Juniper Canopy

C+ 78 completed
Web App
containerized / python · small
386
Files
90,774
LOC
3
Frameworks
9
Languages

Pipeline State

completed
Run ID
#369348
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
70.00
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #55796
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility analyzer · published findings · https://repobility.com

AI Prompt

Create a real-time monitoring dashboard, similar to the Juniper Canopy project. I need this dashboard to visualize data from a Cascade Correlation Neural Network. The architecture should show it connecting to a separate Training Service (like JuniperCascor) via REST and WebSockets, and also fetching datasets from a Dataset Service (like JuniperData). The platform should be built using Python, ideally leveraging Flask or FastAPI for the backend, and I need to include setup instructions for Docker deployment. Please structure the project to support both a live service mode and a demo mode.
python flask fastapi monitoring dashboard ai/ml real-time docker rest websocket
Generated by gemma4:latest

Catalog Information

The juniper-canopy project provides a real-time monitoring dashboard for the Cascade Correlation Neural Network.

Description

This project offers a real-time monitoring dashboard specifically designed for the Cascade Correlation Neural Network. It allows users to track and visualize key performance metrics in real-time, making it easier to understand and optimize the network's behavior. The dashboard is built using modern web technologies and provides an intuitive interface for users to explore their data.

الوصف

هذا المشروع يقدم لوحة مراقبة زمنية حقيقية خاصة بالشبكة العصبية Cascade Correlation Neural Network. يتيح للمستخدمين متابعة وتحليل أداء الشبكة في الوقت الحقيقي، مما يجعل من السهل فهم وتنظيم سلوك الشبكة. تم بناء اللوحة باستخدام تكنولوجيا الويب الحديثة وتقدم واجهة سهلة الاستخدام للمستخدمين لاستكشاف بياناتهم.

Novelty

5/10

Tags

real-time-monitoring neural-network dashboard machine-learning data-visualization

Technologies

click fastapi flask matplotlib numpy plotly pydantic pytorch rich scipy typer uvicorn

Claude Models

claude-opus-4.6 claude-sonnet-4.6

Quality Score

C+
77.5/100
Structure
87
Code Quality
73
Documentation
80
Testing
85
Practices
65
Security
75
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (175% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • 2083 duplicate lines detected \u2014 consider DRY refactoring
  • 6 'god files' with >500 LOC need decomposition

Recommendations

  • Address 81 TODO/FIXME items \u2014 consider tracking them as issues

Security & Health

28.8h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility · code-quality intelligence · https://repobility.com
MIT
License
15.3%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
54.5%
markdown
37.0%
yaml
4.5%
shell
1.6%
json
0.7%
text
0.7%
css
0.6%
toml
0.2%
javascript
0.2%

Frameworks

Flask FastAPI pytest

Concepts (2)

Same analyzer free for public repos: https://repobility.com
CategoryNameDescriptionConfidence
Repobility — same analyzer, your code, free for public repos · /scan/
auto_descriptionProject DescriptionJuniper is an AI/ML research platform for investigating dynamic neural network architectures and novel learning paradigms. The project emphasizes ground-up implementations from primary literature, enabling a more transparent exploration of fundamental algorithms.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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