NutriScan empowers users to make healthier, informed food choices through barcode scanning, intelligent search, healthier alternatives, and community-driven data contributions. 🌱 It translates complex food information into simple, actionable insights to help users shift their daily habits for the better.🌟
- Users can search for food products by name or barcode.
- Displays key nutrition information in a clean, easy-to-understand format. 📋
- Helps users quickly find product details without scanning. ⚡
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- Nutritional information and scores of products are shown in an easy-to-understand and transparent manner. 🥗
- Presents a multitude of information like glycemic index, allergen information, macro and micro nutrients. 🍚🥜
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- Suggests better alternatives based on nutritional profiles. 🔄
- Allows users to compare products easily and make healthier choices. ✅
- Minimalist display with links to detailed information if needed. 🔗
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- A friendly chatbot answers user questions about products, nutrition, etc. 💬
- Provides personalized tips for better eating habits. 🥦✨
- Implements NLP, Noun detection, and NER using TextBlob and NLTK to process complex queries efficiently. 🧠
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- User data such as previous searches, nutritional values, and scores of previously searched products are analyzed to generate a report in a presentable manner using Jinja templating and Chart.js. 🖥️📑
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- If a product is not found or has incomplete data, users can upload the information about the product. 🔎
- The data will then be verified and updated. 🛠️
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- Backend: Flask (Python)
- Database: MySQL
- Frontend: Jinja2 Templates, Chart.js
- NLP: TextBlob, NLTK