Gopher-Industries/Nutrihelp-api
Nov 2024 – Dec 2024
2 Months active
Languages Used
JavaScriptPythonShellYAML
Technical Skills
API DevelopmentChaiChai-HttpChild ProcessesError HandlingExpress.js
cjvrd
PROFILE
Cjvrd
During a two-month period, CJ Virdo developed and enhanced image-based recipe workflows for the Nutrihelp-api repository. He built an API endpoint for recipe image classification, integrating a Python script execution path and implementing error handling to support AI-driven predictions. In the following month, he introduced strict validation for image uploads, accepting only JPG and PNG formats, and expanded automated test coverage to handle edge cases such as missing or invalid files. Working primarily with Node.js, Python, and Express.js, CJ focused on backend reliability and data integrity, laying a solid foundation for future AI model integration and robust image processing pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
410
Activity Months2
Your Network
20 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 — Nutrihelp-api: Strengthened image handling for recipe workflows by introducing strict image type validation and expanding test coverage. Key feature delivered: Recipe Image Upload Validation (accepts only JPG/PNG) with tests for no-file, invalid type, and successful uploads. This work is backed by the commit e187877dd5ccd9e867e636643a882364ccc19cad. Impact: reduces invalid data in image classification, improves reliability of the image processing pipeline, and lowers downstream error rates. Skills demonstrated: backend API validation, test-driven development, robust error handling, and clear commit-based traceability.
1 Commits • 1 Features
Dec 1, 2024December 2024 — Nutrihelp-api: Strengthened image handling for recipe workflows by introducing strict image type validation and expanding test coverage. Key feature delivered: Recipe Image Upload Validation (accepts only JPG/PNG) with tests for no-file, invalid type, and successful uploads. This work is backed by the commit e187877dd5ccd9e867e636643a882364ccc19cad. Impact: reduces invalid data in image classification, improves reliability of the image processing pipeline, and lowers downstream error rates. Skills demonstrated: backend API validation, test-driven development, robust error handling, and clear commit-based traceability.
December 2024
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024: Delivered the Recipe Image Classification API feature for Nutrihelp-api. Implemented an API endpoint to classify recipes from images via an AI model, with a Python script execution path, API route, and basic error handling. Laid groundwork for tests and CI, enabling a more engaging user experience and data-driven nutrition insights. Focused on stability through bug fixes and introduced test scaffolding for automated validation. This sets the stage for future AI model integration and broader image-based capabilities.
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024: Delivered the Recipe Image Classification API feature for Nutrihelp-api. Implemented an API endpoint to classify recipes from images via an AI model, with a Python script execution path, API route, and basic error handling. Laid groundwork for tests and CI, enabling a more engaging user experience and data-driven nutrition insights. Focused on stability through bug fixes and introduced test scaffolding for automated validation. This sets the stage for future AI model integration and broader image-based capabilities.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance65.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
JavaScriptPythonShellYAML
Technical Skills
API DevelopmentBackend DevelopmentChaiChai-HttpChild ProcessesError HandlingExpress.jsFile HandlingKerasMachine LearningNode.jsPythonTensorFlowTesting
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