Math with Gestures using AI

The speaker introduces a project to create a math gesture program that uses hand movements to generate drawings, which an AI model will then interpret and solve mathematically. The project is broken down into parts: hand detection, drawing logic, AI model integration, and creating an app. The speaker outlines the use of specific technologies like Google's MediaPipe for hand tracking and Google Gemini for AI processing, and mentions the use of Streamlit for app development.

The speaker demonstrates how to set up the development environment using PyCharm IDE and installing necessary libraries like OpenCV and CV Zone through the IDE's interface or command prompt. The focus is on detecting hand gestures using CV Zone's hand tracking module, and the speaker provides guidance on installing the module and accessing its documentation for further details.

The speaker explains the process of detecting hand gestures to initiate drawing. They detail the code for importing the hand detector and setting up the parameters for detecting a single hand. The video covers how to capture the landmarks of the hand and fingers, and how to adjust the camera settings for better detection. The speaker also discusses creating functions to encapsulate the hand detection logic for reuse.

The speaker moves on to the drawing functionality, explaining how to create a canvas for drawing and overlay it on the main image. They discuss the logic for detecting when the index finger is up to initiate drawing and how to draw lines between points. The video also covers handling the canvas initialization, updating the drawing on the canvas, and merging the canvas with the webcam feed.

The speaker addresses issues with the drawing overlay, such as flipping the image and overlaying the canvas on the main image with weighted transparency. They also discuss the preparation needed for integrating the AI model, including capturing the correct image from the canvas to be sent to the AI for processing.

The speaker outlines the steps for integrating Google Gemini AI into the project. They discuss obtaining an API key, installing the necessary Python package for Gemini, and writing a function to send the canvas image to the AI model. The video demonstrates how to configure the API key and generate text responses from the AI based on the image input.

The speaker adds features to the drawing functionality, such as resetting the canvas when all fingers are up and changing the drawing prompt for the AI. They show how to convert the canvas to a PIL format for AI processing and how to handle the AI's response in the application.

The speaker demonstrates creating the user interface for the app using Streamlit. They explain how to set up the page configuration, split the interface into columns for the webcam feed and AI responses, and add interactive elements like a run button and placeholders for displaying images and text.

The speaker wraps up the project by showing the complete functionality of the math gesture application. They demonstrate solving math problems by drawing them and receiving answers from the AI. The video also includes experimenting with different prompts for the AI and hints at potential future enhancements like narration.

The speaker concludes the project by expressing amazement at the capabilities of the free technologies used and encourages viewers to experiment with the project. They invite feedback and engagement, promising more content in future videos.