 should be live, just give me a sec, just move that the gesture groove is just live and now just made it, just deployed it now, there's no link to it anyway, so it's only, you will only see it if you're watching this live stream, the gesture groove is live and will be, yeah so we'll have now music in our stream, just want to pop it into, so get into wave on detection and using air in sec, give this stream is, hit the link to share this stream, right here it is, here posted mainly, I'm mainly on LinkedIn, just if you don't know, there should be a link somewhere on YouTube as well, if you want to add me on LinkedIn, you're more than welcome to, maybe just still messing around with OBS, I don't know if this text is useful or not, probably not, wait I do need this separate window, by the way, so this is all, still giving some errors, we can try and fix them later, currently it's just that window, kind of have to, yeah get rid of that text, I don't know how useful it is, yeah I don't know, this window is not great because I can't overlay, I wanted to, this should be okay for now, so we know, yeah the full screen is not visible there, still don't know why we're getting these errors, but we will, yeah hopefully someone will ask about this as well so we can go back to this, it's kind of vaguely biomedical related, well I mean it is biomedical related because it has human hands, has music, we love music, I did want to do something with the face as well when generating those music, by the way, just fix, yeah now the cursor should not be showing on the inset at the bottom, where is it, yeah so the other hand is annoying, one of the hands is super annoying, we can fix that one later as well, I was actually thinking having them as separate musical instruments and stuff, but yeah we'll turn all this into prompts later and pop on the side, so it's only for you if you're watching the live stream and the gesture groove is live and it's all JavaScript based, so you can have all the source code, it's there for you and just go your, you know, what to do, go your browser elements, yeah all the source code should, I know that's the HTML where the JavaScript is at, JavaScript, JavaScript, source and network, okay maybe I'm wrong, I thought when you load a page all the JavaScript should be accessible, no it's not that one, it's the main JavaScript, this one, how do you actually, yeah we might try it later, but yeah the JavaScript should be there, oops, yeah that's not good, just me hiding image, we want to use the image and I also shouldn't be clicking on this page because there is a test button for edge testing the sound and this is not good as well because it stopped working a significant different screen, there is two different screens in white, window capture pipe wire and x composite whatever that is, which one I'm currently using, I don't know, I assume the first one is pipe wire, let's try, right yeah this one has more options and things, a window gesture music and we can crop this one, we can crop it just good, cropping the top, don't have to worry about the bottom I think and yes this window is better because it can be cropped and also can make it a bit bigger, can cut it over there, the other thing is it should be still working, yes it's still working when the window is hidden, so that's good, can get rid of that one, we need this text, that's the stuff we will be doing, sometimes YouTube places stuff there, so we might want to get rid of it or leave it, let me know in the comments, do let me know, so we'll be clustering the GP4, it's a co-host, actually we haven't started yet, those are false detections on my face and I don't know why is that happening, check that code later but it also only happens when I look at the other screen, so if I look forward it doesn't happen and yes we will have music in the stream, hope you like it, yes we'll try to improve it, there's someone asking about the source code, so yes in theory all the Deakin University ads, it's actually quite relevant, so yeah we like those ads, I mean those ads are targeting me obviously, hopefully you will be getting ads that are relevant to you, someone's asking about the source code, yeah that's pretty annoying, keeps detecting a hand on my face, I don't know why, I thought I'd fixed it last time, should we look at it now or do it later, this will be pretty annoying to edit, edit it out later if I want to turn some of the stream into a video, by the way we automated that as well, we have a script for video processing just removing all the silent bits, so if I'm not talking for a while, just get rid of it and chop the longer live streaming tool, short bits that actually have content in them, I was also thinking adding a feature that detects motion on the screen if something is happening on the screen, but most of the time there is because I'm doing something, I forget to talk, it will get rid of those silent bits, so someone was asking about the source code, I was having trouble with github with github, github works okay, but then there's something wrong, yeah I was able to commit actually the video processing for testing onto my, onto the binary chaos github page that worked fine, but then the other stuff, yeah it just doesn't want to do it, it's producing all these errors, if you're joining in please do say hello even if you haven't checked the binary chaos.com