Unleashing the Power of ChatGPT: Building an AI That Will Transform Your Conversations
Table of Contents
- Introduction
- Understanding Chat GPT
- What is Chat GPT?
- How does Chat GPT work?
- Testing Chat GPT
- Setting Ground Rules
- Describing the Project to Chat GPT
- Generating Code for an HTML Canvas
- Challenges and Solutions
- Scaling and Offset Issues
- Changing Background and Stroke Colors
- Building a Machine Learning Model
- Training the Model
- Generating Code for Model Training
- Using TensorFlow 2.0
- Saving the Trained Model
- Loading the Model into JavaScript
- Converting the Model to JSON
- Setting up a Web Server
- Accessing the Model through the Server
- Feeding Data into the Model
- Resizing the Image Data
- Predicting Handwritten Digits
- Conclusion
- Highlights
- FAQ
Introduction
Welcome back to the Channel! In today's video, we will be diving into the world of AI and exploring the capabilities of Chat GPT. Chat GPT has been making waves recently, being hailed as an incredible AI Chatbot that can answer any question thrown at it. But what intrigued me even more was its ability to understand and generate software code. As a mechatronics and software engineer, I wanted to put Chat GPT to the test by asking it to design and build a simple AI or machine learning system for me. Join me on this Journey as we explore the possibilities of Chat GPT and see how well it performs.
Understanding Chat GPT
What is Chat GPT?
Chat GPT is an AI chatbot that has been in the spotlight lately. It is an incredible language model developed by OpenAI that is capable of generating human-like text responses. It is trained on a massive amount of data and can seemingly answer any question that You throw at it. The potential applications of Chat GPT are vast, ranging from writing recipes Based on available ingredients to creating personalized workout plans. It can even draft formal letters for you, like a boss asking for a pay Raise.
How does Chat GPT work?
At its Core, Chat GPT operates on a deep learning model known as a transformer. This model uses a sophisticated architecture that allows it to understand and generate text based on the input provided. It employs a technique called Attention mechanism, which helps it focus on Relevant parts of the text while generating responses. Through a process called unsupervised learning, Chat GPT is trained on a large corpus of text data, enabling it to learn Patterns, Context, and respond in a conversational manner.
Testing Chat GPT
Setting Ground Rules
Before diving into the project, it's important to establish a set of ground rules. In my case, I have two specific rules for this AI application. Firstly, I want the application to be able to run on a browser, as I plan to deploy my projects on my Website for others to Interact with. Secondly, I am only allowed to use code generated by Chat GPT to build this application. This restriction will give me valuable insights into the quality of the code it produces and its ability to generate complete solutions.
Describing the Project to Chat GPT
Now that we have set the ground rules, let's move forward with explaining what I want Chat GPT to build. I envision a canvas on the browser where users can draw anything using their mouse. Every time a user draws a number between 0 to 9 on the canvas, the image will be fed into our AI or machine learning model. The AI will then analyze the drawing and provide the corresponding number. Sounds simple enough, right?
To get started, I describe the project requirements to Chat GPT. Within seconds, it provides me with an overview of the steps needed to build the app. This gives me a clear Outline to follow and helps me understand the necessary components.
Generating Code for an HTML Canvas
Next, I ask Chat GPT to generate a script that allows users to draw on an HTML canvas using their mouse. When Chat GPT presents the code, I Notice that it embeds the HTML, CSS, and JavaScript into a single file. However, I prefer to have these files separated. So, I ask Chat GPT to split them into their respective files for better organization and maintainability.
After reviewing the code and making sure it aligns with my requirements, I copy-paste it into my IDE and open the browser to test it out. I'm pleased to see an HTML canvas being drawn onto the screen. However, when I start drawing with my mouse, something strange happens. The drawing appears scaled and offset from the position of my Cursor.
Challenges and Solutions
Scaling and Offset Issues
Uncertain about the cause of this issue, I turn to Chat GPT for assistance. However, I struggle to describe the problem accurately, leading to a solution that doesn't address the root cause. Chat GPT suggests adjusting the code to correct for an offset, assuming the canvas isn't located at the top left corner of the screen. Though this would be a valid fix if the canvas were elsewhere, it doesn't resolve the problem in this case. After some off-camera investigation, I identify and resolve the issue by removing specific lines of code in the CSS file. It turns out that setting the width and Height of an HTML canvas using CSS can cause a scaling issue, leading to the offset problem.
