 We should start quickly, so now I have a screen dedicated just for JGPD, using Copilot in Visual Studio Code. Let me know if the stream looks okay or not, yeah I'm not sure about Twitch but the YouTube is like several folds better than LinkedIn for videos and streaming. Twitch meant to be good for streaming but just started using it. We have this web application, Plask web application that we've been working on forever for quite some time. So as you know this whole live stream YouTube channel is all around this website, so if there is any questions or anything, most of the answers should be there, however all the live development happening live on YouTube, so a bunch of videos on YouTube as well, you can check out all the essentially all the recordings, all the live stream recordings not then being edited into videos if there is anything interesting. What is that cap? That's where the ad was. Okay so currently we're doing fuzzy logic work and we'll be adding neural networks as well. We just run this Flask application tool, it should become clearer what's what, right so that's the Flask application, we pop it onto this side and console as well, it should be good enough. So just work with these two windows, so hopefully we don't get distracted by anything else. Right so the current pot, so we're making a machine learning whatever AI that will play this game, it's essentially doing it over here, the new GPT-4, so yeah full disclaimer using GPT-4, I just have it on an out screen, somehow using github copout for developers that's fine, I don't think anyone can consider it, considers it cheating or anything, but then chgpt for text generation stuff is a whole different story, so I'm not sure why it's dead. I think people still kind of getting the hang of what this all means. So before we 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, let's pop it into chgpt-4 quick responses, so we're developing during a live stream, so if you can keep your responses short that will be great, if you want to try to be funny that's okay 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. Yep. Got it. Short and snappy with a hint of humor. Ready for the code when you are. Cool. Oops. No emojis. Got it. Yeah, I have no emojis in the instructions, in the chgpt-4 instructions, so that's the HTML. Nice. A biomedical twist on gaming. Here's some feedback. Og tags. You've got two og, image tags. The local path. Okay, okay, okay, don't worry about the og paths and things like that. I want to 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. Yep. Sounds like a plan. Focusing on the technical aspects makes sense. I'm all set to dive into your JavaScript and Python code. Get away and I'll be ready with insights, suggestions or a joke or two if the mood strikes. Let's get technical. Hammer and wrench. All right, diving into waveforms, J.S. Here's a breakdown. Waveform generation. Your script is all about creating ECG waveforms, normal and abnormal. You've got the math nailed down with Gaussian functions and sine waves. Clever use of math. Pi to mimic the ECG wave patterns. All variations. You're creating variations in the QRS complex, P wave, S wave and T wave to simulate different types of cardiac anomalies. This is a great educational tool for understanding ECGs. Randomized abnormalities. Using math. Random to decide whether a waveform is normal or abnormal adds a nice touch of unpredictability to the game, simulating real world scenarios where abnormalities aren't always predictable. Function naming. The names like no P wave, not wave, not wave make it super clear what each function is doing. This kind of self-documenting code is a lifesaver for maintenance and collaboration. Okay, okay, didn't I say keep it short? Didn't I say keep it short and snappy? Yep. Got it, shorter this time. Your waveforms. J.S. looks good. Nice math for ECG simulation and smart use of randomness for real-life feel. Maybe refactor to reduce repeated code. Check performance, and don't forget to touch a fuma. Ready for the next code snippet? Close up medium light skin tone. Get rid of the emojis, no emojis, no more emojis. Here's the game logic JavaScript, then there will be the flask application Python backend script, and then I'll tell you what the problem is. Alright, diving into game underscore logic, J.S. Canvas setup. 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 randomly. No emojis. Keep it short. Here is the Python script of the backend flask. It's really... Your flask backend is well structured. You're using global variables to track min, max values for normalization. Okay, okay, okay. Performance testing, okay, fuzzy logic. Fuzzy logic, impressive use of fuzzy logic for analyzing the ECG data. The fuzzy rules and membership functions seem well thought out. Yeah, whatever. Okay, now, okay, 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, also, okay, let's do this one first, yep. The issue with the raw frequency always being zero could be due to how the frequency analysis is performed in the process underscore data function. Let's troubleshoot. FFT application, check if the fast Fourier transform, FFT, is correctly applied to your data. The line dominant underscore frequency equals np.abs, frequencies, rgmax, should give you the index of the highest frequency component, not always zero. Data input, ensure that the data pass to process underscore data is correct and not a flat line or constant value, which would result in a dominant frequency of zero. FFT interpretation, remember