Pinglab

C 62 completed
Api
monorepo / python · small
259
Files
30,411
LOC
4
Frameworks
9
Languages

Pipeline State

completed
Run ID
#340566
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
72.40
Framework unique
Isolation
Last stage change
2026-05-10 03:35:10
Deduplication group #65643
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility · severity-and-effort ranking · https://repobility.com

AI Prompt

I want to build a lightweight web API called Pinglab. It needs to allow users to train, evaluate, and benchmark machine learning models entirely through HTTP requests. The project structure suggests using FastAPI for the backend API, and I see Expo and Vite listed, so please set up the necessary scaffolding for a modern development environment. Include setup instructions for local running, perhaps using a Dockerfile, and ensure the project supports configuration via pyproject.toml.
python fastapi mlops api web-api machine-learning testing docker expo
Generated by gemma4:latest

Catalog Information

A lightweight web API that allows users to train, evaluate, and benchmark machine learning models via HTTP requests.

Description

PingLab is a lightweight web API built with FastAPI that exposes endpoints for training, evaluating, and benchmarking machine learning models. It supports both classical algorithms through scikit‑learn and deep learning models via PyTorch, while leveraging NumPy, SciPy, and Rich for data handling and pretty console output. Users submit datasets and hyper‑parameters in JSON payloads, and the service returns performance metrics and optional model artifacts. The API is designed for data scientists and ML engineers who need a quick, reproducible way to prototype and benchmark models without setting up local environments. It eliminates the need for a database by keeping all data in memory for the duration of a request.

الوصف

يُقدّم PingLab واجهة برمجة تطبيقات خفيفة الوزن تُبنى على FastAPI وتتيح للمستخدمين تدريب وتقييم ومقارنة أداء نماذج التعلم الآلي عبر طلبات HTTP. يدعم المشروع خوارزميات تقليدية عبر scikit‑learn ونماذج التعلم العميق عبر PyTorch، مع الاستفادة من NumPy وSciPy لمعالجة البيانات وRich لتنسيق الإخراج في الطرفية. يُرسل المستخدمون مجموعات البيانات ومعلمات التدريب في حمولة JSON، ويُرجع النظام مؤشرات الأداء وأحياناً ملفات النماذج. صُمم هذا الحل للباحثين ومهندسي التعلم الآلي الذين يحتاجون إلى طريقة سريعة وقابلة للتكرار لتجريب وتقييم النماذج دون إعداد بيئات محلية. يميز المشروع عدم الحاجة إلى قاعدة بيانات، إذ يُعالج جميع البيانات في الذاكرة خلال مدة الطلب، ما يضمن سرعة الاستجابة وسهولة النشر.

Novelty

6/10

Tags

machine-learning model-training model-evaluation api benchmarking data-science deep-learning scientific-computing

Technologies

fastapi numpy pytorch rich scikit-learn scipy uvicorn

Claude Models

claude-sonnet-4.6 claude-opus-4.6

Quality Score

C
62.3/100
Structure
64
Code Quality
77
Documentation
41
Testing
65
Practices
51
Security
67
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Code linting configured (eslint, ruff (possible))
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • 2 files with critical complexity need refactoring
  • 2661 duplicate lines detected \u2014 consider DRY refactoring
  • 7 'god files' with >500 LOC need decomposition

Recommendations

  • Add a LICENSE file (MIT recommended for open source)

Security & Health

15.3h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility · MCP-ready · https://repobility.com
Unknown
License
9.3%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
38.7%
typescript
30.8%
json
26.3%
markdown
2.1%
yaml
1.3%
css
0.3%
toml
0.3%
javascript
0.1%
html
0.0%

Frameworks

FastAPI Expo pytest Vite

Concepts (2)

Findings curated by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Powered by Repobility — scan your code at https://repobility.com
auto_descriptionProject DescriptionLocal run (macOS)80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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