VIVPRO HOME TASK

Task 1:

Data Processing

The provided dataset of songs in JSON format has been processed to incorporate the data into an SQLite database. The steps undertaken were as follows:

• The JSON file named "playlist.json" was imported and processed, resulting in the creation of a Python dictionary.
• All the keys and values within the dictionary were extracted.
• To accommodate the data, an SQLite database named "songs" was established. This database would contain a table named "playlist".
• The data from the dictionary was systematically inserted into the "playlist" table using an iterative approach.
• To conclude the process, the connection to the database was properly closed.

Instructions to run

Open python terminal in same directory and type below command

py dataProcess.py

Task 2:

API to serve normalised data [Backend]

The database that was created in the previous task will now be utilized to respond to requests through an API built with Flask. Here's how the process was carried out:

• The Flask framework was imported in order to set up an API.
• A connection was established to the "songs" database that was created in the earlier task.
• A route was defined for the index page. This route is responsible for showcasing the data of all the songs in the playlist. The data is presented in JSON format.
• Another route was set up for the search page. This route enables the display of all attributes of a song.
• This display is based on the input provided through a form, which processes the information according to the title of the song.
• Lastly, a route was established for the rating page. This particular route allows users to assign ratings to the songs present in the playlist.

Instructions to run

Open the Python terminal in the same directory and enter the following command:

py backendAPI.py

Then, open any web browser and navigate to the following URL to access the index page:

http://localhost:5000/

To access the search page, use the following URL:

http://localhost:5000/search

To access the rating page, use the following URL:

http://localhost:5000/rating

Compatibility

This code has been developed and tested well in Windows in the virtual python environment.

by Siddharth Mishra