 new application. Go check the previous ones on the side, quickly go over them. There's not a cardio quest game where you label ECG, abnormal normal ECG, now you can play it against a robot which will give you an abnormality score. I shouldn't say against the robot, you can play it with the robot so you can collaborate with AI, whatever. It's a fuzzy logic tool that looks at the number of positive peaks, negative peaks, row amplitude, the shape, the sharpness of the r-wave, r-peak, and the intervals between PR and RT. That's the wrong earpiece. Okay, just so I just so you know I don't have any CEO whispering in my ear, just me testing this. Where was I? Yeah, so it's looking at all these parameters, PR, RT intervals, and normalizing them between zero and one, fitting it all into a fuzzy logic algorithm and speeding out an abnormality score. So it's low for normal ECG waveform like this, gives you a 0.3 over there, and it's actually making a decision as well. And it's high for an abnormal ECG wave. So go check it out, you can play with it, don't forget to provide your feedback. And I'm not actually recording, there's no like leaderboard or anything, so do let me know how you went, what score you were able to achieve, and if how many mistakes the robot was making because another main feature about it is that you can change the noise level. And as you increase the noise level, the robot will start making more mistakes. But then I'm not sure how a skilled or unskilled ECG reviewer will do in the noisy condition. But you would expect the performance will also reduce. So play with it, see how you go. Have the high pupil detected, I don't know if this one will work because currently using the one camera for the stream. I do have another camera connected, but sometimes it wouldn't pick up the second camera. The cameras are the same, but then they are actually different. That's same, same, but different. Yeah, I shouldn't have done that, stopped up the camera. Anyway, it might come back in a bit. There's a command in the terminal where to reset the cameras, but yeah, I don't want to mess around with it, so I might just leave it as is. So currently looking at this new data set, we have a template for it. It's actually the patient ID, patient code, and we're meant to be displaying that over here. It's actually not more to task. It's a different task. It's data by KJ Miller. There's a paper that you need to pay for. It's actually available when you initially look at the data set. The paper is just included as a preprint. So that's the data set. It's a ECOG. So electrocographic data. So the electrodes are placed on the surface of the brain. So this meant to be high quality EEG. This was done in epilepsy patients. But while the patients had the electrodes placed implanted, they were doing 16 different tasks. So that's what we be going over. And trying to display this data in essentially slightly what we just learning, potentially, we can display it in a slightly better way than in the original papers that were made quite a while ago, 2016 or so. Just restart the camera quickly. It's back just testing the sound as well. Because when I unplug any USB from the machine might stuff up the microphone being used as well. I'm simply using the correct microphone. So that's fine. Let's jump into it as well. By the way, the sessions are meant to be interactive. So if no human is willing to interact, I might just use the bot. Talk to the bot. So for some of this code into GPT-4 was originally created by GPT-4 as well. So might continue using it. And this session is from yesterday. Right. So this is an HTML code. And this is by the way, using the OpenAI new text to speech thing, which is pretty good. I had a problem with Facebook as I'm blocking my videos because of copyright infringement for it was actually peaking on the robot talking. So it was a bit odd, but it will be interesting to see if it's actually flags this voice as well. I don't know if it's like a famous actor or something. I don't think so. Let's try this one. This is your index.html layout for the Flask application you are developing. It's set up to present information about the ECOG data library, including an introduction, details about the data set and analysis tools, and references. You also included a section where users can select a patient code to analyze data from the face's basic task. The HTML structure looks well organized for a scientific data exploration web app. You have a section that welcomes users and introduces them to the task they'll be examining. Following this, there's a drop down menu for selecting the patient code which you've dynamically populated with patient codes passed from the Flask back end. For the visualization part, you're planning to use Chart.js to plot data on a canvas element, which is a good choice for creating interactive charts. Now let's discuss some suggestions to enhance your Flask application. One, data loading and processing. The load data.py file in the utils directory should contain functions to load data from the .mat files for a selected patient. Process data.py could have functions to process this data, such as calculating the average response over time or performing frequency analysis. Two, patient code population. The populate to pepcode.py file is likely to have a function that reads the patient codes from the directory structure you've provided and returns them to the app.py to be rendered in the drop down menu. Three, user interaction. The JavaScript file script.js should manage the event listeners for the drop down menu and other user interactions. When the user selects a patient code, it could make an Ajax call to a Flask endpoint that triggers data loading and processing and then receives the processed data to update the chart on the canvas. Four, chart visualization. You will need to write JavaScript functions to handle the data return from the Ajax call and update the chart.js canvas with new plots accordingly. Five, Flask endpoints. In app.py, define endpoints for Ajax calls that return the necessary data for visualization. Six, security and privacy. Given the sensitive nature of the data, ensure that all appropriate security measures are in place to protect patient privacy. This includes secure handling of the data within the app and anonymization protocols as outlined in your ethic statement. Seven, static files. Make sure that your static directory contains all the necessary CSS and JS files. The style.css should Yeah, that finished the paragraph, but that's okay. I actually didn't finish generating as well. Anyway, then we have this Flask, a Flask app. Now we should have we're getting to element by D. So we have the chart. Yeah, my chart is a really bad name. Should call it something else. Something more useful. And we also have the patient code that we are already pre populating. So yes, those are the patients that did this specific task. And they have different electrodes arrays. So electrodes arrays placed in different places over the brain. So we'll see what the difference between them is. And in this particular task, they were sexually wrong. It's not a model task. They were looking at the difference between different images. So looking at faces images of images of faces and then images of houses. And in the EEG supposedly we meant to be able to detect the difference between brain activity, when presented with one type of image, as opposed to the other type. Currently, getting in the error, clear this for a sec, something not Jason serializable. So we having trouble to Jason, the fine, some of the data, some is a bit of a problem. Yeah, we shouldn't have any cause issues because we are on the same machine. So the server, the client in the back end, in the same place. It should be fine. Let's read this quickly. Great. You have a client side script that fetches data from the flask back end upon window load and uses it to plot a chart. Just a couple of points to consider. One, the window dot on load function is set to automatically load data for the patient code GA. If you want users to select a patient code from a dropdown menu instead, you should trigger this data fetch in response to the dropdowns change event. Two, the fetch call is using a hard coded patient code to make this dynamic based on user selection, you could modify the window dot on load function to be an event handler for the selection of a patient in the dropdown menu. Three, make sure the load patient data function on the flask side is prepared to handle file reading and conversion from dot mat files. If that's the data format you're dealing with. Four, consider error handling on the client side for cases where data might not be available or there are issues fetching the data. Five, make sure cores is correctly configured if your flask back end and front end are served from different origins. Six, the current setup assumes that stem data is an array that can be directly plotted. Ensure that your data conforms to this structure or adjust the code to handle the data appropriately. For a dynamic patient code selection, here's an example of how to modify your script to handle user selection. JavaScript, assuming you have a dropdown menu with the ID patient code dropdown document dot get element by ID patient code dropdown dot add event listener change function. Okay, okay, okay. You know why the other text to speech is not the voice is not as good, but it's not, you can have more control of it. You can pause and play and things like that. And so so not reading the code. It's still getting errors for this one and selecting a patient can adjacent serialize and and the array so that yes, we're essentially getting a GPT for to generate the prompts for GitHub co power to use. So we do like the window on load, there was much change there. The location data function on the flux side, it's prepared to handle file reading and conversion from mud file. Doesn't convert from YAMP, I'm pretty sure it does because we were able to display the data within Python. Now we should try to do it on the front end instead of back end. Yes, we have one, we have the stimulation array that shows supposedly in this case, when the images were changed, patients also had like f MRI data. But it's not available as part of this data set, I don't know if it's available elsewhere. But then, yeah, essentially, no, it's it was recorded but not available. This is by the way an open source does that anyone can access, have the links eventually at the bottom of the flask application of the web page. So the current problem of this is that the data is not being displayed. We shouldn't have made any changes unless until we popped all the code into a GPT for using this tokenizer as well. So we don't go over the from the limit. I think it's currently 20,000 tokens can correctly correctly from wrong. Yeah, you can have copilot is a bit silly. Right, so