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Description:

aixplainpipelines 0.0.4

aiXplain Pipelines
aiXplain Pipelines enables python programmers to add AI functions
to their software.
An aiXplain pipeline is a directed acyclic graph (DAG) of AI functions built using aiXplain's designer UI. An AI function is a data processing step that relies on a machine learning model to execute. An example of an AI function is speech recognition or machine translation. It helps you process your data by calling a series of functions as defined in the DAG, abstracting the orchestration by providing a simple python function call.
aiXplain has a collection of AI models for each AI function. You can explore the collection of our AI models by using the discover feature of our platform's website.
aiXplain Pipeline Designer DAG
The image below shows a sample aiXplain pipeline built for subtitling video files. The description of the pipeline can be found in the documentation.

Installation
pip install aixplain-pipelines

User Guide
In order to use aiXplain pipelines, you need to create an account in aiXplain platform. Follow the code samples listed below to get started.
Code Samples and Demos
aixplain-pipelines provides python APIs to call AI workflows you can build with aiXplain designer.
Generic Snippet
from aixplain_pipelines import Pipeline

api_key=<API_KEY>

pipe = Pipeline(api_key=api_key)

path=<DATA_URL>
response = pipe.run(data=path)

API_KEY can be obtained by creating a pipeline in pipeline designer through the aiXplain platform UI.
For DATA_URL generate a http(s) link to your image or video file to process, though text input can be directly supplied to data parameter in the run function without needing a URL.
Information on how to generate the API_KEY can be found in the subtitle generation pipeline sample video.
Subtitle Generation
This demo creates a .srt file for the supplied video using aixplain-pipelines. Follow the instructions in the documentation.
Developer Guide
Follow the developer guide documentation.
Support
Raise issues for support in this repository.
Pull requests are welcome!