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
Worked on the Nutrihelp-api repository to deliver two core features focused on image-based recipe classification and validation. Developed an API endpoint that enables users to classify recipes from images, integrating a Python script execution path and implementing foundational error handling to support future AI model enhancements. Enhanced the backend by introducing strict validation for uploaded recipe images, accepting only JPG and PNG formats, and expanded automated test coverage to ensure reliability. Utilized JavaScript, Python, and Express.js to build robust API routes, emphasizing test-driven development and clear commit traceability, which improved the stability and maintainability of the image processing pipeline.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
410
Activity Months2
Your Network
39 peopleShared Repositories
39
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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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