 we have this flask web application. We just run this flask application. Well, it should become clearer. Right, so that's the flask application. Right, so the current port. So we're making a machine learning whatever AI that will play this game. It's essentially doing it over here. So before you have removed all this normalized positive and negative peaks, well, we don't actually need them. We have this explanation at the bottom of the HTML as well. I'll start sharing a code of a flask application we are making. It's already running, but we need to keep improving it. So that's the HTML. Here's some feedback. Og tags. You've got two org image tags. The local path slash binary chaos. Okay, okay. Don't worry about the org paths and things like that. Focus on the technical side of things of what's actually being displayed. I'll be sharing some JavaScript and Python code next and then you can tell me what you think later. Your script is all about creating ECG waveforms normal and abnormal. You've got the math nailed down with Gaussian functions and sine waves. This kind of self-documenting code is a lifesaver for maintenance and collaboration. Okay, then I say keep it short. Here's the game logic JavaScript and it will be the flask application Python backend script and then I'll tell you what the problem is. All right, diving into game underscore logic JS. Canvas set up. You've set up the canvas well defining height width and colors. Good choice of constants for easy tweaks later. Scoring system. The scoring logic with hits false alarms great rejections and misses is engaging. It encourages accuracy not just no emojis. Keep it short. Here is the Python script of the back end flask. Your flask backend is well structured. Global variables. You're using global variables to track min max values for normalization. Okay, performance testing. Okay, fuzzy logic. Okay, a couple of things and then let me know if you prefer generating the code yourself or generating prompts for GitHub co-pilot to do it. So the current problem is that the raw frequency is always zero. Therefore, the normalized frequency is not showing anything at all. We are calculating FFT currently. Understand what GitHub is complaining about that should have access to the code is finding the dominant frequency. It's taking the absolute value. So if it's always zero, it means the maximum value is always at the first index. And this is the four of five is EECG waveforms that we have. And let's print the frequency in the dominant frequency. Okay, I placed the image, shared the image of the graph of the different EECG waveforms just overlaid one on top of the other. I think we have all that should be in the waveform JavaScript while keep responses short. Do not use any emojis. Check for DC offset. A DC offset in your signal could influence the FFT output. Try applying a high pass filter to remove any DC offset before performing the FFT. Inspect the scaling. The frequency value from the FFT needs to be scaled correctly to represent real frequency units. HZ. A really good point. Plot FFT. Okay, for whatever reason we have all the values sitting at 300. I mean we could just do minus 300. I don't know why it's like that to begin with. But yes, you essentially can subtract the 300 from all the NP data values. Keep responses short. Minus 0. Let's bring the plot back. It's the frequency response. Since I can only need the one side of it, what do you think? It's becoming quite heavy. I get my CPU struggling a bit. I have a dominant frequency of 3. It's being shown. Yeah, that number 3 makes sense, doesn't it? Get rid of the GUI. Don't need much plot leap. Raw frequency does make sense, but you probably don't need any normalization for it. Okay, now the raw frequency meant to be called the dominant frequency. It actually has the value of 5 here to make sure it's actually displaying correctly. It's not running now, so why does it keep scrolling stuff? Yeah, we have to get rid of that normalization. It's way that it's always a value of 3. This is our output in the Flask terminal. Can you check based on the code provided what the sampling rate is? Remember, this is synthetic. Also, it is interesting that the dominant frequency went from 3 to 5 in this particular case. The abnormality output remained the same. I don't think we need to normalize the frequency considering this is the dominant frequency. Oh no, we might need to normalize it because is there an assumption that all the membership functions deal with values between 0 and 1? All the whole idea for the back end to be agnostic to what the waveforms are. Are you suggesting we should be dropping frequency measurement from this analysis? If so, would you recommend replacing it by another feature? Okay, so how do we change to relative frequency? Do we still need to normalize relative frequency or just use it in the fuzzy logic as is right? So the relative frequency is between 0 to 0.5 and the membership functions are designed to handle 0 to 1. So we can just multiply by 2 and use scale relative frequency. Dominant frequency should only be used once. Over here we should be using scaled relative frequency. Relative frequency is we have dominant frequency use once, relative frequency use once and scaled relative frequency is the one we actually using. Using scale relative frequency and normalize I expect them to be the same. This really works as expected. It's always 0, it's between dominant frequency separately. Okay, there's a problem. I want to bring length. So length of data is 800 data points. It's probably too many, but okay. If 5 divided by 800 we get this small number there. This is not good. Any suggestions? It looks like you've successfully calculated the relative frequency and scaled it up to match the expected range for your fuzzy logic system. With this scaled relative frequency you should update your processed underscore data dictionary to include these new values. Now, this scaled underscore relative underscore frequency is ready to be fed into your fuzzy logic system as an input variable, fitting into the membership functions properly designed for the 0 to 1 range. Keep in mind that if the dominant underscore frequency is showing as 5 and the length of data is 800, a relative frequency of 0.00625 5.800 is correct and the scale value of 0.0125 0.006252 would be the appropriate input for the fuzzy system. Yes, but I'm pretty sure