Implemented elevator data service and tests for ML

[gonzayb]

Implement Elevator Data Service and ML-ready data collection

• Added app/elevator_api.py: Flask API with endpoints to record demand and elevator state
• Added test/test.py: pytest tests for data integrity and API endpoints
• Created SQLite schema and ml_training_data view for a ML project

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 45%

Reasoning: The code in this pull request is quite sophisticated and implements a comprehensive elevator data service with support for machine learning data generation, analytics, and a corresponding test suite. It demonstrates consistent design patterns and thoughtful implementation across multiple layers: the Flask web API, SQLite schema migrations, data integrity validation, and unit testing using pytest.

Although large parts of the code exhibit clean structure and standard conventions that are achievable by skilled developers or advanced AI systems (especially given the verbose and formulaic comments), there is also evidence of personalized style and occasional minor idiosyncrasies that suggest human authorship — such as inconsistent comment formatting (e.g., "#ojo", "#Checkk", "#STate success") and small typos that AI systems typically avoid (e.g., "Checkk", "Optioinal", "Reent", "endopint"). These signal unstructured human tendencies more than AI's structured outputs.

Key Indicators:

• ✅ High functionality and consistent architectural design (could indicate AI or experienced developer)
• ✅ Extensive use of testing including test coverage of edge cases like validation and data filtering (slightly favors human diligence)
• ✅ Well-structured SQLite schema and ML view (possible AI generation)
• ❌ Inconsistent and quirky comment styles (e.g., use of Spanish/Taglish words like "ojo", inconsistent casing and spacing)
• ❌ Spelling and grammatical quirks (e.g., "Optioinal", "Checkk", nonsensical "#Weekday morning (7-9) and evening (5-7) based on my country's business hours" lacks context clarity, which isn’t typical of AI)
• ✅ In-line comments sometimes feel redundant or auto-generated, pointing toward AI autocomplete (e.g., “#Saves demand events when someone calls the elevator” restating the function purpose)

Overall, the submission straddles the line between an AI-assisted but human-authored submission. While certain formatting and structure could have been scaffolded by an AI (or with assistance like GitHub Copilot), the quirks and human-like reasoning throughout (especially in tests) suggest a strong human component.

Key Indicators:

• Comment inconsistency and personal tone (likely human)
• Minor spelling/grammar imperfections (human)
• Comprehensive test suite with realistic assumptions (human leaning)
• Structured code and predictably worded doc-comments (AI leaning)
• Use of copy-and-paste style SQL in both source and test (neutral)

Thus, I lean mildly toward human-authored with possible AI assistance.

✅ No strong indicators of AI generation detected