Math with Gestures using AI

The script introduces a project to create a math gesture program using AI. The concept involves hand gestures to draw problems, which the AI will then solve. The project is divided into parts: hand detection, drawing logic, AI integration, and creating an app. The video demonstrates the use of Canva for planning and mentions tools like Google's MediaPipe for hand tracking and Google Gemini for AI model interaction.

The paragraph details the setup of the PyCharm IDE and installation of necessary libraries such as OpenCV and CV Zone for hand detection. It guides through creating a 'main.py' file and setting up the virtual environment for Python package installation. The focus is on detecting hand gestures using CV Zone's hand tracking module.

This section explains the process of detecting hands and fingers using the CV Zone library. It covers the logic for drawing on the screen when specific hand gestures are made, such as when only the index finger is up. The script discusses creating functions to handle hand info and drawing on a canvas, which is initially set to the main webcam image.

The script delves into refining the drawing feature by creating a function to get hand info and another for drawing. It discusses handling the canvas, which initially starts as 'None', and only creates a black canvas when needed. The drawing logic is explained, including checking the number of fingers up and drawing lines between landmarks of the hand.

The paragraph discusses managing the canvas for drawing and merging it with the webcam image. It explains creating a canvas initially set to 'None' and updating it when the hand is detected. The script also covers real-time drawing, flipping the image for correct orientation, and overlaying the canvas on the main image for a visual representation of the drawing.

This section outlines the process of integrating the AI model from Google Gemini with the drawing app. It covers obtaining an API key, installing the necessary Python package for Gemini, and writing functions to send the canvas image to the AI for processing. The goal is to have the AI respond to the drawing, such as solving math problems.

The script describes testing the AI integration by sending text queries to the Gemini model and receiving responses. It then moves on to sending the canvas image as a PIL format to the AI, converting the canvas from a numpy array to the required format. The paragraph also discusses handling the AI's response and displaying it within the app.

The paragraph introduces a feature to reset the canvas when all fingers are up, allowing users to start over with their drawings. It also covers setting up the Streamlit framework to create a user interface for the app, making it visually appealing and easy to interact with the webcam and receive answers from the AI.

This section details the final touches for the Streamlit app, including setting up a checkbox to control the webcam and creating placeholders for displaying the webcam feed and AI responses. The script discusses running the app and ensuring that it can handle drawing, erasing, and sending math problems to the AI for solutions.

The final paragraph showcases the completed math gesture app in action. It demonstrates solving math problems by drawing them and receiving answers from the AI. The script also suggests future enhancements like allowing the AI to guess drawings or narrate solutions, emphasizing the project's potential for educational and interactive applications.

The script concludes by encouraging viewers to like, share, and look forward to the next project. It highlights the accessibility and potential of the technology used in the math gesture app, which is available for free and offers ample room for experimentation before scaling up to commercial projects.