Image Procesor

D 55 completed
Web App
web_app / markdown · tiny
37
Files
4,896
LOC
1
Frameworks
9
Languages

Pipeline State

completed
Run ID
#363456
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
61.10
Framework unique
Isolation
Last stage change
2026-05-10 03:34:29
Deduplication group #64573
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility · code-quality intelligence · https://repobility.com

AI Prompt

Create a universal API server using Flask for processing product images. I need it to handle several features, including automatic format conversion from PNG to JPG, background replacement (changing white/black backgrounds to a custom color), and smart product detection for centering. It should also support intelligent resizing while maintaining aspect ratio, auto-upscaling small images using a multi-scale method, and batch processing of multiple images. The server must expose a REST API with endpoints for single image processing and batch processing, and ideally include a simple web interface for drag-and-drop usage.
flask python api image-processing web-app rest-api image-manipulation backend
Generated by gemma4:latest

Catalog Information

The mwalo4 image processor is a universal API server for processing product images with automatic optimizations, background change, and smart detection.

Description

This project provides a web interface and REST API for image processing. It supports automatic conversion of formats, smart product detection, background removal, intelligent resizing, auto upscaling, batch processing, and more. The API is production-ready and can be deployed on Railway with Docker.

الوصف

هذا المشروع يقدم واجهة ويب ومكتبة REST لتعديل الصور. يدعم تحويل تنسيقات الصور تلقائيًا، وتعرف على المنتج الذكية، وإزالة الخلفية، والتسوية الذكية، زيادة حجم الصورة التلقائية، معالجة المجموعة، وغيرها. يمكن نشره في Railway باستخدام Docker.

Novelty

7/10

Tags

image-processing product-detection background-removal intelligent-resizing auto-upscaling batch-processing

Technologies

flask gunicorn numpy

Claude Models

claude-opus-4.6

Quality Score

D
55.4/100
Structure
43
Code Quality
75
Documentation
55
Testing
0
Practices
66
Security
100
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • 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
  • No CI/CD configuration \u2014 manual testing and deployment
  • 141 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
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.1h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
All rows scored by the Repobility analyzer (https://repobility.com)
Unknown
License
9.6%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

markdown
36.3%
python
34.7%
text
13.6%
css
9.1%
javascript
3.6%
html
1.4%
json
0.8%
toml
0.4%
shell
0.2%

Frameworks

Flask

Concepts (2)

Findings produced by Repobility · scan your repo at https://repobility.com/scan/
CategoryNameDescriptionConfidence
Repobility · MCP-ready · https://repobility.com
auto_descriptionProject DescriptionPokročilý API server pro zpracování produktových obrázků s automatickými optimalizacemi, změnou pozadí a smart detection.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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