Areas Worker
C 62 completed
Other
unknown / html · tiny
13
Files
1,808
LOC
0
Frameworks
5
Languages
Pipeline State
completedRun ID
#364074Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
18.83Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication 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
3/10Tags
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
Languages
Frameworks
None detected
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project 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. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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