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Add an IT suite that verifies required app files exist, starts the Flask server, and submits sample text to confirm sign-word output. Wire it into GitHub Actions with Java/Python/spaCy/torch setup so NLP model changes cannot silently break the running application. Support configurable PORT for CI/local port conflicts and pin torch/spaCy for Python 3.9 compatibility.
Use an optional IT_LIGHT_PIPELINE mode so CI only loads tokenize/pos/lemma instead of the full heavy default stanza stack. Pre-download models in a dedicated workflow step and stream server logs on failure for debugging.
Drain Flask/server logs on a background thread so verbose NLP print output during POST cannot fill the subprocess pipe and hang the server.
main removed the committed LFS jar; download and extract the parser in the workflow instead, and stop requiring it as a tracked repo file.
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Summary
tests/integration/test_app_running.py) that verifies required app files exist, starts the Flask server, and submits sample text (Hello world) to confirm sign-word output..github/workflows/integration-tests.yml) that sets up Java/Python/spaCy/torch, downloads Stanza models, fetches the Stanford parser at CI runtime (no longer committed onmain), and runs the IT suite.PORT, optionalIT_LIGHT_PIPELINEfor lighter CI stanza loading (default app behavior unchanged),torch>=2.0,<2.6for stanza/PyTorch compatibility, Dockerfile/spacy pin for Python 3.9.Why
NLP/model changes should not silently break the running application. This CI gate fails the build if the app cannot start or process text.
Test plan
PORT=5055 IT_LIGHT_PIPELINE=1 python tests/integration/test_app_running.pyadd-integration-tests(post-main merge, with runtime Stanford parser download)IT_LIGHT_PIPELINE(default full pipeline unchanged)