Google Calendar via ChatGPT. YouTube video link:
https://github.com/openai/openai-cookbook
Initially think Google Calendar's APIs might be best way to go. Negative is they require a google cloud project aka billing, and the examples have Python 2 style prints, which means old and likely no more maintained.
https://developers.google.com/calendar/api/guides/overview https://developers.google.com/calendar/api/guides/create-events#python
Better way might be to just use Python's built-in Calendar module, and then export to some kind of format that can be imported into Google Calendar.
https://docs.python.org/3/library/calendar.html
Perhaps iCalendar is the way to go? Used for both Mac and Google and has a convenient python module.
https://icalendar.readthedocs.io/en/latest/usage.html
This is the basic code required:
# I want to workout on March 7th, 2023 from 2pm to 4pm
event = Event()
event.add('summary', 'Workout')
event.add('dtstart', datetime(2023,3,7,12,0,0))
event.add('dtend', datetime(2023,3,7,14,0,0))
calendar.add_component(event)
We don't want to use up that many tokens on every request, so lets make pseudo-code that the model will use to generate the calendar events.
# I want to workout on March 7th, 2023 from 2pm to 4pm
Workout, datetime(2023,3,7,12,0,0), datetime(2023,3,7,14,0,0))
Lets make a bunch of examples of this format, and then use them as context for the model to generate more.
# I want to workout on March 7th, 2023 from 2pm to 4pm
Workout, datetime(2023,3,7,12,0,0), datetime(2023,3,7,14,0,0))
# I want to go to the dentist on March 8th, 2023 from 9am to 10am
Dentist, datetime(2023,3,8,9,0,0), datetime(2023,3,8,10,0,0))
Testing out if lower sampling frequency and single channel also work for Whisper speech to text. Since it will be a smaller file and thus faster/lower cost.
w/ 44kHz, 2 channel
- Transcription time: 1.84s w/ 4kHz, 1 channel
- Transcription time: 1.02s
conda create --name calgpt python=3.10
conda activate calgpt
pip install -r requirements.txt