Knowledge Seeker

A television episode browser inspired by Frinkiac. This one just happens to be configured for all three seasons of Avatar: the Last Airbender, one of the greatest American cartoons of all time. A public instance of Knowledge Seeker is accessible at atla.pictures.

Like Frinkiac, KS indexes episodes by their plain text subtitles, and attempts to determine which frames are "most significant" by examining the differences in the color values of the pixels. Unlike Frinkiac, KS is open source and was written in Python by a bored and nostalgic college kid.

Knowledge Seeker is a CGI program built on Python 3 and Flask. It uses NumPy to read video files, and (of course) ffmpeg to transcode them to GIF animations.

Setup

1. Install KS with pip as you would any ordinary Python package.
2. Configuration takes place within the Flask app instance folder (henceforth referred to as $INSTANCE), the precise location of which depends on wherever pip installed the package. To locate this, you could simply run the app with FLASK_APP=knowledgeseeker flask run, and look for the inevitable failure to read the configuration file.
3. All parameters are stored in $INSTANCE/config.py. sample_config.py contains representative values and some documentation. All paths are relative to $INSTANCE.
4. config.py points to the library file, which is a JSON metadata collection of all episodes and seasons. Seasons are defined by a brief slug, a proper name, (optionally) a path to an icon, and a list of episodes. Episodes are defined by a slug, a name, a video file, and a subtitle file. Video files must be in a format readable by NumPy and Ffmpeg; anecdotally, the x264 codec seems to strike a good balance between storage requirements and transcode speed. Subtitles must be in srt format. All paths are relative to the directory containing the library file (henceforth referred to as $LIBRARY).
5. With the necessary data in the proper locations, use FLASK_APP=knowledgeseeker flask read-library to build the massive database of episodes and snapshots. (This takes a very long time.)
6. Use FLASK_APP=knowledgeseeker FLASK_ENV=development flask run to run the app in debug mode with Flask's built-in Werkzeug server. For production, use the recommended configuration for Flask apps: a WSGI server to run the app behind a hardened reverse proxy.