refactor(workflow-operator): rename PythonCodegenBase to HuggingFaceCodegenBase for clarity

[PG1204]

What changes were proposed in this PR?

Pure internal rename: PythonCodegenBase -> HuggingFaceCodegenBase (object, file, and spec).

Despite the generic name, this object is HF-specific, it lives in .../huggingFace/codegen/, is used only there, and is the shared base of the Hugging Face per-task TaskCodegens. The old name led a reviewer to ask whether the HF logic belonged elsewhere (comment on #6972); the new name matches the sibling codegens and HuggingFaceInferenceOpDesc.

Also updated the references in HuggingFaceInferenceOpDesc.scala, ImageTaskCodegen.scala, and TaskCodegen.scala. Behavior-neutral: no change to the generated Python, workflow JSON, or any public API; git tracks both files as renames.

Any related issues, documentation, discussions?

Closes #7140
Follow-up to the review discussion on #6972.

How was this PR tested?

No new tests as this is a rename, and existing coverage confirms it's behavior-neutral.

sbt "WorkflowOperator/testOnly org.apache.texera.amber.operator.huggingFace.*"

The full HF package passes (124 tests), including PythonCodeRawInvalidTextSpec, which
py-compiles the generated Python of all 117 operators — confirming the generated output is
unchanged. scalafmt clean.

Was this PR authored or co-authored using generative AI tooling?

Co-authored with Claude Opus 4.7 in compliance with ASF.

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• Contributors with relevant context: @anishshiva7, @ELin2025, @Ma77Ball
  You can notify them by mentioning @anishshiva7, @ELin2025, @Ma77Ball in a comment.

[PG1204]

/request-review @xuang7

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 79.58%. Comparing base (084309c) to head (05cbd1e).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #7143      +/-   ##
============================================
- Coverage     79.59%   79.58%   -0.01%     
  Complexity     3838     3838              
============================================
  Files          1159     1159              
  Lines         46122    46121       -1     
  Branches       5127     5127              
============================================
- Hits          36709    36707       -2     
  Misses         7787     7787              
- Partials       1626     1627       +1     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	77.42% <ø> (ø)		Carriedforward from 084309c
amber	73.11% <ø> (-0.01%)	⬇️	Carriedforward from 084309c
computing-unit-managing-service	20.49% <ø> (ø)
config-service	66.66% <ø> (ø)
file-service	67.21% <ø> (ø)
frontend	83.05% <ø> (-0.01%)	⬇️
notebook-migration-service	78.94% <ø> (ø)
pyamber	97.36% <ø> (ø)		Carriedforward from 084309c
workflow-compiling-service	26.31% <ø> (ø)

*This pull request uses carry forward flags. Click here to find out more.

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[github-actions]

⚠️ Benchmark changes need a look

🟢 2 better · 🔴 3 worse · ⚪ 10 noise (<±5%) · 0 without baseline

Compared against main 084309c benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🔴	bs=10 sw=10 sl=64	388	0.237	25,134/31,512/31,512 us	🔴 +16.8% / 🔴 +99.6%
🟢	bs=100 sw=10 sl=64	785	0.479	124,966/154,868/154,868 us	🟢 -7.2% / 🔴 +40.4%
⚪	bs=1000 sw=10 sl=64	910	0.555	1,097,555/1,155,595/1,155,595 us	⚪ within ±5% / 🔴 +9.5%

Baseline details

Latest main 084309c from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	388 tuples/sec	452 tuples/sec	771.62 tuples/sec	-14.2%	-49.7%
bs=10 sw=10 sl=64	MB/s	0.237 MB/s	0.276 MB/s	0.471 MB/s	-14.1%	-49.7%
bs=10 sw=10 sl=64	p50	25,134 us	21,519 us	12,626 us	+16.8%	+99.1%
bs=10 sw=10 sl=64	p95	31,512 us	30,433 us	15,786 us	+3.5%	+99.6%
bs=10 sw=10 sl=64	p99	31,512 us	30,433 us	19,344 us	+3.5%	+62.9%
bs=100 sw=10 sl=64	throughput	785 tuples/sec	770 tuples/sec	971.56 tuples/sec	+1.9%	-19.2%
bs=100 sw=10 sl=64	MB/s	0.479 MB/s	0.47 MB/s	0.593 MB/s	+1.9%	-19.2%
bs=100 sw=10 sl=64	p50	124,966 us	130,088 us	103,463 us	-3.9%	+20.8%
bs=100 sw=10 sl=64	p95	154,868 us	166,908 us	110,296 us	-7.2%	+40.4%
bs=100 sw=10 sl=64	p99	154,868 us	166,908 us	118,690 us	-7.2%	+30.5%
bs=1000 sw=10 sl=64	throughput	910 tuples/sec	928 tuples/sec	1,001 tuples/sec	-1.9%	-9.1%
bs=1000 sw=10 sl=64	MB/s	0.555 MB/s	0.566 MB/s	0.611 MB/s	-1.9%	-9.2%
bs=1000 sw=10 sl=64	p50	1,097,555 us	1,075,976 us	1,008,988 us	+2.0%	+8.8%
bs=1000 sw=10 sl=64	p95	1,155,595 us	1,114,864 us	1,055,260 us	+3.7%	+9.5%
bs=1000 sw=10 sl=64	p99	1,155,595 us	1,114,864 us	1,074,689 us	+3.7%	+7.5%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,515.79,200,128000,388,0.237,25133.66,31511.98,31511.98
1,100,10,64,20,2547.10,2000,1280000,785,0.479,124965.74,154868.19,154868.19
2,1000,10,64,20,21975.08,20000,12800000,910,0.555,1097555.00,1155595.05,1155595.05