website yet, that's okay, so check what's happening with github later as well, I'm pretty sure also for this music face detection thing, the code should be just on the website because it's all front end, but yeah I need to double check that later, I keep saying we can double check this later, if I mention something probably just should do it now, but no we will do it later because, so on the website we have those four, the big four at the bottom that are currently not working, they had the original LabVIEW stuff, might as well get the GPT let's turn all this into, yeah so we are live streaming together, we'll focus on fuzzy logic, so we're doing a fuzzy logic application in Flask, we want for it to be available on our website, we have code we generated in the past, but that code has quite a few issues, I will share the code with you in a bit and we either have to fix it or produce prompts as to how to generate a similar code and we will start generating this code from scratch because we really spent too much time on it, yep. we can decide whether to fix it or create prompts to generate a new one from scratch, looking forward to seeing the code and getting started, okay so GPT is keen to help, that's great, lots of things, yeah we will be once the prompts are generated clear, once you generate specific prompts we also have access to github co-pilot, so we might be using that as well, yep, got it, drive, Benno, once we have the prompts for the fuzzy logic application we can leverage your github profile for implementation or reference, this sounds like a well-rounded approach to streamline the development process, feel free to share the code or specific areas you'd like to focus on, okay we will be uploading html, javascript and python code, please do not generate anything until I say the whole code is available for you, yep, absolutely, drive, Benno, I'll wait for your signal that all the code html, javascript and python is available before proceeding with any generation or analysis, just let me know when you're ready, guess we have the html, great, I see the html code for your fuzzy logic waveform detector, it looks like a comprehensive setup with interactive elements and integration of external libraries for enhanced functionality, I'll wait for the rest of the code, javascript and python, to get the full picture and see how we can proceed, let me know when you're ready to continue, I've reviewed the main js file for your fuzzy logic application, it's well structured and handles various functionalities like fetching data, updating graphs, and processing the fuzzy logic parameters, this javascript code interacts with the user selections and the server side processing, looking forward to the python code to see how the server side logic and fuzzy processing are implemented, please share it when you're ready, thanks for sharing the python flash code, drive, Benno, it looks well organized and handles the core functionality of your fuzzy logic waveform detector, including waveform generation, noise addition, and fuzzy logic analysis, from what I've seen, your code seems to have a solid foundation, if you're experiencing issues, they might be in specific areas like the fuzzy logic analysis, data handling, or the flask setup itself, could you specify the problems you're facing, we can then focus on those areas, either debugging the existing code or generating new prompts as needed. Yeah, let's run it, run the flask application, can show it to the bot as well, it doesn't fit this screen, let's do this quickly, no, no, no, no, you have the screen, okay, we can do this, so this is what they currently the front, this is currently what the front, this, this is currently what the front panel looks like, there are a couple of issues with it, if basic things like all the charts do not fit the one screen. And more importantly, that the detection of the fuzzy logic is incorrect, we would like an option to improve upon it from within the round panel. Yep, keep calling them front panels, because that's what used to be called in lab view, that's lab view, lab view, lab view. Looking at the screenshot, I see two main categories of issues, layout concerns and the functional aspect of fuzzy logic detection. For the layout, the graphs need to be resized or arranged to fit one screen properly. This might involve CSS adjustments to make the display responsive, we may consider using a tab or accordion layout to manage the visibility of graphs without overwhelming the user. Regarding the fuzzy logic detection being incorrect, it could be due to several factors. Incorrect membership functions or their parameters. Error in the rules design or how the minimum and maximum functions are applied. Issues with the defusification method, in this case, the centroid calculation. The noise level might be too high, affecting the accuracy of the detection. Review and refine the membership function parameters. Modify the fuzzy rules to better reflect the characteristics of the waveforms you're detecting. Ensure the noise addition is not distorting the signal too much for the fuzzy logic to process effectively. Implement a user feedback system where users can indicate the correct detection and use this data to fine-tune your system. Would you like to start with adjusting the layout or shall we focus on improving the fuzzy logic detection accuracy first? Yeah, we