that the index returned by rgmax corresponds to a frequency bin. You might need to convert this bin number to an actual frequency value. Debugging, add print statements before and after the FFT operation to see what your frequencies array looks like and what dominant underscore frequency you're getting. Alternative analysis, if the issue persists, consider using a different method to determine the dominant frequency, like finding the first peak in the FFT magnitude spectrum that exceeds a certain threshold. If you're comfortable with direct coding, you can modify and test these changes yourself. If you prefer guided suggestions, GitHub co-pilot can generate code snippets based on your prompts. Whichever works best for you, I'm here to assist. So the first one, 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, wait, now we're printing it already. Data input, ensure that the data pass to process underscore data is correct and not a flat line or constant value, which would result in a dominant frequency of zero. We have control V, control C, in this terminal it should be control SHIFT, and it's printing, can you? Oops, yeah, plot, plot is not fine, yeah, this is the four of five is E, C, G, waveforms that we have, right, so, ah, and yes, they have a good thing, they raise, okay, and it's printing the dominant frequency, okay, I place the image, share the image of the graph of the different ESG waveforms, just overlay one on top of the other, I think we have four that should be in the waveform JavaScript file, keep responses short, do not use any emojis, yep. From the image you provided, it looks like we have an overlaid graph of different ECG waveforms, if the issue is with the raw frequency always being zero, here's what you can do, verify the FFT output, ensure that the output from the FFT operation is correct, the peak should represent the dominant frequency, not zero, check the input waveform, make sure that the waveform data being passed into the FFT is correct and hasn't been modified or corrupted before the analysis, 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, okay, for whatever reason we have all the values sitting at 300, which is not cool, 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 300 from all the NB data values, keep responses short, short, indeed subtracting 300 from NB underscore data to center your waveform data around zero before the FFT analysis is a solid approach, this will remove any DC offset and could resolve the issue with the raw frequency being zero, here's a snippet to modify your process underscore data function, remember to apply this adjustment right after converting the data to an umpire ray and before performing any analysis, give it a shot and let's see if it resolves the raw frequency issue, okay, minus zero, bring the plot back, the frequency response, it's like I only need the one side of it, what do you think, yep, GPT4 became super slow, the FFT plot you've shown indicates that you're seeing the typical FFT output for real valued signals, which is symmetric around the Nyquist frequency, the FFT output for a real valued time series will be mirrored across the halfway point of the spectrum, this is expected due to the complex conjugate symmetry property of the Fourier transform of real valued signals, for your analysis, you should only need to consider the first half of the FFT output, as the second half is just a mirror of the first and doesn't contain additional information about the frequency content of your signal, additionally, the first bin, zero frequency, represents the average or DC component of your time domain signal, if your signal is properly centered around zero, this should be close to zero, to get the dominant frequency, you should ignore the first bin when looking for the peak, as it represents the DC component, only consider the first half of the FFT output, here is a refined approach, by using one int len frequencies to you exclude the DC component and only consider the first half of the spectrum, the plus one adjusts for the index offset caused by excluding the first element, this should give you the index of the dominant frequency component within the first half of the FFT output, you're plotting into figure one, it's becoming quite heavy, I get my CPU struggling with the dominant frequency now, the dominant frequency of three, so being shown, yeah that number three makes sense, doesn't it, get rid of the GUI, need my plot leap, well raw frequency does make sense, but you probably don't need any normalization for it, okay now the raw frequency, which meant to be called, meant to be called the dominant frequency, it actually has the value of five here, to make sure it's actually displaying correctly, not running now, so why does it keep scrolling stuff, yeah we have to get rid of that normalization, it's weird that it's always a value of three, remember quick responses, great, if you have a dominant frequency value now, that's progress, to ensure it's displayed correctly, check the index to frequency conversion, validate the conversion from FFT index to actual frequency using the sampling rate, remove normalization if not needed, if the normalization isn't providing useful information, you can comment it out or remove it, here's a quick snippet to get the actual frequency from the index, this calculates the frequency in HZ corresponding to the dominant frequency index from the FFT, if you're seeing scrolling behavior in the output and it's not desired, make