these values will remain super low. They would not be getting up to 1 because 5 seems to be the highest dominant frequency we get and the relative frequency is way too small. I would think not to use the division by the length of the data. Maybe we can use something else instead. So the scaled relative frequency actually dynamically changes between 0 and 1. Understood, if 5 is the highest dominant frequency you're getting and you want the scaled relative frequency to span the full range from 0 to 1 dynamically, you can normalize it based on the maximum observed frequency so far. This way, the relative frequency could adapt to the range of frequencies you actually encounter in your data. This is how you can adjust your code for dynamic scaling. Keep track of the highest dominant frequency seen so far. Let's call it max underscore dominant underscore frequency Each time you calculate a new dominant frequency, update max underscore dominant underscore frequency if the new value is higher. Normalize the dominant frequency against max underscore dominant underscore frequency to get a value between 0 and 1. PYTHON 1. Copy code hash update max underscore dominant underscore frequency The new dominant frequency is higher if dominant underscore frequency greater than max underscore dominant underscore frequency. Okay, okay, okay. I don't know why it's reading the code as well. Hash normalize the dominant frequency against the maximum observed so far scaled underscore relative underscore frequency equals dominant underscore frequency. Max underscore dominant underscore. Okay, maybe stop reading altogether. Yeah, it's really hard to talk to this board properly, especially when it's giving longer responses with sections and stuff in them. Right, so instead of that, we're monitoring the maximum dominant frequency. And if it's higher than the current one, then we will be saving it. Dominant frequency is scaled. Okay, wait a sec. Can't get confused. Start getting rid of stuff. Okay, we have a dominant frequency, then we're saving the maximum dominant square wave frequency in the scale scaling. What in the name of this should be scale dominant frequency instead of scaled relative frequency? You'd think we don't need the relative frequency anymore. Quick response. Yes, in this context, since you're scaling the dominant frequency based on the maximum observed frequency to date, a more accurate term would be scaled dominance frequency. You can drop the relative frequency term if it's no longer providing additional context or value to your analysis. Here's the updated code snippet reflecting this change. Now, scaled underscore dominance underscore frequency is what you would use for your fuzzy logic analysis, which will be between zero and one dynamically adjusted based on the data you receive. That's the way that in maximum frequencies, if the new dominant frequency is higher in a one liner, instead of two lines, don't need two lines. And then we have, and we're normalizing, yes, and we call it the scale dominant. What? Just making mistakes with the name should be scaled dominant frequency. Now in the process data, it's not called process data, it's called something else. This is the code I have so far with the bits I modified myself. Can you fix it or provide prompts or GitHub co-pilot to do it? Fix the code with your modifications for the scale dominant frequency. You'll need to make a few changes. Initialize max underscore dominant underscore frequency, add a global variable to keep track of the maximum dominant frequency observed. By process data, update the process underscore data dictionary to include the scaled key instead of normalized under the frequency field. Update global ranges function. Since you're no longer using normalized frequency, you need to remove or modify the code that updates global ranges for frequencies. Fuzzy logic input. Ensure that the fuzzy logic analysis function uses the scaled dominant frequency correctly. Here are the code modifications. These changes should align your code with the new approach of using a scaled dominant frequency. Yeah, why is this not a link, you know, that's all global marks, so we don't no longer using normalized frequency. Fuzzy logic input. Ensure that the fuzzy logic analysis function uses the scaled dominant frequency correctly. We can't deal with the whole code, there's too much code for it. So we don't need to normalize the frequency, we're getting an error, and this is the kind of stuff GitHub co-pilot can deal with. We don't need chgpt4 for it, and it should be better how to solve this error, keep it short. The error is because global underscore max underscore dominant underscore frequency is not being recognized as a global variable inside your function. Okay, it's clear that don't have the analysis result, so this thing should be playing the ECG detection game instead of a human. The analysis results are missing, we're still using normalized stuff, but we actually decided not to normalize the features extracted from the signal. This meant we had machine learning playing this game, it's been kind of working, we were trying to tune parameters. So using a fuzzy logic, I don't know if you're familiar with fuzzy logic, but you will after I will. After we finish with this tool, but currently we have this pesky errors coming in, something with the global variables being defined in wrong places, GitHub co-pilot is kind of hopeless at solving errors, chgpt4 is better. Can you provide a prompt for GitHub co-pilot to make sure the global max dominant frequency global variable is assigned correctly? Yeah, we have global variable, script need to update this variable with a function called process data, so we have process data, finding a global variable with what a function. It's to do the normalization, so for the what are they called in fuzzy logic, the things to work, you need the thresholds, so we need to normalize all the parameters between zero and one. What we are struggling with is run the flask application, still running it locally. Yeah, this normalization is better. So the dominant frequency in this waveform is three or five or whatever, and then it's taking five as the maximum, so because five is the highest number we had so far, so the normalization will return one, and if it's three it will return 0.6, so it makes sense. However, the error message indicates a problem with accessing a