Nutrition Application
Budget: $250 – $750 USD
We are seeking an experienced machine learning engineer/programmer/Web application programmer to help us develop an advanced nutrition analysis and recommendation web application using Convolutional Neural Networks (CNNs) and natural language processing with ChatGPT. The system will utilize TensorFlow for image recognition, leveraging the YOLO algorithm for precise ingredient identification, and will integrate with ChatGPT-4 via APIs for personalized dietary advice. Our goal is to create a robust, scalable application that offers users personalized meal plans, dietary advice, and nutrition information based on both text and image inputs. This application aims to support users in achieving their health goals, managing dietary restrictions, and making informed nutritional choices.
The Front-end will utilize React, Back-end will utilize Python, TensorFlow, YOLO for image recognition, which will also integrate with ChatGPT-4 through API's. Data handling with MongoDB.
Tasks:
Data collection and preprocessing: Acquire and preprocess nutritional data, including images of various foods and their nutritional profiles. This will involve gathering historical and real-time data from verified sources and preparing it for model training and testing.
Model development: Design and implement a CNN model using TensorFlow and the YOLO algorithm to accurately recognize and categorize food items from user-uploaded images.
Backend development: Implement the backend using Flask to handle API requests, manage data flow, and integrate with the MongoDB database for storing user data, meal plans, and nutritional information.
API integration with ChatGPT-4: Develop and fine-tune API requests to ChatGPT-4 for providing natural language-based dietary advice, ensuring the responses are personalized and relevant based on user inputs and recognized food items.
Model training and testing: Train and test the CNN model on the preprocessed data to ensure high accuracy in image recognition. Continuously improve the model based on user feedback and performance metrics.
Frontend development: Collaborate with frontend developers to integrate the AI models into a React-based user interface, ensuring seamless and intuitive user interactions.
Evaluation and optimization: Evaluate the performance of the image recognition and recommendation models. Optimize hyperparameters and improve the models based on system performance metrics and user feedback.
Documentation: Document the development process, model architectures, integration steps, and evaluation results. Provide detailed guides for both technical maintenance and user instructions.
Skills Required:
Strong experience in data science and machine learning
Proficiency in Python and its libraries (TensorFlow, Keras, Pandas, NumPy)
Experience with CNNs and YOLO algorithm for image recognition
Knowledge of natural language processing and API integration (preferably with ChatGPT-4)
Strong problem-solving and analytical skills
Good communication and documentation skills
Familiarity with Flask for backend development
Familiarity with React for frontend development
Experience with MongoDB for data storage and management
The Front-end will utilize React, Back-end will utilize Python, TensorFlow, YOLO for image recognition, which will also integrate with ChatGPT-4 through API's. Data handling with MongoDB.
Tasks:
Data collection and preprocessing: Acquire and preprocess nutritional data, including images of various foods and their nutritional profiles. This will involve gathering historical and real-time data from verified sources and preparing it for model training and testing.
Model development: Design and implement a CNN model using TensorFlow and the YOLO algorithm to accurately recognize and categorize food items from user-uploaded images.
Backend development: Implement the backend using Flask to handle API requests, manage data flow, and integrate with the MongoDB database for storing user data, meal plans, and nutritional information.
API integration with ChatGPT-4: Develop and fine-tune API requests to ChatGPT-4 for providing natural language-based dietary advice, ensuring the responses are personalized and relevant based on user inputs and recognized food items.
Model training and testing: Train and test the CNN model on the preprocessed data to ensure high accuracy in image recognition. Continuously improve the model based on user feedback and performance metrics.
Frontend development: Collaborate with frontend developers to integrate the AI models into a React-based user interface, ensuring seamless and intuitive user interactions.
Evaluation and optimization: Evaluate the performance of the image recognition and recommendation models. Optimize hyperparameters and improve the models based on system performance metrics and user feedback.
Documentation: Document the development process, model architectures, integration steps, and evaluation results. Provide detailed guides for both technical maintenance and user instructions.
Skills Required:
Strong experience in data science and machine learning
Proficiency in Python and its libraries (TensorFlow, Keras, Pandas, NumPy)
Experience with CNNs and YOLO algorithm for image recognition
Knowledge of natural language processing and API integration (preferably with ChatGPT-4)
Strong problem-solving and analytical skills
Good communication and documentation skills
Familiarity with Flask for backend development
Familiarity with React for frontend development
Experience with MongoDB for data storage and management