sure you're not accumulating or continuously appending data in a way that's not intended, check the logic where you update the waveform and the canvas drawing functions, this is output in the flask terminal, can you check based on the code provided what the sampling rate is, remember this is synthetic, code keep responses short, also it is interesting that the dominant frequency went from three to five 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 zero and one, based on the javascript code provided it appears that the waveforms are generated synthetically without a specified sampling rate, the sampling rate would be necessary to convert the FFT index to a physical frequency in HZ, in synthetic data if the timescale isn't defined the frequency units in the FFT will also be arbitrary, stability of the dominant frequency, the change from three to five suggests variability in the waveforms generated, if you expect the dominant frequency to be consistent check the waveform generation process, normalization, if you're using fuzzy logic with membership functions defined between zero and one then normalization of the frequency to this range is necessary, consistency in abnormality, the same abnormality score despite different dominant frequencies suggests that the fuzzy logic rules are perhaps not sensitive to the changes in frequency or the frequency is not heavily weighted in determining abnormality, remember to cross verify the integration between your javascript that generates the waveforms and the python code that analyzes them ensuring consistency in how the data is handled and interpreted, all the whole idea for the back end to be agnostic to what the waveforms are quick response, yep understood, if the back end is meant to be agnostic to the waveforms specifics then it should not rely on a fixed sampling rate for frequency analysis, it should just analyze the pattern of the waveform data it receives, the normalization and analysis should be based on the patterns and features extracted from the waveforms regardless of their specific time base or sampling rate, well right so are you suggesting we should be dropping frequency measurement from this analysis, if so would you recommend replacing it by another feature, not necessarily drop the frequency measurement but if the time base is arbitrary the raw frequency value index from FFT itself might not be as informative without a sampling rate context, instead consider using features derived from the waveform that don't depend on the actual time base, relative frequency use the index of the dominant frequency relative to the length of the waveform, it provides a dimensionless metric that could still be useful for pattern recognition, peak ratios the ratio of positive to negative peaks or the presence of certain peak patterns can be a feature, waveform shape metrics metrics like skewness or ketosis can describe waveform shape irrespective of the actual frequency, time domain features such as the intervals between peaks or the number of zero crossings, these features can be useful for the fuzzy logic system and keep the analysis time base agnostic, okay so how do we change to relative frequency yep, to use a relative frequency you can express the dominant frequency as a proportion of the number of data points, here's how you can adjust your code, calculate the relative frequency instead of using the raw index from the FFT divide the index by the total number of data points to get a relative frequency value between zero and zero point five since the meaningful part of the FFT output is only the first half due to symmetry, use the relative frequency for analysis you can then use this relative frequency for your fuzzy logic analysis or any other comparative analysis, since it's a ratio it is independent of the actual sampling rate and maintains the time base agnosticism of your back end, in your fuzzy logic system the membership functions would need to be adapted to work with these relative frequencies, this approach allows you to still utilize frequency information without needing a concrete time base or sampling rate, yeah we then definitely don't need to print stuff like that, only because we're already printing it elsewhere and this you have again we don't need to normalize it, okay this is a great idea do we still need to normalize the relative frequency or just use it in the fuzzy logic as is, for fuzzy logic the input variables are often normalized to fall within the universe of discourse which is typically zero one, since the relative frequency naturally falls between zero and zero point five you can use it directly if your membership functions are designed to handle this range, however if your existing membership functions expect inputs from zero to one you should scale the relative frequency to match, this would ensure compatibility with membership functions defined from zero to one, whether you normalize further or not depends on the design of your fuzzy sets and what they represent in the context of the ECG analysis, if the sets are defined in a way that accommodates the relative frequency as is no further normalization is needed, right so the relative frequency is between zero to point five and the membership functions are designed to handle zero to one so we can just multiply by two and use scale, scale relative frequency, dominant frequency should only be used once, over here we should be using scaled relative frequency, relative frequency, okay so we have dominant frequency used once, relative