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We can modify the /predict endpoint so that when a video comes in, it is converted to audio and text as well, as per the user's request (this would involve thinking about the front-end UI first, and what is the most intuitive way to present options to the investigator before tackling the back-end). This issue deals with the front-end.
In the back-end, this would be done by adding a clause to the prediction pipeline that is something like:
if file type is video and audio models are selected:
1. convert files to audio
2. run predictions on audio models.
Things to think about:
For step 2, the same create_new_prediction function can be called.
Would recommend abstracting this clause to a helper function because the create_new_prediction function for readability
There is a model_type parameter in the create_new_prediction function. Is this still necessary?
The user could select both video and audio models, so video prediction should still be run in that case.
The text was updated successfully, but these errors were encountered:
We can modify the
/predict
endpoint so that when a video comes in, it is converted to audio and text as well, as per the user's request (this would involve thinking about the front-end UI first, and what is the most intuitive way to present options to the investigator before tackling the back-end). This issue deals with the front-end.In the back-end, this would be done by adding a clause to the prediction pipeline that is something like:
Things to think about:
create_new_prediction
function can be called.create_new_prediction
function for readabilitymodel_type
parameter in thecreate_new_prediction
function. Is this still necessary?The text was updated successfully, but these errors were encountered: