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
Developed and delivered an end-to-end allergen detection AI integration for the SafeBite_Infosys_Internship_Oct2024 repository, focusing on predicting allergen presence from product ingredients and user ratings. The solution combined a Flask API for serving model predictions with a Streamlit interface for interactive user testing, leveraging Python for both backend and frontend components. Integrated a pretrained machine learning model and encoder, organizing all model artifacts and scripts to ensure reproducibility and deployment readiness. The work demonstrated a methodical approach to model deployment, API development, and file management, resulting in a live prediction workflow that supports both programmatic and user-driven evaluation scenarios.
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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline