ConsumerWise is an AI-powered tool designed to help users make informed food choices by providing in-depth nutritional analysis. Through seamless integration of context-aware chatbot interactions and advanced image recognition, users can simply scan food labels to instantly receive detailed dietary insights. Whether exploring visual charts or chatting with NutriBot, users are guided to better understand the nutritional content of their food. Personalization is enhanced through user registration and setup questions, tailoring the experience based on individual dietary preferences, intolerances, and goals.
Link:
- Install the required dependencies:
pip install -r requirements.txt
- Flask Backend: Powers the application logic, complemented by a responsive frontend built with HTML, CSS/Bootstrap, and JavaScript.
- FastAPI: Serves as a wrapper to handle custom API endpoints, ensuring fast and scalable performance.
- Google Cloud App Engine: Provides a robust, scalable platform for deploying and running the application seamlessly.
- Google Cloud SQL: Manages our database infrastructure, offering reliable and secure data storage.
- GPT-4o MINI & GPT Vision API: AI models for natural language processing and image recognition, enabling NutriBot to analyze food labels and engage in meaningful conversations.
- Tavily Search API: Enhances search capabilities, offering efficient retrieval of relevant data from various sources.
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User Readability: The main attempt is to provide nutritional information in a concise manner that is user readable , further assisted by visual aids such as pie-charts and histograms. This is to give the user a starting point.
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Personalisation: This involves Context Awareness , answering user-level queries based of the information uploaded by them. It attempts to interlink the current analysis with these queries for personalised answers.
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Data Interpretation and Visualization: Interpreting extracted data and provide insights, such as analyzing nutritional content or suggesting diet alternatives, and visualizing the data to enhance user comprehension.
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Integration with User Data: Connecting nutritional recommendations with user habits (e.g., current exercise routine, stress level) for a holistic health approach.
This project is licensed under the MIT License - see the LICENSE file for details.
Department of Mechanical Engineering
Indian Institute of Technology, Madras
Chennai, Tamil Nadu 600036
Email: me23b049@smail.iitm.ac.in
Department of Electrical Engineering
Indian Institute of Technology, Madras
Chennai, Tamil Nadu 600036
Email: ee23b001@smail.iitm.ac.in
Department of Mechanical Engineering
Indian Institute of Technology, Madras
Chennai, Tamil Nadu 600036
Email: me23b233@smail.iitm.ac.in
Department of Metallurgical and Materials Engineering
Indian Institute of Technology, Madras
Chennai, Tamil Nadu 600036
Email: mm23b066@smail.iitm.ac.in