Movie_Database_Build.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook does the following \n",
    "* **Retrieves and prints basic data about a movie (title entered by user) from the web (OMDB database)**\n",
    "* **If a poster of the movie could be found, it downloads the file and saves at a user-specified location**\n",
    "* **Finally, stores the movie data in a local SQLite database**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request, urllib.parse, urllib.error\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gets the secret API key (you have to get one from OMDB website and use that, 1000 daily limit) from a JSON file, stored in the same folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('APIkeys.json') as f:\n",
    "    keys = json.load(f)\n",
    "    omdbapi = keys['OMDBapi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "serviceurl = 'http://www.omdbapi.com/?'\n",
    "apikey = '&apikey='+omdbapi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for printing a JSON dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_json(json_data):\n",
    "    list_keys=['Title', 'Year', 'Rated', 'Released', 'Runtime', 'Genre', 'Director', 'Writer', \n",
    "               'Actors', 'Plot', 'Language', 'Country', 'Awards', 'Ratings', \n",
    "               'Metascore', 'imdbRating', 'imdbVotes', 'imdbID']\n",
    "    print(\"-\"*50)\n",
    "    for k in list_keys:\n",
    "        if k in list(json_data.keys()):\n",
    "            print(f\"{k}: {json_data[k]}\")\n",
    "    print(\"-\"*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to download a poster of the movie based on the information from the jason dataset\n",
    "**Saves the downloaded poster in a local directory called 'Posters'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_poster(json_data):\n",
    "    import os\n",
    "    title = json_data['Title']\n",
    "    poster_url = json_data['Poster']\n",
    "    # Splits the poster url by '.' and picks up the last string as file extension\n",
    "    poster_file_extension=poster_url.split('.')[-1]\n",
    "    # Reads the image file from web\n",
    "    poster_data = urllib.request.urlopen(poster_url).read()\n",
    "        \n",
    "    savelocation=os.getcwd()+'\\\\'+'Posters'+'\\\\'\n",
    "    # Creates new directory if the directory does not exist. Otherwise, just use the existing path.\n",
    "    if not os.path.isdir(savelocation):\n",
    "        os.mkdir(savelocation)\n",
    "    \n",
    "    filename=savelocation+str(title)+'.'+poster_file_extension\n",
    "    f=open(filename,'wb')\n",
    "    f.write(poster_data)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to create/update the local movie database with the data retreived from the web\n",
    "**Saves the movie data (Title, Year, Runtime, Country, Metascore, and IMDB rating) into a local SQLite database called 'movieinfo.sqlite'** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_in_database(json_data):\n",
    "    \n",
    "    filename = input(\"Please enter a name for the database (extension not needed, it will be added automatically): \")\n",
    "    filename = filename+'.sqlite'\n",
    "    \n",
    "    import sqlite3\n",
    "    conn = sqlite3.connect(str(filename))\n",
    "    cur=conn.cursor()\n",
    "    \n",
    "    title = json_data['Title']\n",
    "    # Goes through the json dataset and extracts information if it is available\n",
    "    if json_data['Year']!='N/A':\n",
    "        year = int(json_data['Year'])\n",
    "    if json_data['Runtime']!='N/A':\n",
    "        runtime = int(json_data['Runtime'].split()[0])\n",
    "    if json_data['Country']!='N/A':\n",
    "        country = json_data['Country']\n",
    "    if json_data['Metascore']!='N/A':\n",
    "        metascore = float(json_data['Metascore'])\n",
    "    else:\n",
    "        metascore=-1\n",
    "    if json_data['imdbRating']!='N/A':\n",
    "        imdb_rating = float(json_data['imdbRating'])\n",
    "    else:\n",
    "        imdb_rating=-1\n",
    "    \n",
    "    # SQL commands\n",
    "    cur.execute('''CREATE TABLE IF NOT EXISTS MovieInfo \n",
    "    (Title TEXT, Year INTEGER, Runtime INTEGER, Country TEXT, Metascore REAL, IMDBRating REAL)''')\n",
    "    \n",
