Movie Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f59816c",
   "metadata": {},
   "source": [
    "# Movie Recommendation System "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58aeebb",
   "metadata": {},
   "source": [
    "### Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dce1a178",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a32148a",
   "metadata": {},
   "source": [
    "### Importing the Movies Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dc9655c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv(\"movies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6870ffce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "\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  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784c90b6",
   "metadata": {},
   "source": [
    "### Cleaning the Data (Removing Punctuations using Regular expression Library)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "94e8adb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def clean_title(title):\n",
    "    title = re.sub(\"[^a-zA-Z0-9 ]\", \"\", title)\n",
    "    return title\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b0902d63",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies[\"clean_title\"] = movies[\"title\"].apply(clean_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dbd3f738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>clean_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>Toy Story 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>Jumanji 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Grumpier Old Men 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>Waiting to Exhale 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Father of the Bride Part II 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62418</th>\n",
       "      <td>209157</td>\n",
       "      <td>We (2018)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>We 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62419</th>\n",
       "      <td>209159</td>\n",
       "      <td>Window of the Soul (2001)</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Window of the Soul 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62420</th>\n",
       "      <td>209163</td>\n",
       "      <td>Bad Poems (2018)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>Bad Poems 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62421</th>\n",
       "      <td>209169</td>\n",
       "      <td>A Girl Thing (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>A Girl Thing 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62422</th>\n",
       "      <td>209171</td>\n",
       "      <td>Women of Devil's Island (1962)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>Women of Devils Island 1962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62423 rows × 4 columns</p>\n",
       "</div>"
      ],
      "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",
       "...        ...                                 ...   \n",
       "62418   209157                           We (2018)   \n",
       "62419   209159           Window of the Soul (2001)   \n",
       "62420   209163                    Bad Poems (2018)   \n",
       "62421   209169                 A Girl Thing (2001)   \n",
       "62422   209171      Women of Devil's Island (1962)   \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",
       "...                                            ...   \n",
       "62418                                        Drama   \n",
       "62419                                  Documentary   \n",
       "62420                                 Comedy|Drama   \n",
       "62421                           (no genres listed)   \n",
       "62422                       Action|Adventure|Drama   \n",
       "\n",
       "                            clean_title  \n",
       "0                        Toy Story 1995  \n",
       "1                          Jumanji 1995  \n",
       "2                 Grumpier Old Men 1995  \n",
       "3                Waiting to Exhale 1995  \n",
       "4      Father of the Bride Part II 1995  \n",
       "...                                 ...  \n",
       "62418                           We 2018  \n",
       "62419           Window of the Soul 2001  \n",
       "62420                    Bad Poems 2018  \n",
       "62421                 A Girl Thing 2001  \n",
       "62422       Women of Devils Island 1962  \n",
       "\n",
       "[62423 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0b3724c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(ngram_range=(1,2))\n",
    "\n",
    "tfidf = vectorizer.fit_transform(movies[\"clean_title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24786fb6",
   "metadata": {},
   "source": [
    "## Creating Search Engine for our Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2af56747",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def search(title):\n",
    "    title = clean_title(title)\n",
    "    query_vec = vectorizer.transform([title])\n",
    "    similarity = cosine_similarity(query_vec, tfidf).flatten()\n",
    "    indices = np.argpartition(similarity, -5)[-5:]\n",
    "    results = movies.iloc[indices].iloc[::-1]\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38377374",
   "metadata": {},
   "source": [
    "### Creating Interface for the Search Bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "885bbb71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7888\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7888/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/plain": []
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg=gr.Interface(fn=search,inputs=gr.components.Textbox(lines=1,placeholder=\"Enter the Name of the Movie.....\"),\n",
    "                outputs=gr.components.Dataframe(headers=['movieId','title','genres','clean_title'])\n",
    "               ,examples=['Toy Story','Avengers','The Accountant','Thor','The Dictator']\n",
    "               ,title='Search Bar 😺😺😺',live=True)\n",
    "gg.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d85d6b",
   "metadata": {},
   "source": [
    "### Importing the ratings Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "268c9a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv(\"ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cc30c204",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3.5</td>\n",
       "      <td>1147868817</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1147868828</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>5.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>899</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1147868510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>25000090</th>\n",
       "      <td>162541</td>\n",
       "      <td>50872</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1240953372</td>\n",
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       "      <th>25000091</th>\n",
       "      <td>162541</td>\n",
