 entertain you with some music before we begin. Well, maybe someone will be able to make music out of this. By the way, it's available on the side. It's an early release, so if you want to look like me, go to, there's obviously no ads here yet, because it's gonna, it doesn't work really well. I'm still trying to improve it. Yeah, that's still running in the background. Because if I minimize the window, the camera stops working. So let me know if you tried it, have any questions, and I'll jump straight into the waveform detection using positive logic. Trying to detect these different waveforms. So we have those membership functions that as you change the input waveform, you get a different activation. So we're looking at symmetry of the signal and sharpness. For square, you have high activation, triangle. Yeah, so that doesn't make sense. Left trapezoid. Right, left trapezoid is getting something. Yeah, we need a bit of more noise and simple in the sharpness. It's low. So highly symmetrical, so it makes sense. And sharpness is low, the toes make sense. Let's say for trapezoid, compare the two. It's only also highly symmetrical. It's pretty much, yeah, the same. So based on also have sharpness is has low activation is high. So meaning it's not very sharp. It's same as for square. And that's why we're having trouble distinguishing between the two. Expect the sharpness or square to be slightly higher. As you know, we have GPT helping us out, suggesting looking at printing the activation level sounds good. However, is there any option to plot the actual numbers on top of one of the charts? For example, for example, can we overlay these values on top of the symmetry and sharpness membership functions as dots of different colors? Let's turn the volume. Yes, you can definitely overlay the activation levels on your symmetry and sharpness membership function plots. This visualization will provide a clear, intuitive representation of how the inputs are being interpreted by the fuzzy logic system. Here's a general approach to achieve this using plotly in JavaScript. Modify the Python back end. Update your flask back end to send the activation levels to the front end. For instance, include them in the fuzzy underscore plot underscore data dictionary that gets returned to the front end. Python copycode fuzzy underscore plot underscore data symmetry activation underscore levels equals low symmetry underscore low mid symmetry underscore mid high symmetry underscore high closing curly bracket fuzzy underscore plot underscore data sharpness activation underscore levels equals low sharpness underscore low sharpness underscore mid high sharpness underscore high closing curly bracket update the JavaScript front end in your JavaScript code modify the function that plots the membership functions to also plot the activation levels. You will use the data sent from the back end. For example, style the dots. Adjust the size, color, and any other styling attributes of the dots to make them clearly visible and distinguishable on the chart. Refresh and test. After implementing these changes, refresh your front end and test with different inputs to see how the activation levels are represented on the plots. Remember, this approach requires that the front end and back end are correctly synchronized in terms of data format and structure. Ensure the data sent from the flask application matches what the JavaScript expects to receive. Okay, can you check the Python code? We are already sending those. We are already doing it. Can you find the relevant code? Yep. To check if your Python back end is already sending the necessary activation level data to the front end. And to find the relevant code, I would typically need to see the specific sections of your Python flask application code where the fuzzy logic processing and data preparation for the front end are handled. In your flask route or function that handles the fuzzy logic processing, look for the part of the code that prepares the data to be sent to the front end. It might look something like this. But you should have the clear, clear. Please check above. You should have the Python code. Let me know if you have trouble finding it. Yep. Yep. It's generating very slowly again. To confirm if your Python code is sending the necessary activation level data to the front end, I'll review the relevant portions of the Python code you've provided earlier in our conversation. Let's check for the section where the fuzzy logic data, including the activation levels, is prepared for front end. Yeah. From the earlier shared Python code, I see the fuzzy underscore logic function, which calculates and returns the fuzzy logic systems output, fuzzy plot data, and centroid. However, based on the provided snippet, the activation levels for the membership functions, symmetry and sharpness, are not explicitly included in the fuzzy underscore plot underscore data that's sent to the front end. Wait a second. Here is a relevant part of your code with comments on where to include the activation levels. It doesn't make sense. Do we send this already? Yes, we did. I suspect we need to start a new conversation. Close it for God about the code. Should we just update it here? Just to plot the fuzzy. Yeah, let's just plot this one. Clear? Clear? Clear? Oh, we already have that code here is the current code that we have. Thank you for providing the updated fuzzy underscore plot underscore data structure. It's clear now that your Python code is indeed sending the necessary activation level data for both the symmetry and sharpness membership functions to the front end. This data includes the low, mid, and high activation levels reach. Given this setup, you can proceed to plot these activation levels on your membership function charts using plotly in the front end. Here's an outline of how you can modify your JavaScript to include this data in the plots. Yes, those should be these two plots. We want to overlay the data on top of them. It's a plot container. Okay, right. It seemed to have access to the JavaScript. Clear? So we have the scatter plot. Now we're also adding these markers, symmetry activation trace, the centroid symmetry membership function, entry activation trace. We still have the one trace to this one by one. Get rid of that. Entry activation trace. And we're getting some sort of error. And when we get error, we'll play some music to relax. Hopefully you find it relaxing. By the way, you can try it in the link in the chat. There's a link to the gesture group. That's the one I'm using. And so I'm running in JavaScript. So I'm not sending any of your video hand cautions to my server or anything. So stay local on your machine. And we're having an error. Nothing indicating an error. Must be the names. I'll see that symmetry activation law. Instead of sending a division law, it seems like shit is the wrong window. Yeah, this one. Let's check the terminal first. The terminal same. Okay, right, because the problem with JavaScript, because it is JavaScript, I don't understand is it line 101 or 94? Okay, I know. No, wait, I don't know. You don't have the fuzzy data. Do we have the fuzzy data? It's actually 124, isn't it? 24. Symmetry function. Symmetry activation trace. Must be an array. Why? It is an array. Why values are an array? Yeah, I don't want to ask you to keep up with it, because it won't have all the context. Yeah, this error around this code. Let's see what it says. The area you're encountering, error, grand data must be an array, suggests that there's an issue with the data format being passed to plotly's add traces function. It's likely that the data structure is not as plotly expects. In the plot fuzzy logic parameters function, particularly this line, the issue could be with the structure of symmetry activation trace. Let's inspect and correct it. Let you correct it. I'll just use the code. Check the data structure for symmetry activation trace. The Y property in symmetry activation trace is set to an array containing three arrays, activation underscore low, activation underscore mid, activation underscore high. However, plotly expects a single array of values for Y. You need to ensure that each activation level is a single number, not an array, and that they correspond correctly to the X values. Okay, I particularly care how it looks like. Yes, this code is much longer, but we can deal with it. It's just the symmetry, the sharpness, interaction trace, and same plotly add traces. Ensure that the element with the ID symmetry membership function already has a plotly plot rendered in it before you try to add traces to it. If not, you'll need to create the initial plot first. Ensure that the element with the ID symmetry membership function already has a plotly plot rendered in it before you try to add traces to it. If not, you'll need to create the initial plot first. What so did I do? I need to add this tool, keep one as well. Yes, looks like, okay, just bombarded my graph if, okay, wait a second. Maybe the original thing gave was correct, seems like it was correct. The activation, symmetry activation level, right, actually moving around. This is good. What's the, that's just the red. Symmetry activation now, what's the blue and the green? Symmetry activation, okay, coffee. Okay, might have to finish. I started too late today, and yes, we'll have to do a more of this monitoring. Yeah, we actually want to get rid of that, say, working well. Right, not the questions. There's red, green, blue. We need to sort it out later. Might do it next time. So I'll leave you some amazing music. Yeah, if you can sing maybe as well, you can do a lot of TikTok videos or something. I'll add the instructions to it and wrap it up in a proper page. I'll see you later. This is what bye-bye sounds to it.