    "    cur.execute('SELECT Title FROM MovieInfo WHERE Title = ? ', (title,))\n",
    "    row = cur.fetchone()\n",
    "    \n",
    "    if row is None:\n",
    "        cur.execute('''INSERT INTO MovieInfo (Title, Year, Runtime, Country, Metascore, IMDBRating)\n",
    "                VALUES (?,?,?,?,?,?)''', (title,year,runtime,country,metascore,imdb_rating))\n",
    "    else:\n",
    "        print(\"Record already found. No update made.\")\n",
    "    \n",
    "    # Commits the change and close the connection to the database\n",
    "    conn.commit()\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to print contents of  the local database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_database(database):\n",
    "    \n",
    "    import sqlite3\n",
    "    conn = sqlite3.connect(str(database))\n",
    "    cur=conn.cursor()\n",
    "    \n",
    "    for row in cur.execute('SELECT * FROM MovieInfo'):\n",
    "        print(row)\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to save the database content in an Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_in_excel(filename, database):\n",
    "    \n",
    "    if filename.split('.')[-1]!='xls' and filename.split('.')[-1]!='xlsx':\n",
    "        print (\"Filename does not have correct extension. Please try again\")\n",
    "        return None\n",
    "    \n",
    "    import pandas as pd\n",
    "    import sqlite3\n",
    "    \n",
    "    #df=pd.DataFrame(columns=['Title','Year', 'Runtime', 'Country', 'Metascore', 'IMDB_Rating'])\n",
    "    \n",
    "    conn = sqlite3.connect(str(database))\n",
    "    #cur=conn.cursor()\n",
    "    \n",
    "    df=pd.read_sql_query(\"SELECT * FROM MovieInfo\", conn)\n",
    "    conn.close()\n",
    "    \n",
    "    df.to_excel(filename,sheet_name='Movie Info')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to search for information about a movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def search_movie(title):\n",
    "    if len(title) < 1 or title=='quit': \n",
    "        print(\"Goodbye now...\")\n",
    "        return None\n",
    "\n",
    "    try:\n",
    "        url = serviceurl + urllib.parse.urlencode({'t': title})+apikey\n",
    "        print(f'Retrieving the data of \"{title}\" now... ')\n",
    "        uh = urllib.request.urlopen(url)\n",
    "        data = uh.read()\n",
    "        json_data=json.loads(data)\n",
    "        \n",
    "        if json_data['Response']=='True':\n",
    "            print_json(json_data)\n",
    "            \n",
    "            # Asks user whether to download the poster of the movie\n",
    "            if json_data['Poster']!='N/A':\n",
    "                poster_yes_no=input ('Poster of this movie can be downloaded. Enter \"yes\" or \"no\": ').lower()\n",
    "                if poster_yes_no=='yes':\n",
    "                    save_poster(json_data)\n",
    "            # Asks user whether to save the movie information in a local database\n",
    "            save_database_yes_no=input ('Save the movie info in a local database? Enter \"yes\" or \"no\": ').lower()\n",
    "            if save_database_yes_no=='yes':\n",
    "                save_in_database(json_data)\n",
    "        else:\n",
    "            print(\"Error encountered: \",json_data['Error'])\n",
    "    \n",
    "    except urllib.error.URLError as e:\n",
    "        print(f\"ERROR: {e.reason}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Search for 'Titanic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Enter the name of a movie (enter 'quit' or hit ENTER to quit): Titanic\n",
      "Retrieving the data of \"Titanic\" now... \n",
      "--------------------------------------------------\n",
      "Title: Titanic\n",
      "Year: 1997\n",
      "Rated: PG-13\n",
      "Released: 19 Dec 1997\n",
      "Runtime: 194 min\n",
      "Genre: Drama, Romance\n",
      "Director: James Cameron\n",
      "Writer: James Cameron\n",
      "Actors: Leonardo DiCaprio, Kate Winslet, Billy Zane, Kathy Bates\n",
      "Plot: A seventeen-year-old aristocrat falls in love with a kind but poor artist aboard the luxurious, ill-fated R.M.S. Titanic.\n",
      "Language: English, Swedish\n",
      "Country: USA\n",
      "Awards: Won 11 Oscars. Another 111 wins & 75 nominations.\n",