       "      <td>55768</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1240951998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000092</th>\n",
       "      <td>162541</td>\n",
       "      <td>56176</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1240950697</td>\n",
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       "    <tr>\n",
       "      <th>25000093</th>\n",
       "      <td>162541</td>\n",
       "      <td>58559</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1240953434</td>\n",
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       "    <tr>\n",
       "      <th>25000094</th>\n",
       "      <td>162541</td>\n",
       "      <td>63876</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1240952515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>25000095 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating   timestamp\n",
       "0              1      296     5.0  1147880044\n",
       "1              1      306     3.5  1147868817\n",
       "2              1      307     5.0  1147868828\n",
       "3              1      665     5.0  1147878820\n",
       "4              1      899     3.5  1147868510\n",
       "...          ...      ...     ...         ...\n",
       "25000090  162541    50872     4.5  1240953372\n",
       "25000091  162541    55768     2.5  1240951998\n",
       "25000092  162541    56176     2.0  1240950697\n",
       "25000093  162541    58559     4.0  1240953434\n",
       "25000094  162541    63876     5.0  1240952515\n",
       "\n",
       "[25000095 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "65033a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId         int64\n",
       "movieId        int64\n",
       "rating       float64\n",
       "timestamp      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea524225",
   "metadata": {},
   "source": [
    "### Creating a function for finding similar movies liked by other users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "852af83b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_movies(movie_id):\n",
    "    similar_users = ratings[(ratings[\"movieId\"] == movie_id) & (ratings[\"rating\"] > 4)][\"userId\"].unique()\n",
    "    similar_user_recs = ratings[(ratings[\"userId\"].isin(similar_users)) & (ratings[\"rating\"] > 4)][\"movieId\"]\n",
    "    similar_user_recs = similar_user_recs.value_counts() / len(similar_users)\n",
    "\n",
    "    similar_user_recs = similar_user_recs[similar_user_recs > .10]\n",
    "    all_users = ratings[(ratings[\"movieId\"].isin(similar_user_recs.index)) & (ratings[\"rating\"] > 4)]\n",
    "    all_user_recs = all_users[\"movieId\"].value_counts() / len(all_users[\"userId\"].unique())\n",
    "    rec_percentages = pd.concat([similar_user_recs, all_user_recs], axis=1)\n",
    "    rec_percentages.columns = [\"similar\", \"all\"]\n",
    "    \n",
    "    rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "    rec_percentages = rec_percentages.sort_values(\"score\", ascending=False)\n",
    "    return rec_percentages.head(10).merge(movies, left_index=True, right_on=\"movieId\")[[\"score\", \"title\", \"genres\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "555adefd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>232.470046</td>\n",
       "      <td>The Accountant (2016)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40872</th>\n",
       "      <td>72.991914</td>\n",
       "      <td>Jason Bourne (2016)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47952</th>\n",
       "      <td>69.550465</td>\n",
       "      <td>The Hitman's Bodyguard (2017)</td>\n",
       "      <td>Action|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41190</th>\n",
       "      <td>65.329174</td>\n",
       "      <td>War Dogs (2016)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39656</th>\n",
       "      <td>63.909291</td>\n",
       "      <td>Snowden (2016)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21755</th>\n",
       "      <td>57.751993</td>\n",
       "      <td>Equalizer, The (2014)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16116</th>\n",
       "      <td>49.236283</td>\n",
       "      <td>Mechanic, The (2011)</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27490</th>\n",
       "      <td>48.362563</td>\n",
       "      <td>Focus (2015)</td>\n",
       "      <td>Comedy|Crime|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19020</th>\n",
       "      <td>47.541535</td>\n",
       "      <td>Jack Reacher (2012)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41117</th>\n",
       "      <td>45.504775</td>\n",
       "      <td>Sully (2016)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            score                          title                      genres\n",
       "41860  232.470046          The Accountant (2016)        Crime|Drama|Thriller\n",
       "40872   72.991914            Jason Bourne (2016)                      Action\n",
       "47952   69.550465  The Hitman's Bodyguard (2017)               Action|Comedy\n",
       "41190   65.329174                War Dogs (2016)                      Comedy\n",
       "39656   63.909291                 Snowden (2016)              Drama|Thriller\n",
       "21755   57.751993          Equalizer, The (2014)       Action|Crime|Thriller\n",
       "16116   49.236283           Mechanic, The (2011)       Action|Drama|Thriller\n",
       "27490   48.362563                   Focus (2015)  Comedy|Crime|Drama|Romance\n",
       "19020   47.541535            Jack Reacher (2012)       Action|Crime|Thriller\n",
       "41117   45.504775                   Sully (2016)                       Drama"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_similar_movies(162606)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a03a8443",
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_type(title):\n",
    "    if len(title) > 5:\n",
    "        results = search(title)\n",
    "        movie_id = results.iloc[0][\"movieId\"]\n",
    "        ans=find_similar_movies(movie_id)\n",
    "        return ans\n",
    "#Function for returning the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779ee8a7",
   "metadata": {},
   "source": [
    "## Creating Interface for the Movie Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "943d3eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7886\n",
      "\n",
      "Could not create share link. Please check your internet connection or our status page: https://status.gradio.app.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7886/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recom=gr.Interface(fn=on_type,inputs=gr.components.Textbox(lines=1,placeholder=\"Enter the Name of the Movie.....\"),\n",
    "                outputs=gr.components.Dataframe(headers=['score','title','genres'])\n",
    "               ,examples=['Toy Story','John Wick','The Accountant','Evil Dead','The Dictator']\n",
    "               ,title='Movie Recommendation System 😺😺😺',live=True)\n",
    "recom.launch(share=True)"
   ]
  }
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