Implement elevator data collection system , CRUD API and tests

[darthfelipe21]

Hi,

I already finished the process, I implemented a Flask-based REST API for collecting elevator demands and states for ML, per requirements.

Key features:

• CRUD endpoints for "ElevatorDemand" and "ElevatorState" ("POST", "GET", "PUT", etc).
• SQLite database with "elevator_demands" and "elevator_states".
• Indices on "timestamp" and "floor" for ML query optimization.
• 20/20 tests passing, covering success and error cases.

Tested endpoints in Postman, verified data and indices in DBeaver.

Ready for your review.

Let me know anything you need.

Regards

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 85%

Reasoning: The structure, language, thoroughness, and consistency throughout this pull request strongly suggest that it was AI-generated, likely with human oversight or minor editing. The documentation is formulaic, neutral in tone, and highly comprehensive while avoiding any complex idiomatic touches typically inserted by humans ad hoc.

Key Indicators:

• Descriptive yet formulaic PR description: The phrasing (“I already finished the process…”, “Ready for your review…”) is somewhat generic and consistent with ChatGPT-style completions. The bullet points are framed with clarity and predictability seen in LLM-generated summaries.

• Coverage and completeness: The code, especially in test cases, exhibits full CRUD coverage with descriptive and pass/fail conditions, resembling textbook or prompt-driven AI behavior geared toward completeness. Human-authored test suites often vary more in thoroughness and comment verbosity.

• Documentation and naming: The naming conventions such as to_dict, test_create_state_invalid_floor, and informative error messages are textbook clean and follow best practices that AI models tend to follow strictly, often more so than many human developers would in early-stage projects.

• Embedded comments: Many comments present in the main.py and test files take the form of declarative statements of intent (e.g., “# Validate vacant”, “# Check that an invalid vacant value fails with a 400”) — a common AI-generated artifact when producing code with in-line documentation, often mapped 1:1 from prompt entries.

• Formatting regularity: The styling of the docstring comments, imports, test naming patterns, and the JSON response formatting are consistent in quality and regularity that is difficult to sustain manually across such a large codebase without tooling or automation.

• Unusual absence of human idiosyncrasies: No obvious grammatical errors, typos, or inconsistent naming — which are commonly observed in even well-written manually created PRs.

While there are signs of possible minor human intervention (e.g., confirming tests passed, mentioning real tools like DBeaver), the overall project appears highly structured, methodically commented, and fits the AI-generated profile.

Thus, the pull request is most likely generated by AI, possibly enhanced or reviewed by a developer.

⚠️ Warning: High confidence that this PR was generated by AI