Areas Worker

C 62 completed
Other
unknown / html · tiny
13
Files
1,808
LOC
0
Frameworks
5
Languages

Pipeline State

completed
Run ID
#364074
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
18.83
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47257
Member of a group with 1 similar repo(s) — canonical #81378 view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility analyzer · published findings · https://repobility.com

AI Prompt

Create a unified Streamlit tool for managing delivery area data. The tool needs to allow users to upload Excel files (`.xlsx` or `.xls`), automatically detect the header, and select the correct sheet. Next, it must clean the data by extracting `id`, `area name`, and `fees`, dropping rows missing fees, and removing parent city rows, allowing the user to download the cleaned CSV. The core feature is visualizing the areas on an interactive Leaflet map, joining the cleaned data with 363 reference polygons. The map should only render areas with fees, and users must be able to select unselected areas from a sidebar list to add them (triggering a fee prompt). Finally, the app needs export functionality to download the enriched CSV or just the selected areas from the map.
streamlit python data-visualization gis excel-processing leaflet web-app data-cleaning
Generated by gemma4:latest

Catalog Information

A unified Streamlit tool that imports delivery area data from Excel files, cleans it, visualizes areas on an interactive map, and exports the final curated dataset.

Description

A unified Streamlit tool that imports delivery area data from Excel files, cleans it, visualizes areas on an interactive map, and exports the final curated dataset.

Novelty

3/10

Tags

streamlit python data-visualization gis excel-processing leaflet web-app data-cleaning

Technologies

streamlit

Claude Models

claude-opus-4-6

Quality Score

C
62.2/100
Structure
44
Code Quality
100
Documentation
44
Testing
0
Practices
78
Security
100
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected

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

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 (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (5)
Open data scored by Repobility · https://repobility.com
Unknown
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

html
80.2%
python
13.7%
markdown
5.1%
toml
0.7%
text
0.3%

Frameworks

None detected

Concepts (2)

Findings produced by Repobility · scan your repo at https://repobility.com/scan/
CategoryNameDescriptionConfidence
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
auto_descriptionProject DescriptionA unified Streamlit tool that imports delivery area data from Excel files, cleans it, visualizes areas on an interactive map, and exports the final curated dataset.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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