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FoodSnap AI - A Community driven project part of WeCode Community
Demo video:
https://github.com/user-attachments/assets/5eafcecb-4aff-4e34-a911-2ee615433322
Tech stack used:
Backend: .Net Core Web Api
FrontEnd: Angular
Db: Sql
As part of my journey into exploring AI, I developed FoodSnap AI—a full-stack system that leverages deep learning to identify food items and estimate their nutritional value from images.
🧠 Core Features
Image Classification Model: A convolutional neural network (CNN) architecture featuring Conv2D, MaxPooling, and Dropout layers, trained to recognize common fruits like apples, bananas, and mangoes.
Custom Dataset: Constructed using a combination of web scraping and real-world photos of fruits taken at home for diverse training inputs.
Dynamic Calorie Estimation: Calculates calorie values based on the detected food class and optional weight input, ensuring flexibility in user interaction.
UI: Angular-powered interface allows users to upload food images with a live preview before submission.
Backend Integration: ASP.NET Core API processes the uploaded images, triggers the ML model, and returns results including prediction confidence and calorie data.