have a CSS that I'll share with you later. We can deal with it later, but yes, we do like to review and refine the membership function parameters and modify the fuzzy rules. The noise addition, no, we made sure it's not distorting the signal too much. User feedback is a nice idea, but then we can just in the code have a calculation for correct responses, which can be easily tested by looking at the name of the input signal and matching it with the output string. Now just one note about and note about the input and output strings. The input strings have, you can just check in the code, but for example, they have a left trapezoid, right trapezoid and a trapezoid, whereas the output is more of a family of the signal. It will be, it should be returning trapezoid for all these three inputs. Understood. Drive. Beno. Let's start by addressing the fuzzy logic detection accuracy. We'll focus on refining the membership function parameters and modifying the fuzzy rules. We'll also ensure the noise addition isn't too high, which could be distorting the signal. Here are some steps we could consider. Refine membership functions, adjust. Adjust the shapes and ranges of the membership functions, low, mid, high, for both symmetry and sharpness to better match the characteristics of your waveforms. Test different types of membership functions, e.g. Earsian, Triangular, Trapezoidal, to see if they offer better precision. Review the rule base to ensure it accurately reflects the relationship between the input features, symmetry and sharpness, and the waveforms. Consider adding more rules if the current set does not cover all possible scenarios or overlaps too much. Check the amplitude of noise being added. It should be significant enough to test the robustness of the detection, but not so high that it obscures the waveform features. Implement an automated check within the code that compares the input waveform name with the output classification to quickly gauge accuracy. This can be used to iterate over different configurations and find the best performing parameters. For inputs like left trapezoid, right trapezoid, and trapezoid, ensure that the system is tuned to recognize these variations as part of the trapezoid family, possibly by adjusting the centroid ranges that determine the output classification. Would you like to proceed with these refinements step by step, starting with the membership functions, or do you have a different approach in mind? Yeah, I would like to do refinement and modifying fuzzy rules eventually, but we would like a more systematic way of doing it. Is there currently a display, and the current display of the fuzzy logic settings doesn't seem complete? We also have a problem in the percent detection error measurement. Another thing is that the output wave called undefined should affect the percent unknown in the detection error and the unknown percentage of the time. It doesn't seem to work at the moment. We might also want to adjust the names used unknown instead of undefined as the output waveform. Certainly, drive. Beno. To systematically refine and modify the fuzzy logic rules, as well as address the detection error measurement and the output labelling, here's a structured approach you could take. Systematic refinement process, S. Establish a baseline by running the current system with known inputs and recording the outputs and errors. Identify the range of inputs where the system performs pulley. Incrementally adjust the membership functions and rules, focusing on one parameter at a time while monitoring how changes affect the output. Ensure that the dashboard displays all relevant fuzzy logic parameters, including membership function shapes and rule activations. This will help in fine-tuning the system. If necessary, extend the UI to allow real-time tweaking and visualization of these parameters. Revise the function that calculates detection error to accurately reflect discrepancies between the input waveform names and the output classifications. Ensure that the undefined output is correctly integrated into the error calculation and that it is treated as unknown to be consistent. Update the code to use unknown instead of undefined to maintain consistency and clarity in the output labels. After each change, validate the system's performance with a set of test waveforms. Automate this testing process to quickly assess the impact of each adjustment. Implement logging to keep track of changes and their effects on system performance. Consider a feedback mechanism where the system suggests the next parameter to tweak based on the error patterns observed. To implement these changes, you'd start by reviewing and updating the Flask app through the process as the fuzzy logic. Make sure the error calculation is robust and that the output labels are consistent. From there, you can move on to refining the logic and then finally to enhancing the UI for better visualization and control. Let's begin by correcting the output label to unknown and ensuring the detection error and unknown percentage calculations are accurate. Would you like to proceed with this or do you need assistance with another aspect first? And let's do that. We can generate the code or are we getting GitHub co-pilot to do it? Excellent. Drive. Beno. If you're set to proceed with the adjustments, here's a concise checklist for your next