Elevator resting floor model solution

[macphailmagwira]

This PR introduces a solution to optimize elevator resting positions in buildings based on historical data. The system begins the process of predicting the optimal resting floor for elevators between calls to minimize wait times and improve efficiency.

THIS IS NOT A COMPLETE SOLUTION BUT A FIT FOR PURPOSE SOLUTION ENCOMPASSING,

• Thought process, working and decision making

• Data modeling

• Feature extraction

COMPLETE THOUGHT PROCESS AND WORKING DOCUMENTATION STORED IN ROOT "readme.md" in project files

Key Features

• RESTful API built with Flask for elevator system management and data collection
• Feature extraction for CatBoost floor prediction
• Comprehensive Test Suite with unit and integration tests
• CI/CD Pipeline with GitHub Actions

Technical Details

• Backend: Python 3.8+, Flask, SQLAlchemy
• ML Framework: CatBoost (handles categorical features natively)
• Database: SQLite (dev)

API Endpoints

• POST /api/buildings - Create a new building
• POST /api/buildings/<building_id>/elevators - Add elevator to building
• POST /api/elevators/<elevator_id>/call - Record elevator call
• GET /api/elevators/<elevator_id>/status - Get elevator status
• GET /api/buildings/<building_id>/ml-features - Export ML training data

Testing

Run

python -m pytest app_tests.py -v

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 75%

Reasoning: The pull request content exhibits characteristics of both human and AI involvement. The implementation demonstrates a sophisticated degree of engineering knowledge—custom Flask APIs, complex test cases, thoughtful modeling and architectural decisions—which leans toward a human author, especially due to the holistic nature of the solution and coding practices. However, the accompanying README, PR description, and architectural thinking are unusually verbose, use consistently structured subheaders and lists, and sometimes exhibit unnatural phrasing or repetition—signs commonly associated with LLM-generated content.

Specifically, the "Thought Process" section in the README and technical descriptions contain an almost pedagogical tone, going systematically through modeling steps, methodology, and justification for model choice (CatBoost), with polished transitions and elaborate elaboration of even minor observations. This kind of detailed nesting of ideas, with an aim to anticipate reader understanding and logical flow, is typical of AI-generated text when instructed to explain concepts methodically.

The code itself includes extensive test coverage and model evaluation—more than what’s typically included by automated tools—but could have been generated or scaffolded by an AI with expert-level prompting and guided refinement.

Key Indicators:

• Structured explanation style: The README and PR description systematically break down the problem using numbered steps, clean headers, and methodical explanations common in AI-generated instructional content.
• Repetitive phrasing: Frequent use of similar sentence structures like “### This is another one of those non obvious ones...” and over-explaining common sense design decisions.
• High completeness: Documentation covers problem understanding, modeling, data design, API design, evaluation, and follow-up considerations in one go, suggesting synthesis more than incremental development.
• Slight unnatural phrasing: e.g. “because anything that can be broken down into a process can be measured and anything that can be measured can be improved, this i believe is the very heart of artifiicial intelligence”—this philosophical articulation is fluid but sounds a bit forced and generic.

Despite all this, much of the code and logic in the system design reveals a depth that could belong to a proficient developer. The best conclusion is that it was likely authored with significant AI assistance, perhaps via ChatGPT + code editor workflow, but not 100% AI-generated end-to-end.

Thus, we conclude strong signs of AI assistance, but not complete automation.

Confidence Score: 75%

✅ No strong indicators of AI generation detected