frequency used once and scaled relative frequency is the one we actually using, we're using scaled relative frequency and normalize, I expect them to be the same in the terminal, this really works as expected it's always zero, it's between dominant frequency separately, okay there's a problem I'm going to print length here so length of data is 800 data points it's probably too many but okay five divided by 800 we get this small number there this is not good any suggestions it looks like you 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 process to 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 zero to one range keep in mind that if the dominant underscore frequency is showing as five and the length of data is 800 a relative frequency of 0.00625 five eight hundreds 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 one because five seems to be the highest dominant frequency we get and the relative frequency is way too small think not 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 zero and one so what was the problem that if synthetic data understood if five is the highest dominant frequency you're getting anyone the scaled relative frequency to span the full range from zero to one 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 here's 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 zero and one python copy code hash update max underscore dominant underscore frequency if 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. 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. So it's essentially suggesting this code, 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 scaled. Wait a sec, getting confused. Start getting rid of stuff. Here we have a dominant frequency, then we're saving the maximum dominant frequency and the scaling. What in the name of this should be scale dominant frequency instead of scaled relative frequency? I don't think we don't need the relative frequency anymore. Quick response. Yep. 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. That's what I have. 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. Right, so we're updating max dominant frequency, so the new dominant frequency is higher in a one liner instead of two lines. We don't need the two lines. And then we have normalizing, yes, and we call it the scale dominant. What mistakes with the name should be scaled dominant frequency? Process data. It's not called process data. It's called something else. Okay, this is the script. 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? To 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. Modify 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. Ensure you test the code thoroughly to check if the modifications work as expected. Modify process data, update the process underscore data dictionary to include the scaled key instead of normalized under the frequency field. Fuzzy logic input, ensure that the fuzzy logic analysis function uses the scaled dominant frequency correctly. Fuzzy logic input, ensure that the fuzzy logic analysis function uses the scaled dominant frequency correctly. Fuzzy logic input, ensure that the fuzzy logic analysis function uses the scaled dominant frequency correctly. Fuzzy logic input, ensure that the fuzzy logic analysis function uses the scaled dominant frequency correctly. The error is because global underscore max underscore dominant underscore frequency is not being recognized. The error is because global underscore max underscore dominant underscore frequency is not being recognized as a global variable inside your function. To solve this, declare it as global inside the process underscore data function where you're trying to update it. Here's the fix. In your process underscore data function, add a global declaration for global underscore max underscore dominant underscore frequency. This line tells Python that global underscore max underscore dominant underscore frequency should be treated as the global variable you've defined outside of the function, not a local one. 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 will try 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 variables being with the global variables being defined in wrong places. Copilot is kind of hopeless at solving errors. GPT-4 is better. Can you provide a prompt for GitHub Copilot to make sure the global max dominant frequency global variable is assigned correctly? Yep. Sure. Here's a prompt you can use with GitHub Copilot to ensure the global variable global underscore max underscore dominant underscore frequency is correctly assigned and updated. This prompt specifies the context and what you want to achieve, which should help GitHub Copilot generate the appropriate code snippet. We have a global variable. Script need to update this variable with a function called processData. So it's taking... We have process... So we have processData finding a global variable before the 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, correct? So we need to normalize all the parameters between 0 and 1. That's what we are struggling with. Yeah, this is the same line, isn't it? So there's two lines of code. Yeah, should be the same as that. Clear this... Run the flask application. Still running it locally, hopefully it will be available for you to play with very soon. Yeah, this normalization is better. So the dominant frequency in this waveform is 3 or 5 or whatever and then it's taking 5 as the maximum. Because 5 is the highest number we had so far so the normalization will return 1 and if it's 3 it will return 0.6. So that makes sense. 