steps. Update output labeling. Replace. Replace any instance of undefined with unknown in your code to maintain consistency. Review the calculate underscore detection underscore error and calculate underscore unknown underscore percentage functions to ensure they correctly process the outputs and consider the unknown label. Start by tweaking the membership function parameters slightly and observe the changes in detection accuracy. Adjust fuzzy rules one at a time to see their individual impact on the system's performance. Implement a script to automatically test various inputs and measure the system's performance, providing you with immediate feedback on changes. Incorporate a logging system to record each test's inputs, expected outputs, actual outputs, and any errors. Plan for a future UI update that will allow real-time visualization and adjustment of fuzzy logic parameters. Proceed with these modifications and test after each change to evaluate the impact. If you need further assistance at any point, feel free to reach out. Good luck with your refinements. Good luck if you have refunds. So it's telling me what to do obviously. It seems like it doesn't plan to generate generating any code. That's unfortunate. We're not forced you to or use co-pilot. But first, yeah, that seemed to be a simple task for co-pilot. Can you clear? Keep showing your hand on my face to fix that. To replace instances of undefined with unknown, you need to adjust the part of your Python code that assigns the output waveform label. Specifically, you should look at the fuzzy underscore logic function where the output underscore wave variable is set. Here is the updated snippet from your fuzzy underscore logic function. Ensure that this change is consistent throughout your flask application. If you have JavaScript or HTML code that also references undefined, you would need to update those two, unknown, as well. After making these changes, you should also update any part of the code that uses the output label to calculate error rates or display results to the user to ensure it recognizes the new, unknown label. Once these changes are made, test your application to ensure that the label is correctly applied and that the detection error and unknown percentage are calculated accurately. If any issues arise, review the changes to confirm they've been applied uniformly across all relevant parts of the code base. I find that myself unknown right, output wave. So we're changing them to no, unknown is what we want it to be. So where's the undefined coming from? It's nowhere to be found. By hallucinating, output wave undefined. Where is it coming from? Okay, that's super weird. All right, so it's, okay, I might be mistaken. Okay, the undefined is actually coming from the code clear. Okay, I just realized undefined, okay, I just realized that the undefined is actually coming from the code, from the execution of the code. It's an actual error. It seemed to be an error. But that's what is only happening sometimes. It's not happening for all input waveforms, actually, most input waveforms work. Okay, I think we'll have to rewrite this code. Ah, I see, drive. Beno. If undefined is coming from an error in the code execution, it likely means that at some point, your code is trying to reference a variable that either does not exist or hasn't been assigned a value. The output underscore wave variable isn't being said due to a path in the logic that isn't accounted for. There's an error in the processing functions, calculate underscore symmetry, calculate underscore sharpness, etc. That causes them to return an unexpected result. There's an issue with the JSON response structure where the output wave property isn't being set correctly. To track down this error, you would check the JavaScript console in your web browser for any errors that might provide a clue as to where the undefined value originates. Add error handling and logging in your flask routes to catch and log any exceptions or unexpected behaviors. Ensure that your JavaScript fetch function properly handles non-200 status responses and any exceptions that might occur during the request or response processing. Here is an example of how you might add error logging to your flask route. And in your JavaScript, you can add a check for NON200 responses and log the error. By implementing thorough error handling and logging, you should be able to pinpoint where undefined is coming from and correct the underlying issue. Remember to test after making changes to ensure the issue is resolved. There's actually too many issues with this code. Can we use a different approach? A more inclined to restart the whole development from scratch. Yep. Starting fresh can sometimes be more efficient, especially when the existing codebase has multiple intertwined issues that are difficult to unravel. Here are the steps for a clean restart. Define clear requirements, outline. Outline the functional requirements and goals of your fuzzy logic application. Determine the types of waveforms and the noise levels you want to detect and analyze. Decide on the structure of your flask application, including endpoints, data flow, and error handling. Consider modularizing your code to keep it organized and maintainable. Define the membership functions and rules based on your requirements. Choose the right fuzzy logic operators and defuzzification