https://github.com/adsh16/dish-nutrient-simplifier
It provides a collection of ingredient from diverse cuisines worldwide including their enhanced profile (like gluten-free, raw, cooked). It includes detailed ingredient lists and nutritional profiles for each ingredient, allowing users to explore dishes that match their dietary preferences.
https://github.com/adsh16/dish-nutrient-simplifier
flask food-nutrient-data reactjs
Last synced: 2 months ago
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It provides a collection of ingredient from diverse cuisines worldwide including their enhanced profile (like gluten-free, raw, cooked). It includes detailed ingredient lists and nutritional profiles for each ingredient, allowing users to explore dishes that match their dietary preferences.
- Host: GitHub
- URL: https://github.com/adsh16/dish-nutrient-simplifier
- Owner: adsh16
- Created: 2024-12-14T18:44:13.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-01-01T09:55:02.000Z (6 months ago)
- Last Synced: 2025-03-29T04:25:29.905Z (3 months ago)
- Topics: flask, food-nutrient-data, reactjs
- Language: Python
- Homepage: https://dish-nutrient-simplifier-cn9t.vercel.app/
- Size: 19.9 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
DishIT: A Web-Based Nutritional Analysis Tool
An awesome project that optimizes the Nutrition Profile!
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DishIT is a full-stack web application that simplifies complex nutritional data into actionable insights. By leveraging a robust dataset from the USDA FoodData Central and integrating advanced algorithms, DishIT empowers users to analyze recipes, explore nutrient-dense ingredients, and optimize their dietary choices.
## Features
1. **Nutritional Data Collection**
- A comprehensive dataset from USDA FoodData Central containing food descriptions and macronutrient profiles.
2. **Nutritional Analysis Engine**
- Analyze the nutritional profile of recipes based on user inputs like protein, carbohydrate, and sugar requirements.
3. **Ingredient Substitution Logic**
- Suggest alternative ingredients that align with user dietary preferences or restrictions.
4. **Interactive Web Interface**
- Built with React.js for a dynamic and user-friendly experience.
5. **Recipe Improvement Suggestions**
- Recommend ingredients or recipes to meet specific nutrient goals.
## Technology Stack
- **Frontend:** React.js (hosted on Vercel)
- **Backend:** Flask (hosted on Render)
- **Database:** CSV dataset preprocessed using Pandas and NumPy
## Methodology
1. **Preprocessing and Cleaning**
- Removed invalid entries, handled missing values, and standardized columns.
- Extracted relevant macronutrient data (protein, carbohydrates, sugars).
2. **Nutritional Profile Analysis**
- Identify top ingredients based on user-input nutrient requirements.
- Dynamic search for nutrient-dense options.
3. **Ingredient Substitution**
- Recommend alternatives considering nutritional equivalence and dietary restrictions.
4. **Frontend and Backend Integration**
- API endpoints built with Flask serve nutritional data and handle ingredient recommendations.
- React-bootstrap-typeahead enhances search functionality on the user interface.
## How to Use
1. Input your daily nutrient intake goals (e.g., protein, carbohydrates, sugars).
2. Explore ingredient suggestions tailored to your needs.
3. Get recipe improvement suggestions for healthier alternatives.
## Results
- Accurate analysis of ingredient and recipe nutritional profiles.
- Personalized dietary recommendations for various dietary needs and preferences.
- A seamless, intuitive user experience bridging data-driven insights and culinary exploration.
## Future Scope
- Expand the dataset to include regional and rare ingredients.
- Integrate machine learning models for personalized recipe suggestions.
- Add dietary filters like vegan, gluten-free, or low-fat options.
- Develop a mobile application for wider accessibility.
## Installation
### Prerequisites
- Node.js and npm
- Python 3.x
- Flask
### Steps
1. Clone this repository:
```bash
git clone https://github.com/yourusername/DishIT.git
```
2. Install frontend dependencies:
```bash
cd frontend
npm install
```
3. Install backend dependencies:
```bash
cd backend
pip install -r requirements.txt
```
4. Run the backend server:
```bash
python app.py
```
5. Run the frontend application:
```bash
cd frontend
npm start
```
## Contributors
- **Aditya Sharma**
- Preprocessing, Nutritional Analysis, Flask API Development, Deployment
- **Akshat Raj Saxena**
- Frontend Development, React Components, Implementing Algorithmic Logic
- **Kanishk Kumar Meena**
- Dataset Management, Algorithm Design, Testing, Frontend
## References
- [USDA FoodData Central](https://fdc.nal.usda.gov/)
- [Python Documentation](https://docs.python.org/3/)
- [React Bootstrap Typeahead](https://github.com/ericgio/react-bootstrap-typeahead)
---
### Hosted Application Links
- **Frontend**: [Vercel](https://your-vercel-link.com)
- **Backend**: [Render](https://your-render-link.com)
### License
This project is open source and available under the [MIT License](LICENSE).