Changing Background and Stroke Colors
To ensure the images drawn closely match the dataset used for training the model, I decide to change the background color of the canvas to black and the stroke color to white. I request Chat GPT to make this adjustment, which it promptly produces the correct code for. By incorporating these changes, our canvas aligns with the preferred color scheme.
Building a Machine Learning Model
Now, it's time to Delve into the exciting part of our project: building a machine learning model. I ask Chat GPT to generate code that will train a model for handwritten digit recognition. It quickly delivers Python code using the Keras library. However, I want to leverage TensorFlow 2.0 instead, as it supports training a model in Python and loading it into the browser using TensorFlow.js.
I request Chat GPT to rewrite the code using TensorFlow 2.0, and I'm impressed by the result. The generated code takes care of loading and preprocessing the dataset, creating a simple convolutional neural network, and training it using the prepared data. The only missing piece is the code to save the trained model. Fortunately, Chat GPT provides a solution for that as well.
Training the Model
Generating Code for Model Training
I copy the generated code into my IDE, excited to train my model. To my surprise, the code runs without any errors, and within minutes, I have a fully trained model. Although it's important to fine-tune the model architecture and training parameters in most cases, for this video, I wanted to see how well the model generated by Chat GPT performs as-is.
At the end of the training process, the model is saved as an H5 file. While unsure about this file format, I Gather that I need to convert it to a JSON file for loading it onto the browser using TensorFlow.js. I turn to Chat GPT for guidance on conversion and receive a suggestion to use a TensorFlow.js converter.
I encounter a slight issue with the recommended install command for the TensorFlow.js converter. After some investigation, I realize that the correct command is "pip install tensorflow.js" instead of what Chat GPT initially proposed. This serves as the first mistake Chat GPT has made so far, potentially due to the cutoff date for the training data used in its programming.
Once resolved, I successfully run the converter and convert my H5 model into a JSON file, preparing it for loading onto our HTML canvas.
Loading the Model into JavaScript
Converting the Model to JSON
With the JSON model file ready, I proceed to load it into the JavaScript code we previously wrote for our HTML canvas. I Seek assistance from Chat GPT regarding this step and follow the instructions it provides. However, I encounter a CORS (Cross-Origin Resource Sharing) error. This error indicates that I need to load the application from a web server instead of directly opening the HTML file in the browser. I ask Chat GPT for help with this issue, specifically requesting instructions to set up an Nginx web server. Following its guidance, I manage to access my trained model through the Nginx server.
Feeding Data into the Model
The final challenge revolves around converting the data from our canvas into a 28 by 28 pixel dataset, preparing it for input into the machine learning model. I seek Chat GPT's guidance on this matter but encounter glitches and poorly formatted code blocks. The code it generates fails to work correctly, leading to numerous errors. Frustrated, I decide to find my own solution to the problem.
I devise a method that involves copying the drawn image onto a smaller canvas with Dimensions of 28 by 28 pixels. From there, I extract the image data and feed it into the machine learning model. With this approach, everything finally falls into place.
Conclusion
In conclusion, this project has been an exciting journey exploring the capabilities of Chat GPT and its potential to generate code and build applications. We successfully built an AI application that can recognize handwritten digits drawn on an HTML canvas. Despite some challenges along the way, we were able to overcome them and achieve our goal.
It's important to note that while the model produced by Chat GPT performs reasonably well, it is not perfect. It occasionally makes inaccurate predictions, indicating possible overfitting to the training data. Fine-tuning the model architecture and training parameters would be necessary to improve its performance. However, for the purpose of this video, we focused on assessing the model generated by Chat GPT without modifications.
Thank you for joining me on this journey, and if you enjoyed this video, please consider liking and subscribing to support the channel. The code used in this project will be provided in the description below. Stay tuned for more exciting content, and until next time!