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. To resolve this issue, check the process underscore data dictionary structure in the process underscore data function. Ensure that it contains a normalized key under the frequency key. If you've modified the dictionary structure, like replacing normalized with scaled, make sure these changes are consistently applied throughout your code, particularly in the analyze under... Yeah, the frequency of this thing should be... No, that's not the other options. Yeah, that's weird. So this is the actual fuzzy logic algorithm. This is all the rules for red. Yeah, this should be a good example. Of the difference between GitHub co-pilot and GPT4... The error normalized is likely due to the fact that the frequency data structure in the process underscore data dictionary no longer contains a key called normalized. You have replaced it with scaled. To fix this, you should update the fuzzy underscore logic underscore analysis function to use the scaled key instead of normalized for the frequency data. Here's the corrected portion of your code. Make sure to apply this change wherever normalized is referenced in relation to the frequency data. This should resolve the normalized key error. Okay, maybe I was wrong. Gopal 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. Try to access normalized. Yeah, we still want access. Yeah, so amplitude stays normalized because we are normalizing amplitude. Okay. Pop it in the old value board. Okay, replace default value with the value that makes sense in your context. Okay, this is where we close. This is where we shut down GitHub core pilot. So I tried having it in a different screen during this stream. But yes, so it says. 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. You have replaced it with scaled. To fix this, you should update the fuzzy underscore logic underscore analysis function to use the scaled key instead of normalized for the frequency data. Here's the corrected portion of your code. 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 multi analysis frequency. Get rid of what they get up while it told us to do. Gail is the same. Okay. Then we have somewhere normalized frequency membership. Yeah, this bit. Yeah, this should be scaled. It's all case sensitive, isn't it? Scaled frequency data. Yeah, that's a membership function. So essentially the same frequency membership is low. The frequency is low and we're using this scale frequency. Okay. Same. Turning frequency membership. Frequency membership. Yay. The location is not actually running. By the way, go check out bodykills.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 ACG 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 will 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 or so. 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. 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 schedule. Do you also stuff something up? Every time we use frequency, we should not have the normalized bit anymore. Yeah, like over here. Yeah, that there went away. So yes, we don't need this line. And we have... Oops. Keep pressing Ctrl C instead of Ctrl Shift C. Frequency. Do I have a scaled frequency? It's like it's being stored somewhere. Not here. Yeah, this one. Yeah, we have frequency, raw and scale. So it should be fine. Should be fine. Yeah, probably will hit the limit of GPT-4 as this. What's it? 40 messages per three hours currently. We hit this limit fairly soon. The error scaled suggests that the key scaled is not found in the frequency dictionary within processed underscore data. This can happen if there's a mismatch between the keys set in process underscore data function and the keys expected in fuzzy underscore logic underscore analysis function. To resolve this, ensure that the scaled key is correctly assigned in the process underscore data function and that the fuzzy underscore logic underscore analysis function is correctly using this key. Here's what you need to check and potentially update. In process underscore data function, ensure that scaled underscore dominant underscore frequency is correctly calculated and assigned to the processed underscore data dictionary, Python copycode return. Frequency, raw, dominant underscore frequency, scaled, scaled underscore dominant underscore frequency. How shall the keys? Closing curly bracket. In fuzzy underscore logic underscore analysis function, when accessing the frequency data, make sure to use the scaled key. And also update the membership value extraction. After making these changes, the keys should align and the error should be resolved. Make sure these changes are consistent throughout your code. Sorry, I had my microphone muted. Not sure for how long. Still getting the same error. It's something with this name. Okay, I checked the code. The script is exactly as you are suggesting. Do I also need to update the game logic JavaScript? Quick responses, please. Yep. For updating the game logic JavaScript, ensure it aligns with the changes you've made in your Python flask backend. Here's what to focus on. Data format, ensure the JavaScript sends waveform data in the format expected by the flask app. Response handling, update the JavaScript to correctly handle and display the data received from the flask backend, especially if you've made changes to the structure of the response. Synchronization, if any new features or data points are added