feat: Add Python Virtual Environment Support: Uninstalling User Defined Packages (#5035)

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### What changes were proposed in this PR?
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This PR is an extension of PR https://github.com/apache/texera/pull/4484
and #4902. Previously, we introduced support for creating Python Virtual
Environments (PVEs) with system-level dependencies preinstalled, along
with support for installing user-defined packages. This PR extends that
functionality by allowing users to uninstall user-installed packages
from their PVEs.

### Any related issues, documentation, discussions?
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This change is part of ongoing efforts to support environment isolation
and reproducibility within Texera. Related issue includes
https://github.com/apache/texera/issues/4296. This PR closes sub-issue
#4466.

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Tested Manually and PveResourceSpec test file updated.

To test:

On CU click "+" Python Environments.
Input environment name.
Input package name and version.
Click "OK" and wait for pip logs.
To delete click on "Delete Icon" and click "OK" 

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Co-authored using: ChatGPT (OpenAI)

---------

Co-authored-by: Kunwoo (Chris) <143021053+kunwp1@users.noreply.github.com>
7 files changed
tree: 1a9d2bd695d397b832a28d9b1c071622aa30d092
  1. .github/
  2. .run/
  3. access-control-service/
  4. agent-service/
  5. amber/
  6. bin/
  7. common/
  8. computing-unit-managing-service/
  9. config-service/
  10. docs/
  11. file-service/
  12. frontend/
  13. licenses/
  14. licenses-3rd-party-code/
  15. project/
  16. pyright-language-service/
  17. sql/
  18. workflow-compiling-service/
  19. .asf.yaml
  20. .dockerignore
  21. .gitattributes
  22. .gitignore
  23. .jvmopts
  24. .licenserc.yaml
  25. .scalafix.conf
  26. .scalafmt.conf
  27. AGENTS.md
  28. build.sbt
  29. CLAUDE.md
  30. codecov.yml
  31. CONTRIBUTING.md
  32. DISCLAIMER
  33. LICENSE
  34. NOTICE
  35. README.md
  36. SECURITY.md
README.md

Apache Texera (Incubating) is an open-source platform for human-AI collaborative data science using visual workflows. It enables human analysts to construct, execute, and refine data analysis tasks through an intuitive GUI, assisted by AI agents that understand natural-language instructions. Texera is well suited for a wide range of applications, including “AI for Science,” by making advanced AI and data science capabilities accessible to a broader community. It can run on a laptop for local use or be deployed in the cloud to support scalable processing of large datasets.

The platform has the following key features:

  • Natural-language data science through AI agents
  • Intuitive GUI-based workflows for data science
  • Real-time collaboration for workflow editing and execution
  • Runtime debugging and interactive workflow execution
  • Language-agnostic workflow runtime, native support for Python and Java
  • Parallel backend engine for scalable big-data processing
  • Separation of compute and storage for flexible cloud deployment

texera-screenshot

Citation

Please cite Texera as


@article{DBLP:journals/pvldb/WangHNKALLDL24, author = {Zuozhi Wang and Yicong Huang and Shengquan Ni and Avinash Kumar and Sadeem Alsudais and Xiaozhen Liu and Xinyuan Lin and Yunyan Ding and Chen Li}, title = {Texera: {A} System for Collaborative and Interactive Data Analytics Using Workflows}, journal = {Proc. {VLDB} Endow.}, volume = {17}, number = {11}, pages = {3580--3588}, year = {2024}, url = {https://www.vldb.org/pvldb/vol17/p3580-wang.pdf}, timestamp = {Thu, 19 Sep 2024 13:09:37 +0200}, biburl = {https://dblp.org/rec/journals/pvldb/WangHNKALLDL24.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }