It is a developed RESTful API based on Flask. The interface supports methods for searching for a movie by id, title, and release year, as well as issuing recommendations at the user's request. This application works with a database of IMBD movies taken from the site https://datasets.imdbws.com/title.basics.tsv.gz

The program consists of two files. main.py launches Flask application and calls file data retrieval methods.py. Optionally, the program can be run from the Docker container, having previously built the image, and without it.

If you don't need to run this application from Docker, you must skip the below commands and run the following commands after them

• docker build -t movies .
• docker run -d -p 5000:5000 movies

The application is launched in the standard way using the python command, if you use any command line or using the launch buttons in your development environment. The next step is to check the server is working. Alternatively, you can simply write 127.0.0.1:5000 to your address bar. You will see the Hello message in a simple html page.

In this application, three GET requests and one POST request are implemented according to the application description at the beginning. This is described in the main.py file.

• GET by id

  

• GET by title

  

• GET by release year

  

• POST for recommendations (with using any software, for example, on the screen it can be seen POST request in Chrome extension that is called as "RESTED")

  The request body contains a JSON document containing a non-empty list of binary movie ratings, that is, (movie_id, rating) pairs. For example, 127.0.0.1:5000/suggest/3 means a request to issue 3 recommendations. The response body contains a JSON document containing a list of H recommended movies in the format (movie_id, number). Movie rating (predicted rating) is generated randomly in the file methods. The list of films is sorted in descending order of rating. The recommendations cannot include those films that the user has already rated (that is, those that came in the request body).

  

• Curl GET by id

  $ curl -X GET 127.0.0.1:5000/movie_id/tt0000022
  

  Request result

  {
    "tconst": "tt0000022",
    "titleType": "short",
    "primaryTitle": "Blacksmith Scene", 
    "originalTitle": "Les forgerons",   
    "isAdult": "0",
    "startYear": "1895",
    "endYear": "\\N",
    "runtimeMinutes": "1",
    "genres\n": "Documentary,Short\n"   
  }
  

• Curl GET by title

  $ curl -X GET 127.0.0.1:5000/movie/Carmencita
  

  Request result

  [
    {
      "tconst": "tt0000001",
      "titleType": "short",
      "primaryTitle": "Carmencita",
      "originalTitle": "Carmencita",
      "isAdult": "0",
      "startYear": "1894",
      "endYear": "\\N",
      "runtimeMinutes": "1",
      "genres\n": "Documentary,Short\n"
    },
    {
      "tconst": "tt0453643",
      "titleType": "short",
      "primaryTitle": "Carmencita",
      "originalTitle": "Carmencita",
      "isAdult": "0",
      "startYear": "1897",
      "endYear": "\\N",
      "runtimeMinutes": "\\N",
      "genres\n": "Short\n"
    }
  ]
  

• Curl GET by release year

  $ curl -X GET 127.0.0.1:5000/year/1894
  

  Request result

  {
    "tconst": "tt8742740",
    "titleType": "short",
    "primaryTitle": "Chat En Chute Libre",
    "originalTitle": "Chat En Chute Libre",
    "isAdult": "0",
    "startYear": "1894",
    "endYear": "\\N",
    "runtimeMinutes": "\\N",
    "genres\n": "Short\n"
  }
  

• Curl POST for recommendations

  $ curl -d '{ "likes": { "tt0000001": 1, "tt0000002": 0, "tt00000010": 0 }}' -H "Content-Type: application/json" -X POST 127.0.0.1:5000/suggest/5
  

  Requset result

  {
    "ratings": {
      "tt5368654": 0.8040031576345136,
      "tt8339638": 0.6780302124054454,
      "tt8645720": 0.37887919587478647,
      "tt0459963": 0.3745079668387131,
      "tt0173364": 0.33233496168265964,
      "tt10933950": 0.037933429676038855
    }
  }