to the flask backend, make sure they're also incorporated into the JavaScript front end as needed. Check for any specific areas in your JavaScript that interact with the updated parts of your Python code and revise them to ensure smooth data exchange and functionality. It's always tempting to go and 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? Yep. Using GitHub co-pilot can be a great idea for generating code updates, especially if you're comfortable with its style of code suggestions. It can help automate some of the more tedious aspects of coding and potentially offer creative solutions. For updating your game logic JavaScript, you can provide GitHub co-pilot with specific prompts about the changes you made in your Python flask app and what you need to reflect in your JavaScript code. Be as detailed as possible in your prompt to get the most relevant suggestions. For example, your prompt to GitHub co-pilot could be. This way, you can leverage GitHub co-pilot to quickly adapt your front end code to match your backend changes. Yay, I'll try that. I need to do it in... Let's clear the chat. We need to do it with a workspace function so it can find... Wait a second. So we need to select the Python script. Then we go up there to the Flask back end. 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. So we... This would be JavaScript, right? That's my Flask endpoint. Shouldn't you know what the endpoint is? Clear. Clear. 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? To line your JavaScript with the backend changes, focus on how the response from the Flask app is handled. Since you're now using scaled instead of normalized for frequency, update the display analysis result function. Update frequency display. Since you're using scaled underscore dominant underscore frequency in your Flask app, reflect this change in your JavaScript. Replace references to normalized frequency with something like scaled frequency. Check response structure. Ensure that the structure of the response data matches what you're trying to access. If the Flask app sends data under different keys, update the JavaScript accordingly. Here's a suggested update for the display analysis result function. Ensure that the ID is used and get element by it match the elements in your HTML. This will make your JavaScript correctly display the updated frequency information from your Flask backend. You're going to check the HTML as well? Your HTML structure looks well organized for the ECG waveform game. Here are a few suggestions based on your JavaScript updates. Scaled frequency element. Add an HTML element to display the scaled frequency. You've updated your JavaScript to handle scaled frequency of your HTML still has normalized frequency. Replace it with scaled frequency. Ensure element IDs match verify that the IDs used in the JavaScript get element by it match those in your HTML. JavaScript file references confirm the paths to waveforms. JS and game underscore logic. JS are correct and the files are loaded properly. General check. Ensure all links, images, and other resources are correctly referenced and loading properly. Here's the updated HTML segment for displaying the analysis result. These changes should align your HTML with the updates you've made in your JavaScript and Python Flask backend. Now I'm still getting the error above. Can you double check the code? The error, scaled, suggests there's an issue with accessing a scaled key in your Flask application, specifically in the Analyze underscore waveform function. This could be because the scaled key is not present in the frequency dictionary within processed underscore data. Looking at the processed data output, it seems processed underscore data has raw key and a frequency, but not a scaled key. To resolve this, you need to ensure that the scaled key is correctly added to the frequency dictionary in the process underscore data function and then correctly referenced in the fuzzy underscore logic underscore analysis function. In the process underscore data function, the following line should correctly add the scaled key. In the fuzzy underscore logic underscore analysis function, ensure you're accessing the scaled key correctly. Double check these parts in your Flask application to ensure the scaled key is being correctly assigned and accessed. If the scaled key is missing, the area you're seeing will occur. Yeah, the code seem okay, but I'm still getting the same error. Here is the whole Python script. Again, shall be really hitting that limit. Based on your Python script, the issue might be in the fuzzy underscore logic underscore analysis function. There seems to be a mismatch between the data structure of processed underscore data and the way data is being accessed in the fuzzy underscore logic underscore analysis function. Specifically, the area you're encountering scaled suggests that the key scaled is being referenced but not found in the frequency dictionary. In your process underscore data function, you correctly create a scaled key under frequency. However, in the fuzzy underscore logic underscore analysis function, you're referencing data frequency scaled directly. If there's any condition where scaled key might not be set in frequency dictionary, it would result in the area you're seeing. Ensure that the scaled key is always set in the frequency dictionary before the process underscore data function returns it. Also, verify that the fuzzy underscore logic underscore analysis function is always receiving