Sloy

C 62 completed
Other
web_app / json · tiny
34
Files
6,895
LOC
4
Frameworks
9
Languages

Pipeline State

completed
Run ID
#397000
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
52.84
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #47291
Member of a group with 13 similar repo(s) — canonical #77829 view group →
Top concepts (2)
Project DescriptionWeb Frontend
Repobility · code-quality intelligence platform · https://repobility.com

AI Prompt

Create a full-stack web application, similar to a blueprint digitization tool. The goal is to automatically convert photos of engineering drawings into the DXF CAD format. The frontend should be built with React and TypeScript, styled with Tailwind CSS, and support drag-and-drop image uploading. Key features needed include automatic contour detection, perspective transformation, adaptive binarization for drawing extraction, and line/circle detection using Hough Transform. On the backend, use FastAPI with Python to handle the logic, integrating OpenCV for computer vision tasks and ezdxf for generating the final DXF file. Finally, implement a comparison view to show the original vs. processed image.
python fastapi react typescript opencv dxf web-app computer-vision tailwind fullstack
Generated by gemma4:latest

Catalog Information

Fullstack приложение для автоматической конвертации фотографий чертежей в CAD формат (DXF).

Description

Fullstack приложение для автоматической конвертации фотографий чертежей в CAD формат (DXF).

Novelty

3/10

Tags

python fastapi react typescript opencv dxf web-app computer-vision tailwind fullstack

Claude Models

claude-opus-4-6

Quality Score

C
61.5/100
Structure
50
Code Quality
79
Documentation
46
Testing
15
Practices
84
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Code linting configured (eslint)
  • Good security practices \u2014 no major issues detected
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions
  • 173 duplicate lines detected \u2014 consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.8h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
Unknown
License
1.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

json
65.9%
python
16.4%
typescript
9.4%
markdown
6.6%
yaml
0.7%
javascript
0.4%
css
0.3%
html
0.2%
text
0.1%

Frameworks

FastAPI React Tailwind CSS Vite

Concepts (2)

Same analyzer free for public repos: https://repobility.com
CategoryNameDescriptionConfidence
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot
auto_descriptionProject DescriptionFullstack приложение для автоматической конвертации фотографий чертежей в CAD формат (DXF).80%
auto_categoryWeb Frontendweb-frontend70%

Quality Timeline

1 quality score recorded.

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