AabidMK/SafeBite_Infosys_Internship_Oct2024
Dec 2024 – Dec 2024
1 Month active
Languages Used
CSSHTMLPython
Technical Skills
API DevelopmentData ScienceFile ManagementFlaskMachine LearningModel Deployment
NITHISHPRASANNA
PROFILE
Nithishprasanna
Nithish Prasanna developed an end-to-end allergen detection AI workflow for the SafeBite_Infosys_Internship_Oct2024 repository, focusing on integrating a pretrained model and encoder to predict allergen presence from product ingredients and user ratings. He built a Flask API to serve model predictions and a Streamlit UI for interactive user testing, enabling live inference and feedback. The project emphasized reproducibility by organizing model artifacts and scripts into dedicated directories, supporting deployment readiness. Using Python, Flask, and Streamlit, Nithish demonstrated depth in both backend API development and user interface integration, delivering a cohesive solution for ingredient-based allergen risk assessment within one month.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
115
Activity Months1
Your Network
6 peopleWork History
December 2024
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for SafeBite project (AabidMK/SafeBite_Infosys_Internship_Oct2024). Delivered end-to-end Allergen Detection AI Integration, including a Flask API for model predictions and a Streamlit UI for interactive testing. Implemented integration of a pretrained model and encoder to predict allergen presence from product ingredients and user ratings. Organized model artifacts under Model and Scripts directories to ensure reproducibility and deployment readiness.
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for SafeBite project (AabidMK/SafeBite_Infosys_Internship_Oct2024). Delivered end-to-end Allergen Detection AI Integration, including a Flask API for model predictions and a Streamlit UI for interactive testing. Implemented integration of a pretrained model and encoder to predict allergen presence from product ingredients and user ratings. Organized model artifacts under Model and Scripts directories to ensure reproducibility and deployment readiness.
December 2024
Activity
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Quality Metrics
Correctness64.0%
Maintainability64.0%
Architecture64.0%
Performance64.0%
AI Usage32.0%
Skills & Technologies
Programming Languages
CSSHTMLPython
Technical Skills
API DevelopmentData ScienceFile ManagementFlaskMachine LearningModel DeploymentStreamlit
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