this scaled key. One way to avoid this issue is to use a more defensive programming approach, like checking if the key exists before accessing it. This change will prevent your application from crashing if the scaled key is missing for some reason, allowing you to handle the situation more gracefully. The error, all antecedents must have input values. Indicates that one or more of the input variables antecedents to your fuzzy logic control system haven't been provided with input values. This typically happens when the data passed to the control system simulation does not include values for all the variables defined in your fuzzy rules. Given the message scaled frequency not found in input data, it seems that the scaled key under frequency might not be set or passed correctly to the fuzzy logic analysis function. To resolve this issue, ensure the following. Check data passing to fuzzy logic analysis. Make sure that the process underscore data function always computes and includes the scaled frequency in its return value, and that this value is correctly passed to the fuzzy underscore logic underscore analysis function. Data validation, before you set the inputs for the fuzzy logic analysis, validate that all necessary data is present. If any required piece of data, like scaled frequency, is missing, handle this case appropriately, either by setting a default value or by skipping the analysis with a suitable error message. Fuzzy logic analysis function, update the fuzzy underscore logic underscore analysis function to check whether the scaled frequency exists in the input data. If it does not, either set a default value or handle the absence appropriately. Here's a revised approach for setting the inputs in your fuzzy logic analysis function. This modification includes a check for the presence of scaled frequency and handles the situation if it's missing, which should resolve the error you're encountering. Okay, we're going in circles here. 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? Let's tackle this from a different angle. The error, scaled frequency not found in input data, suggests that the scaled key within the frequency dictionary is either not being created or not being passed properly to the fuzzy underscore logic underscore analysis function. Given your updated code, let's walk through the steps where this issue might arise and ensure that the scaled frequency is correctly handled. Creation of scaled frequency in process underscore data function. Ensure that scaled underscore dominant underscore frequency is always computed and added to the frequency dictionary python copy code dev process underscore data data. Global global underscore max underscore dominant underscore frequency. hash rest of your code. Scaled underscore dominant underscore frequency equals dominant underscore frequency global underscore max underscore dominant underscore frequency if global underscore max underscore dominant underscore frequency equals zero else zero. return. Frequency, raw, dominant underscore frequency, scaled, scaled underscore dominant underscore frequency. Hash, other keys. Closing curly bracket. Passing data to fuzzy underscore logic underscore analysis function. In the analyze underscore waveform function, where fuzzy, okay, wait a second, let's, okay, that's fine. Now in return, that's the important bit. In the analyze underscore waveform function, where fuzzy underscore logic underscore analysis is called, ensure the processed underscore data contains the scaled key on the frequency. So we're printing it process data. No, it does not have it a process that seem to only have the raw frequency. The output you've provided indicates that the processed underscore data dictionary only contains the raw key under the frequency field, but not the scaled key, which is expected based on your previous code snippets. This is likely where you're encountering the scaled frequency not found in input data error. To resolve this issue, you need to ensure that scaled underscore dominant underscore frequency is correctly calculated and included in the process underscore data dictionary within the process underscore data function. Here's how you can modify your process underscore data function to include the scaled frequency. Ensure scaled frequency calculation in the process underscore data function, confirm that scaled underscore dominant underscore frequency is calculated correctly and added to the frequency dictionary. Check global variable usage, ensure the global underscore max underscore dominant underscore frequency is being correctly updated and used. Here's an updated snippet for the process underscore data function. This modification ensures that scaled underscore dominant underscore frequency is calculated and included in the process underscore data dictionary. Make sure that this change is reflected in your code, and it should resolve the issue of the missing scaled key in the process underscore data. It keeps changing, alternating with those two lines. Obviously the second one is better, should be faster. Yeah, return, yeah, frequency, raw and scaled. Returning JSON file. Can you regenerate the whole function? Yep, 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, scale frequency, peaks and amplitude. A check is added to ensure that the scale frequency data is present. The fuzzy underscore logic underscore analysis function is called to perform the analysis on the process data. The 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.