methods. Develop a testing strategy. Create a set of test cases that cover all your waveforms and the different noise conditions. Ensure you have a method to validate the output of your fuzzy logic system against these test cases. Implement in stages. Start by coding the core logic of the waveform analysis. Develop the front-end components and visualization after the back-end logic is solid. As you develop, continuously integrate new code and test against your test cases. Use version control, like Git, to manage your code base and track changes. Document your code and your fuzzy logic system settings as you develop. Write user documentation on how to use the application. Once you have a basic version running, gather user feedback. Use this feedback to refine your system and improve the user interface. By following these steps, you should be able to create a more robust and maintainable application. Yeah right, we might fix Git first because we had trouble pushing or pulling above. Then we get back to this one, probably restructuring the whole thing. Starting from scratch, could you generate the prompts for GitHub co-pilot to use? Yep. Certainly. To use GitHub co-pilot effectively for your project, you'll want to provide it with clear, concise prompts to describe the functionality you need. Here are some example prompts you could use. Write a flask application with one root for the home page that renders an HTML template. The home page should include a form where users can upload waveform data. Create a Python function that initializes a fuzzy logic system with membership functions and rules for analyzing waveform data with noise. The system should have three inputs, waveform type, noise amplitude and actual waveform data. The actual waveform data is being simulated. Define a Python function to generate synthetic waveform data. Ah yes right. The function should be able to create square, triangle and trapezoidal waveforms with a specified level of noise. So we have we happy with those as they are maybe accept the triangles. The triangles should be look more like triangles. Now the noise amplitude need to make sure it's actually going to zero. The slider that the slider goes to zero. What else do we have? Develop a Python function that takes noisy waveform data as input and uses a fuzzy logic system to classify it as either square, triangle, trapezoidal or unknown. That's right. Write JavaScript code to visualize waveform data on a web page using Kotli. Include graphs for the input waveform, the noise added waveform and the fuzzy logic system's output. Yeah what need the do we need the fuzzy logic system output? Do we need the input sets as well? Or not? Yes we'll do error handling. Add functionality to a web page that allows users to provide feedback on the fuzzy logic system's classification accuracy. This should include form elements to capture user input and a root in the Flask application to process this feedback. This music is good reminder for you to move a bit. Well I'm already standing but moving my hands could help as well. Write unit tests for the Flask roots, fuzzy logic system and waveform generation functions. Use pitis to ensure that each component is working as expected. You can input these prompts in the github co-pilot in your code editor and it will generate code snippets based on them. You'll still need to review and possibly refine the code it generates that it can give you a significant head start on your development process. Well why do I need to review surely github co-pilot generating it perfectly working code? If you're facing issues with gith operations like push or pull, you might want to verify your configuration, remote repository access rights and network settings. Let me know if you need assistance with gith or any other part of your project setup. Yeah we'll do the gith later, we will do the gith settings another time. Yep, let's probably do this as the last prompt. I'll address the points you've raised one by one. CSS and waveform display, the CSS should be designed to ensure that the simulated waveforms on the website are displayed accurately and they're visually distinct, even when the noise amplitude is set to zero. You want the triangle waveforms to be more accurately represented, especially when visualized on the site. The slider for noise amplitude on the front end should allow for a range that includes zero, giving the option to view the waveform with no added noise. The fuzzy logic system needs both input sets for the waveform and noise data, and output sets for the classification results. For the system's accuracy, you'll need to review the error and learning mechanisms to improve classification over time. It's good to remember to move around, even if it's just moving your hands, to stay active and maintain well-being during development work. While github co-pilot can generate code snippets, it's essential to review them to ensure they meet your project's specific needs and adhere to best practices. The eye is a powerful tool, but it's not infallible and won't replace the developer's oversight. It seems you plan to address the gith setting soon. This is crucial for maintaining a robust version control system. Remember to take breaks, stay hydrated, and keep moving. When you're ready to work on the gith settings or if you need further help with your code and the setup,