normalized key, likely in a dictionary within the analyze underscore waveform function. This might be because the key does not exist in the dictionary you're trying to access. So this is the actual fuzzy logic algorithm, this is all the rules for it, yeah this should be a good example of the difference between GitHub co-pilot and GPT-4, so GPT co-pilot series. The error normalized is likely due to the fact that the frequency data structure in the processed underscore data dictionary no longer contains a key called normalized. Okay, maybe I was wrong, and co-pilot is able to seem to be able to deal with it as well. Sometimes it's putting these links in the chat, which really helpful because they go to the way you need to make the change, yeah like this one should be a link, which it sometimes is, it's just weird. Trying to access normalized, yeah, but still want access, yeah, so amplitude stays normalized because we are normalizing amplitude, okay, pop it in the old value, what? Okay, replace default value with the value that makes sense in your context. This is where we shut down GitHub co-pilot, right, let's see if this one is any better expected to be better. So we have the, yes we are in the fuzzy logic analysis, we have the abnormality analysis frequency at rate of what the GitHub co-pilot told us to do, so scale is the same, okay, then we have somewhere normalized frequency membership, yeah this bit, okay this should be scaled, it's all case sensitive, isn't it? Scaled frequency data, yeah that's a membership function, so essentially we're saying that the frequency membership is low, if the frequency is low and we're using this scaled frequency, okay, same and we're returning frequency membership, frequency membership, okay, right there, application is not actually running, by the way, go check out buddykills.com if you haven't done it already, my CPM for today is very high, this is your chance of supporting the project, yeah so this is the ECG game that we're trying to modify, that's the one that humans can play, so if you go on the side you can play that game, there's a video, yes there are ads, sorry for that, yes there would be a video somewhere of me getting to about 1000 points, you can see if we can beat that, it does become faster every 50 points also, so go check it out, now we're doing this fuzzy logic business, but we're essentially combining this too and later if this works well we'll be also adding neural nets to it, to make it even better and so you can compare different algorithms, it's clear, it's got so, run it again, still getting somewhere, okay this might be a longer debugging, it should also stop something up or every time we use frequency we should not have the normalized bit anymore, yeah like over here, is that it, that there went away, so yes we don't need this line, now we have, oops, keep pressing ctrl c instead of ctrl shift c, pretty annoying frequency, we have a scaled frequency, it's like the, it's being stored somewhere, no not here, yeah this one, yeah we have frequency, row and scale, so it should be fine, it should be fine, yeah probably we'll hit the limit of GPT-4 as this, what's it, 40 messages per three hours currently, hit this limit, still getting the same error, it's something this name, okay I have the, I checked the code it is, the script is exactly as you are suggesting, do I also need to update the game logic JavaScript, quick responses please, yeah it's always tempting to go, use github co-pilot, should I use github co-pilot for this, so are you gonna generate, change the update, the code, do we need the game logic JavaScript again, let's clear the chat, we need to do it with a workspace function so it can find, and so we need to select the Python script, then we go update the Flask back in, yeah so I'm not sure which way is it, but in theory when you select workspace it should find the script it needs, maybe, maybe not, this won't be JavaScript right, no, etch, my Flask endpoint, shouldn't you know what the endpoint is, etch, okay github co-pilot doesn't seem to be able to make the necessary changes, maybe I'm not using it correctly, here is my JavaScript, can you suggest updates, now I'm still getting the error above, can you double check the code, looking at the process data output, it seems processed underscore data has raw key and a frequency but not a scaled key, yeah the code seems okay but I'm still getting the same error, here is the whole Python script again, yeah I'll be really hitting that limit, we are going in circles here, can you look at the entire code, the updated code that I shared with you, and try a different approach for resolving this error, in the analyze underscore waveform function where fuzzy underscore logic underscore analysis is called, ensure the processed underscore data contains the scaled key and the frequency, so we're printing it out, process data, no it does not have it, a process data seems to only have the raw frequency, it keeps changing, alternating within those two lines, obviously the second one is better, should be faster, yeah frequency, raw and scaled, okay returning JSON5, can you regenerate the whole function, yeah we'll do this, continue next time, I'll see you later, put your questions, comments, let's regenerate the analyze underscore waveform function in your flask application, this function is responsible for handling the post request to the analyze underscore waveform endpoint, processing the waveform data received, analyzing it using fuzzy logic, and then sending back the analysis results, here's the regenerated analyze underscore waveform function, in this function, the waveform data is extracted from the post request, the data is processed using the process underscore data function, which should calculate and return metrics like raw frequency, scaled frequency, peaks, and amplitude, a check is added to ensure that the scaled frequency data is present, the fuzzy underscore logic underscore analysis function is called to perform the analysis and the process data, results of the analysis, along with the process data, are structured into a response dictionary, this response is sent back to the client in JSON format, ensure that the process underscore data and fuzzy underscore logic underscore analysis functions are correctly implemented and handle the data as expected for this analyze underscore waveform function to work correctly