we get the data there, but that's being displayed in the back end. So this is the stimulus over time. Notice things are just the same, I assume it's actually matters when it's zero or 100. I don't know if I have to look, this is what displayed on the screen at each point in time. So I guess there's different images. There's essentially 0 to 100 images. Zero is pre post task run. This is just baseline, eG baseline recorded for that long. A one to 50 is picture of houses being presented and 51 to 100 is a picture of a face being presented. And 101. Apparently, there's 101 in there as well. It's a stimulus interval. So it will be between each image as well. And zero will be between each task. And it's pretty clear from here that the image presentation is not random. It's being pre determined because it's being presented three times in exactly the same way can see those shapes are exactly identical. So essentially the same battery of 100 images was presented to the patient three times. Then we have the data. If I close this should get the data. And the data is sample at 1000 Hertz. There's a built in band pass filter between 0.15 to 200 Hertz, a one pole band pass. So there is no sharp corner 200 Hertz. We'll see we will, we'll have an option in the application to look at the spectrogram at the power band for each frequency component. The amplitude row of function is in the another MATLAB file. And S rate holds the sampling rate, which we currently just printing out. So I want to bring all this into the front. And so it's actually accessible. Yeah, this is a bit fine. This is like a filter filtering thing. Just look like some sort of, well, there shouldn't be eye blinks in there or anything because the electrodes are on the surface of the brain. But I get some noisy, noisy electrodes, something is going on. There's no, yeah, I have to check there is impedance impedance data, I don't think so. We kind of have to assume that the electrodes are placed perfectly well and the impedance is low and everything. But then this looks quite noisy. So I don't know what's up with that. Anyway, we'll have some a better way of displaying this. So I have the populate code process that is currently empty. Don't have any styling. That's okay. Do that. Later, question came into the whole HTML as well 3000 tokens. Let's bug a role that should be manageable in the half K and we pop in script.js as well. And this one actually contains the structure of the files in this project, files and folders for this flask application. And also the folder structure. So it's the faces basic. And those are the patients, patients IDs were really populating them. And the data, what that file is, there's nothing in the figures. That is the MRI. How big are those files? both brains folder, only 16 megabytes. So it must be a static. It's not obviously not functional MRI. I don't know what this will have look, see if we can open it in Python as well. The data process is providing code in MATLAB. But we don't have MATLAB license. It's just too expensive. Let's pop this one as well into the prompt. This is what we have so far. Currently the process data Python file is empty. And we don't have anything in the style CSS as well. We do, however, have one for the whole project. So we already have about the 15 flask applications published on the server. And we would like to add this one as the additional flask application. We're using a that's probably too early for that, but we're using Ubuntu and Apache server. But currently, we just want the application to show something useful in the local environment. So currently the patient code drop down list is being populated seem to be correctly. But then the data is not being displayed. And we're getting the following error, error, error. How do you say? Yep, that's the one. I wasn't meant to send it yet. But yeah, I forgot that I say yep, it's actually sending stuff. Clear. They are not so great thing about these text to speeches that it's not reading straight away with the one I have embedded in the browser, it can read as the text comes in. For the new open AI one, the inbuilt one, I have to wait until the response is fully generated. So a lot about patient data should be correct. Because we're able to load the file in the back end and display the data. Yes, we verify that the data is loading. It seems that you are trying to set up a flask application to load and display ECOG data from dot mat files. You have prepared the scripts for the front end and back end, but you're encountering issues with the data not displaying correctly. Firstly, ensure your load patient data function is correctly reading the dot mat files using sci pi dot IO dot load mat. This function seems correct, but debugging it with print statements or logging would be helpful to ensure it's returning the expected data structure. In your flask route slash patient data, you are converting the data to a JSON response. Ensure the stem ECOG data and straight are in the correct format that your JavaScript expects. The following steps might help you to debug the problem. One, check the file path. Ensure that the file path constructed in load patient data is correct. Print out the file path variable to see if it correctly points to the dot mat file you expect. To verify data loading, confirm that the load mat function is loading data by printing the keys and values of the data dictionary before returning it in load patient data. Three, validate