train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Poster_Link', 'Series_Title', 'Released_Year', 'Certificate',\n",
       "       'Runtime', 'Genre', 'IMDB_Rating', 'Overview', 'Meta_score', 'Director',\n",
       "       'Star1', 'Star2', 'Star3', 'Star4', 'No_of_Votes', 'Gross'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"data.csv\")\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Overview</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Two imprisoned men bond over a number of years...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>An organized crime dynasty's aging patriarch t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>When the menace known as the Joker wreaks havo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>The early life and career of Vito Corleone in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>A jury holdout attempts to prevent a miscarria...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Breakfast at Tiffany's</td>\n",
       "      <td>A young New York socialite becomes interested ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Giant</td>\n",
       "      <td>Sprawling epic covering the life of a Texas ca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>From Here to Eternity</td>\n",
       "      <td>In Hawaii in 1941, a private is cruelly punish...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Lifeboat</td>\n",
       "      <td>Several survivors of a torpedoed merchant ship...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>A man in London tries to help a counter-espion...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Series_Title  \\\n",
       "0    The Shawshank Redemption   \n",
       "1               The Godfather   \n",
       "2             The Dark Knight   \n",
       "3      The Godfather: Part II   \n",
       "4                12 Angry Men   \n",
       "..                        ...   \n",
       "995    Breakfast at Tiffany's   \n",
       "996                     Giant   \n",
       "997     From Here to Eternity   \n",
       "998                  Lifeboat   \n",
       "999              The 39 Steps   \n",
       "\n",
       "                                              Overview  \n",
       "0    Two imprisoned men bond over a number of years...  \n",
       "1    An organized crime dynasty's aging patriarch t...  \n",
       "2    When the menace known as the Joker wreaks havo...  \n",
       "3    The early life and career of Vito Corleone in ...  \n",
       "4    A jury holdout attempts to prevent a miscarria...  \n",
       "..                                                 ...  \n",
       "995  A young New York socialite becomes interested ...  \n",
       "996  Sprawling epic covering the life of a Texas ca...  \n",
       "997  In Hawaii in 1941, a private is cruelly punish...  \n",
       "998  Several survivors of a torpedoed merchant ship...  \n",
       "999  A man in London tries to help a counter-espion...  \n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data[[\"Series_Title\", \"Overview\"]]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_not_null = data[\"Overview\"].notnull().all()\n",
    "all_not_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "c:\\Users\\sayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\__init__.py:169: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "c:\\Users\\sayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from txtai import Embeddings\n",
    "# Initialize the embeddings model \n",
    "embedding = Embeddings() \n",
    "# Extract the \"Overview\" column as a list of sentences \n",
    "sentences = data[\"Overview\"].tolist() \n",
    "# Calculate embedding vectors for multiple sentences \n",
    "embeddings = embedding.batchtransform(sentences)\n",
    "\n",
    "with open('data.pkl', 'wb') as file: \n",
    "    # Step 4: Use pickle.dump to save the object \n",
    "    pickle.dump(data, file)\n",
    "\n",
    "with open('embedding.pkl', 'wb') as file: \n",
    "    # Step 4: Use pickle.dump to save the object \n",
    "    pickle.dump(embeddings, file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Series_Title': 'Wait Until Dark',\n",
       " 'Overview': 'A recently blinded woman is terrorized by a trio of thugs while they search for a heroin-stuffed doll they believe is in her apartment.'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[700].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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