      "Ratings: [{'Source': 'Internet Movie Database', 'Value': '7.8/10'}, {'Source': 'Rotten Tomatoes', 'Value': '88%'}, {'Source': 'Metacritic', 'Value': '75/100'}]\n",
      "Metascore: 75\n",
      "imdbRating: 7.8\n",
      "imdbVotes: 879,510\n",
      "imdbID: tt0120338\n",
      "--------------------------------------------------\n",
      "Poster of this movie can be downloaded. Enter \"yes\" or \"no\": yes\n",
      "Save the movie info in a local database? Enter \"yes\" or \"no\": yes\n",
      "Please enter a name for the database (extension not needed, it will be added automatically): movies\n"
     ]
    }
   ],
   "source": [
    "title = input('\\nEnter the name of a movie (enter \\'quit\\' or hit ENTER to quit): ')\n",
    "if len(title) < 1 or title=='quit': \n",
    "    print(\"Goodbye now...\")\n",
    "else:\n",
    "    search_movie(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show the downloaded poster of 'Titanic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"Posters/Titanic.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print the content of the local database, only single entry so far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Titanic', 1997, 194, 'USA', 75.0, 7.8)\n"
     ]
    }
   ],
   "source": [
    "print_database('movies.sqlite')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Search for 'Jumanji'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Enter the name of a movie (enter 'quit' or hit ENTER to quit): Jumanji\n",
      "Retrieving the data of \"Jumanji\" now... \n",
      "--------------------------------------------------\n",
      "Title: Jumanji\n",
      "Year: 1995\n",
      "Rated: PG\n",
      "Released: 15 Dec 1995\n",
      "Runtime: 104 min\n",
      "Genre: Adventure, Family, Fantasy\n",
      "Director: Joe Johnston\n",
      "Writer: Jonathan Hensleigh (screenplay), Greg Taylor (screenplay), Jim Strain (screenplay), Greg Taylor (screen story), Jim Strain (screen story), Chris Van Allsburg (screen story), Chris Van Allsburg (book)\n",
      "Actors: Robin Williams, Jonathan Hyde, Kirsten Dunst, Bradley Pierce\n",
      "Plot: When two kids find and play a magical board game, they release a man trapped for decades in it and a host of dangers that can only be stopped by finishing the game.\n",
      "Language: English, French\n",
      "Country: USA\n",
      "Awards: 4 wins & 9 nominations.\n",
      "Ratings: [{'Source': 'Internet Movie Database', 'Value': '6.9/10'}, {'Source': 'Rotten Tomatoes', 'Value': '51%'}, {'Source': 'Metacritic', 'Value': '39/100'}]\n",
      "Metascore: 39\n",
      "imdbRating: 6.9\n",
      "imdbVotes: 246,203\n",
      "imdbID: tt0113497\n",
      "--------------------------------------------------\n",
      "Poster of this movie can be downloaded. Enter \"yes\" or \"no\": yes\n",
      "Save the movie info in a local database? Enter \"yes\" or \"no\": yes\n",
      "Please enter a name for the database (extension not needed, it will be added automatically): movies\n"
     ]
    }
   ],
   "source": [
    "title = input('\\nEnter the name of a movie (enter \\'quit\\' or hit ENTER to quit): ')\n",
    "if len(title) < 1 or title=='quit': \n",
    "    print(\"Goodbye now...\")\n",
    "else:\n",
    "    search_movie(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Search for \"To kill a mockingbird\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Enter the name of a movie (enter 'quit' or hit ENTER to quit): To kill a Mockingbird\n",
      "Retrieving the data of \"To kill a Mockingbird\" now... \n",
      "--------------------------------------------------\n",
      "Title: To Kill a Mockingbird\n",
      "Year: 1962\n",
      "Rated: NOT RATED\n",
      "Released: 16 Mar 1963\n",
      "Runtime: 129 min\n",
      "Genre: Crime, Drama\n",
      "Director: Robert Mulligan\n",
      "Writer: Harper Lee (based on her novel \"To Kill a Mockingbird\"), Horton Foote (screenplay)\n",
      "Actors: Gregory Peck, John Megna, Frank Overton, Rosemary Murphy\n",
      "Plot: Atticus Finch, a lawyer in the Depression-era South, defends a black man against an undeserved rape charge, and his children against prejudice.\n",
      "Language: English\n",
      "Country: USA\n",
      "Awards: Won 3 Oscars. Another 10 wins & 16 nominations.\n",
