add import option for Netflix watch history

[4ndriH]

This adds an import option for the Netflix watch history CSV.
The CSV only provides names of the media watched, which has to be matched against TMDb. The matching process currently looks for exact matches which could be improved by utilizing something like min edit distance in the future. I am pointing this out because Netflix does sometimes have small discrepancies when it comes to episode names.
For example the episodes of the tv show Blacklist can additionally contain a number on Netflix, indicating where the blacklister is on Reddingtons list.

[gemini-code-assist]

Summary of Changes

Hello @4ndriH, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a significant new integration, allowing users to import their Netflix watch history. By processing CSV files exported from Netflix, the system can now automatically identify and log watched movies and TV series, including specific seasons and episodes, by matching them against TMDb. This enhances the platform's ability to provide a comprehensive tracking solution for user media consumption.

Highlights

• New Netflix Watch History Import: Introduced a new feature allowing users to import their Netflix watch history from a CSV file, enabling tracking of watched movies and TV shows.
• TMDb Matching Logic: Implemented logic to parse Netflix CSV titles and match them against TMDb for accurate media identification, handling various title formats including season and episode details.
• Current Matching Limitations: The initial implementation uses exact title matching, with a note that future improvements could include fuzzy matching (e.g., min edit distance) to better handle minor discrepancies in Netflix episode names.
• Comprehensive Media Tracking: The importer creates database entries for movies, TV series, individual seasons, and episodes, including their watched dates and completion statuses.

Changelog

• src/integrations/imports/netflix.py
  • Added a new Python module containing the core logic for parsing Netflix watch history CSVs, matching titles to TMDb, and preparing data for database insertion.
• src/integrations/tasks.py
  • Updated to include the new netflix module and registered a Celery task import_netflix to handle asynchronous processing of Netflix CSV imports.
• src/integrations/tests/imports/test_netflix.py
  • Added unit tests for the NetflixImporter class, covering various scenarios such as movie and TV show imports, date parsing, and series completion status determination.
• src/integrations/tests/mock_data/import_netflix.csv
  • Added a sample CSV file containing mock Netflix watch history data to be used in tests.
• src/integrations/urls.py
  • Added a new URL pattern /import/netflix to route requests for Netflix watch history imports to the corresponding view function.
• src/integrations/views.py
  • Added a new view function import_netflix to handle the upload of Netflix watch history CSV files and queue the import task for processing.
• src/static/img/netflix-logo.svg
  • Added the SVG image file for the Netflix logo, to be used in the user interface.
• src/templates/users/import_data.html
  • Updated the import data template to include a new section for Netflix import, providing a user interface element for uploading CSV files.
• src/users/models.py
  • Updated the get_import_tasks method in the user model to include 'netflix' as a recognized import source.
• src/users/templatetags/user_tags.py
  • Added Netflix to the SOURCE_INFO dictionary, enabling the display of its name and logo in the application's UI.

Activity

• No explicit activity (comments, reviews, or progress updates) has been recorded for this pull request yet.

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[gemini-code-assist]

Code Review

This pull request introduces a new importer for Netflix watch history from a CSV file. While a great addition, it has potential Denial of Service (DoS) vulnerabilities. Specifically, handling large CSV files by reading the entire file into memory and performing unbounded external API calls and database operations per row can lead to resource exhaustion. Recommendations include implementing streaming file processing and limiting the number of entries processed per task. Additionally, consider improving error handling for resilience, fixing error message formatting, and refactoring for better code clarity and efficiency.