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README.md
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# Data-intensive-systems
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## Members
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1. Reem
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## Usage
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Installation of [gardio](https://www.gradio.app) to build machine learning apps in Python:
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```pip install gradio```
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Run the app with ```python3 app.py```
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- Local development, switch to [cscloud](https://cscloud.lnu.se/project/) if needed.
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- Meetings as and when needed
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---
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# Data-intensive-systems
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Group project to develop in a machine learning setting. The developed [gradio](https://www.gradio.app) app for movement assessment is automatically deployed to the [synchronized huggingface space](https://huggingface.co/spaces/Bachstelze/github_sync).
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## Members
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1. Reem
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## Usage
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Installation of [gardio](https://www.gradio.app/docs/gradio/interface) to build machine learning apps in Python:
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```pip install gradio```
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Run the app with ```python3 app.py```
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- Local development, switch to [cscloud](https://cscloud.lnu.se/project/) if needed.
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- Meetings as and when needed
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### Week 2 - first linear regression model development and deployment
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- Testing pickled models with test/test_model.py
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### Outlook
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Input the raw video into a [reasoning vision model](https://huggingface.co/blog/nvidia/nvidia-cosmos-reason-2-brings-advanced-reasoning) and develop the workflow with [daggr](https://huggingface.co/blog/daggr) for gradio.
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