      "Ratings: [{'Source': 'Internet Movie Database', 'Value': '8.3/10'}, {'Source': 'Rotten Tomatoes', 'Value': '91%'}, {'Source': 'Metacritic', 'Value': '87/100'}]\n",
      "Metascore: 87\n",
      "imdbRating: 8.3\n",
      "imdbVotes: 247,349\n",
      "imdbID: tt0056592\n",
      "--------------------------------------------------\n",
      "Poster of this movie can be downloaded. Enter \"yes\" or \"no\": yes\n",
      "Save the movie info in a local database? Enter \"yes\" or \"no\": yes\n",
      "Please enter a name for the database (extension not needed, it will be added automatically): movies\n"
     ]
    }
   ],
   "source": [
    "title = input('\\nEnter the name of a movie (enter \\'quit\\' or hit ENTER to quit): ')\n",
    "if len(title) < 1 or title=='quit': \n",
    "    print(\"Goodbye now...\")\n",
    "else:\n",
    "    search_movie(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Search for \"Titanic\" again, note while trying to save the record, the message from the database connection saying 'Record already found'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Enter the name of a movie (enter 'quit' or hit ENTER to quit): Titanic\n",
      "Retrieving the data of \"Titanic\" now... \n",
      "--------------------------------------------------\n",
      "Title: Titanic\n",
      "Year: 1997\n",
      "Rated: PG-13\n",
      "Released: 19 Dec 1997\n",
      "Runtime: 194 min\n",
      "Genre: Drama, Romance\n",
      "Director: James Cameron\n",
      "Writer: James Cameron\n",
      "Actors: Leonardo DiCaprio, Kate Winslet, Billy Zane, Kathy Bates\n",
      "Plot: A seventeen-year-old aristocrat falls in love with a kind but poor artist aboard the luxurious, ill-fated R.M.S. Titanic.\n",
      "Language: English, Swedish\n",
      "Country: USA\n",
      "Awards: Won 11 Oscars. Another 111 wins & 75 nominations.\n",
      "Ratings: [{'Source': 'Internet Movie Database', 'Value': '7.8/10'}, {'Source': 'Rotten Tomatoes', 'Value': '88%'}, {'Source': 'Metacritic', 'Value': '75/100'}]\n",
      "Metascore: 75\n",
      "imdbRating: 7.8\n",
      "imdbVotes: 879,510\n",
      "imdbID: tt0120338\n",
      "--------------------------------------------------\n",
      "Poster of this movie can be downloaded. Enter \"yes\" or \"no\": no\n",
      "Save the movie info in a local database? Enter \"yes\" or \"no\": yes\n",
      "Please enter a name for the database (extension not needed, it will be added automatically): movies\n",
      "Record already found. No update made.\n"
     ]
    }
   ],
   "source": [
    "title = input('\\nEnter the name of a movie (enter \\'quit\\' or hit ENTER to quit): ')\n",
    "if len(title) < 1 or title=='quit': \n",
    "    print(\"Goodbye now...\")\n",
    "else:\n",
    "    search_movie(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print the database contents again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Titanic', 1997, 194, 'USA', 75.0, 7.8)\n",
      "('Jumanji', 1995, 104, 'USA', 39.0, 6.9)\n",
      "('To Kill a Mockingbird', 1962, 129, 'USA', 87.0, 8.3)\n"
     ]
    }
   ],
   "source": [
    "print_database('movies.sqlite')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the database content into an Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_in_excel('test.xlsx','movies.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Country</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>IMDBRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>1997</td>\n",
       "      <td>194</td>\n",
       "      <td>USA</td>\n",
       "      <td>75</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>104</td>\n",
       "      <td>USA</td>\n",
       "      <td>39</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "      <td>1962</td>\n",
       "      <td>129</td>\n",
       "      <td>USA</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Title  Year  Runtime Country  Metascore  IMDBRating\n",
       "0                Titanic  1997      194     USA         75         7.8\n",
       "1                Jumanji  1995      104     USA         39         6.9\n",
       "2  To Kill a Mockingbird  1962      129     USA         87         8.3"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_excel('test.xlsx')\n",
    "df"
   ]
  }
 ],
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