NutriVIsion - Food Classification and Nutrient Information API

Welcome to NutriVIsion, an advanced food classification and nutrient information API powered by cutting-edge technology. This repository houses the backend server built using Python Flask, integrated with the powerful BiT-M Fine-Tuned Image Classification Model. NutriVIsion seamlessly analyzes images of various foods and provides comprehensive macronutrient information based on the identified food class. Table of Contents

Introduction
Features
Getting Started
    Prerequisites
    Installation
    Usage
API Endpoints
    Classify Food
Advanced Configuration
    Model Fine-Tuning
    Customization
Contributing
License

Introduction

NutriVIsion is designed to simplify the process of obtaining macronutrient information for a wide array of foods. Whether you're a health-conscious individual or a developer seeking to enhance your application with nutritional insights, NutriVIsion has got you covered. The integration of the BiT-M model enables high-precision food classification, ensuring accurate identification of food items from images. Features

State-of-the-Art Model: NutriVIsion employs a fine-tuned BiT-M image classification model, providing accurate food recognition.
Effortless Integration: The Python Flask server ensures easy integration into your applications.
Multipart Image Support: Submit food images via multipart form data to receive prompt classifications.
Comprehensive Nutrient Data: Obtain detailed macronutrient information based on the recognized food class.
Customizable: Tailor NutriVIsion's configuration to your specific requirements for enhanced performance.

Getting Started

Follow these steps to set up NutriVIsion on your local machine or server. Prerequisites

Python 3.6+
Pip package manager
Virtual environment (recommended)

Installation

Clone this repository: git clone https://github.com/your-username/nutrivision.git
Navigate to the project directory: cd nutrivision
Create a virtual environment: python -m venv venv
Activate the virtual environment:
    On Windows: venv\Scripts\activate
    On macOS and Linux: source venv/bin/activate
Install the required dependencies: pip install -r requirements.txt

Usage

Start the Flask server: python app.py
Make POST requests to http://localhost:5000/classify with the food image using multipart form data.
Receive the food class and macronutrient information in the response.

API Endpoints Classify Food

Endpoint: /classify
Method: POST
Request Body: Multipart form data with an image field containing the food image.
Response:

json

{
  "food_class": "apple_pie",
  "macronutrients": {
    "calories": 250,
    "protein": 2.5,
    "carbohydrates": 40.2,
    "fat": 9.8
  }
}

Contributing

We welcome contributions from the open-source community.