 and above"
          }
        },
        "52534279ad4f483293eab2f8a4cf99ba": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "IntSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "IntSliderView",
            "continuous_update": true,
            "description": "top_k",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_4508e23736ae4848a9c8927ec3380de2",
            "max": 12,
            "min": -4,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_2d24d317f2a8424da1dcccac211132a8",
            "value": 4
          }
        },
        "0c9071fec7054da78077676dbca01362": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ButtonModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ButtonModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ButtonView",
            "button_style": "",
            "description": "Run Interact",
            "disabled": false,
            "icon": "",
            "layout": "IPY_MODEL_c4dda352f91e4a6d85cdbd450777b614",
            "style": "IPY_MODEL_0684bb057e884de88b680605bcf693b6",
            "tooltip": ""
          }
        },
        "5303c62efab34bbcbcc34927bf3aeda6": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_4b04e259ca86474993805fae8a692c2d",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "stream",
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