Project Overview
This project demonstrates a health management application utilizing Google's Generative AI technology (Gemini). The app focuses on analyzing user-uploaded images of food items and estimating total calorie content.
Technologies Used
Streamlit: A Python library for creating interactive web applications.
dotenv: A Python library for loading environment variables from a .env file.
google.generativeai (GenAI): Google's API for accessing Generative AI models, including Gemini.
PIL (Python Imaging Library): Provides image processing functionalities.
Requirements
Python environment with required libraries installed (Streamlit, dotenv, GenAI, PIL).
Google Cloud Project and API key for GenAI access.
A pre-trained Gemini model configured for image analysis and text generation ("gemini-pro-vision").
How it Works
User Interface (UI):
The app starts with a title and input prompt field for users to specify additional information.
Users can upload an image containing food items.
A submit button triggers the analysis.
Image Processing:
Upon submission, the uploaded image is converted into a format suitable for the Gemini model.
GenAI Integration:
The pre-defined input text instructs the model to analyze the image and identify food items, then calculate the total calorie content.
The GenAI model processes the user prompt, image data, and predefined text to generate a response.
Output:
The app displays the generated response, potentially listing identified food items and their estimated calorie intake.
Key Code Snippets
get_gemini_response() function:
input_image_setup() function:
Main application logic:
Limitations
Accuracy: The calorie estimations provided are based on the model's training data and may not be perfectly accurate for every image.
Model Specificity: The current implementation uses a generic "gemini-pro-vision" model. Training a custom model specifically for food image analysis might improve accuracy.
Limited Functionality: This is a basic example, and the app could be expanded to offer additional features like dietary recommendations or personalized advice.
Future Improvements
Integrate with a calorie database for more accurate estimations.
Allow users to specify dietary restrictions.
Enhance the UI to display identified food items within the image.
Train a custom model specifically for food image analysis and calorie estimation.
Overall, this project showcases the potential application of Generative AI in health management. By leveraging image recognition and language generation, such tools can offer users a convenient and informative way to keep track of their dietary intake.