feat(logging): add centralized logger factory and replace print statements#21
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Kunal-Somani wants to merge 1 commit into
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feat(logging): add centralized logger factory and replace print statements#21Kunal-Somani wants to merge 1 commit into
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- Add utils/logger.py: a get_logger() factory that configures the root handler once via LOG_LEVEL env var, ensuring consistent structured output both locally and on Cloud Run - Refactor app.py: move logging init to module level so it applies on all deployment paths, not just __main__ - Refactor utils/utils.py: replace all print() calls with logger.info() / logger.debug() / logger.error() for production-grade observability
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Mar 21, 2026
- Remove coloredlogs.install() calls from EmotionsAnalysisImp.__init__() and FirebaseImp.__init__() — reinstalling the log handler on every object instantiation was causing duplicate log entries - Replace instance-level self.logger with module-level logger via get_logger(__name__) for consistency with utils/logger.py (PR ruxailab#21) - Switch all f-string log calls to % formatting (Python logging best practice — avoids string interpolation when log level is disabled) - Add debug log to FirebaseImp._initialize_app() for the already- initialized path - Add logger.info() to upload_to_firestore() on successful Firestore write - Remove unused logging and coloredlogs imports from both files
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Adds schemas/multimodal_output_schema.py — a Pydantic wrapper around
GetEmotionPercentagesResponse that standardizes the facial API output
for consumption by the MultimodalSentimentEngine in sentiment-analysis-api.
Key additions:
- FacialEmotionOutput.from_response(): converts GetEmotionPercentagesResponse
into a validated Pydantic model with field-level range constraints (0-100%)
- to_multimodal_dict(): produces the {emotion_label: percentage} dict
format expected by MultimodalSentimentEngine.analyze(facial_emotions=...)
- dominant_emotion(): returns the top emotion and its confidence as a
(label, float) tuple — useful for logging and single-label summaries
This is the integration boundary between the two RUXAILAB sentiment repos:
the facial API produces GetEmotionPercentagesResponse, FacialEmotionOutput
converts it, and the multimodal engine fuses it with text and prosody.
Closes the gap identified in PR ruxailab#21 and ruxailab#22 where the two pipelines
had no shared contract.
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Summary
The codebase had two observability gaps:
app.pyonly configured logging insideif __name__ == "__main__",meaning Cloud Run deployments received zero logging output — every
logger.info()call invideo_routes.pysilently did nothing in production.utils/utils.pyused rawprint()statements which cannot be filteredby log level, routed to external sinks, or controlled via environment variables.
Changes
utils/logger.py(new file)get_logger(name)factory function_configuredguard flagLOG_LEVELenvironment variable (default:INFO)stdoutin a structured format compatible with Cloud Run log ingestionapp.py(modified)coloredlogsdependency (was only active in__main__block)get_logger(__name__)at module levelutils/utils.py(modified)print()calls withlogger.info(),logger.debug(), andlogger.error()delete_video()andsplit_video_into_clips()delete_video()now useslogger.error()with%sformattingTesting
Confirmed structured log output appears at startup:
Motivation
This directly supports the Unified Logging and Traceability System outlined in the GSoC 2026 ideas — establishing a structured, environment-aware logging foundation that future traceability features can build on.