Aicc
C 64 completed
Ai Ml
mobile_app / kotlin · small
186
Files
11,824
LOC
3
Frameworks
5
Languages
Pipeline State
completedRun ID
#371788Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
66.80Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:02Deduplication group #49208
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (1)
Web Backend
Source: Repobility analyzer · https://repobility.com
AI Prompt
I want to build a mobile application for image classification, targeting developers and researchers. The core logic should utilize a machine learning model. Since the project involves both mobile development and ML, please structure it using Kotlin for the Android front-end with Jetpack Compose. For the backend or testing components, please incorporate FastAPI and pytest, and handle configuration using TOML or YAML files.
kotlin android jetpack-compose fastapi pytest machine-learning image-classification mobile-app
Generated by gemma4:latest
Catalog Information
This project is a machine learning model for image classification, intended for developers and researchers.
Description
Psrvere__aicc is an image classification model built using Python. It appears to be a machine learning-based tool designed to classify images into predefined categories. However, without further information, it's difficult to provide more details about its functionality or target audience.
الوصف
هذا المشروع هو نموذج التعلم الآلي لتصنيف الصور، مصمم للباحثين والمطورين.
Novelty
5/10Tags
image-classification machine-learning deep-learning computer-vision object-detection
Claude Models
claude-opus-4.6
Quality Score
C
63.6/100
Structure
62
Code Quality
88
Documentation
2
Testing
75
Practices
68
Security
75
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (43% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (PascalCase)
- Low average code complexity \u2014 well-structured code
- Containerized deployment (Docker)
Weaknesses
- Missing README file \u2014 critical for project understanding
- No LICENSE file \u2014 legal ambiguity for contributors
- 610 duplicate lines detected \u2014 consider DRY refactoring
Recommendations
- Add a comprehensive README.md explaining purpose, setup, usage, and architecture
- Add a LICENSE file (MIT recommended for open source)
Security & Health
9.6h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
Same scanner, your repo: https://repobility.com — Repobility
Unknown
License
9.2%
Duplication
Languages
Frameworks
FastAPI Jetpack Compose pytest
Concepts (1)
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| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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