the JavaScript fetch call, verify that the fetch call to slash load data patient core code patient code is successful by checking the network tab in your browser's developer tools. If the request fails, it will provide you with the error status and message. For inspect the JSON response, inspect the JSON response in the browser developer tools to ensure the data structure matches what the JavaScript expects. Five, error handling. Implement proper error handling on the back end and front end. If an error occurs, log it and ensure that the front end handles it gracefully, possibly by informing the user that the data could not be loaded. Six, client side rendering. Once you have confirmed that the data is being received correctly, check your chart JS implementation. Ensure that the data format fits what chart JS expects for rendering the plot. Seven, check console for errors. Open the browser console to look for any JavaScript errors that could prevent the chart from rendering. JavaScript errors will also appear here. Eight, flask app debugging. Since you're running the flask app in debug mode, app.run debug is true, you should see error messages and stack traces in the terminal if something goes wrong server side. Nine, local development environment. To make sure the app works in your local development environment before deploying, run it and test all functionalities thoroughly. 10, front end integration. Ensure that the drop down menu is correctly calling the change event listener and that patient code is being passed properly to your fetch call. 11, styling in CSS. The lack of CSS styles will not affect the functionality, but will impact the user interfaces look and feel. Once you have the data displaying correctly, you can proceed to style your application with CSS. 12, Python script processdata.py. If you have any data processing to do, implement this in processdata.py. Since it's currently empty, ensure that any necessary data manipulation is accounted for either here or within the data loading function. As you progress, you can incrementally test each part of your application to isolate where the issue may be occurring. Once you have the local version working, you can then think about deploying it to your Ubuntu server with Apache, ensuring you have the proper server configurations, such as setting up a WSGI application if needed. Now, we have that data being plotted correctly in the back end. I'm not sure if it's correct or not, but it's being plotted. So it's the EEG to check how many for each patient, we should have the data of how many electrodes they were, and where they were positioned. I don't think comparing between patients will be a viable for this dataset, and also not between the different tasks as well. So just looking at the one task, we do have an option to load different patients, but then the display, the way the data is displayed, we'll just comparing the different images based on the nice to see what the images were. The figures for this empty data. Yeah, we have locations. So this should have the number of electrodes and where they were placed on the brain. We have another four PDF references. Dealing with large datasets in a web application can indeed be challenging due to the potential for long load times and heavy use of system resources. Here are some strategies you can implement to optimize performance. One load data asynchronously, implement lazy loading, where you only load a subset of the data initially, and then load more as needed. This can be done via Ajax calls when the user scrolls or navigates to a new section of the data. Two pagination. Instead of loading the entire dataset at once divided into pages, the user can then navigate through pages, which load individually upon request. Three data sampling. If displaying every data point is not necessary, consider loading a representative sample of the data. Techniques like down sampling can help reduce the amount of data that needs to be transferred and rendered. Four data caching. If the data doesn't change often, cache it server side after the first load so subsequent requests are faster. Five, optimize data transfer. Use binary formats like BSON, buffers, etc. to transfer data instead of JSON when appropriate, as they can be more size efficient. Implement data compression on the server side to reduce the size of the response. Six, front end optimizations. On the client side, ensure that rendering is optimized. For instance, don't re-render the entire dataset if only a small part has changed. Utilize virtual scrolling where only the items visible in the viewport plus a small buffer are rendered. Seven, load on demand. Instead of loading all patient data at startup, load it only when a user selects a patient from the drop-down. Eight, back-end improvements. In your Flask application, consider using asynchronous route handling if the data processing is IO bound. Use a more efficient method of serving data, like streaming responses for very large datasets. Nine, database optimization. If the data is stored in a database, ensure that your queries are optimized and that you are using indexes effectively. Consider using a NoSQL database like MongoDB if your data is document oriented, which can be faster for certain operations. Ten, infrastructure improvements. If you're running into hardware limitations, you might need to consider scaling your server resources. Eleven, profiling. Profile your code to identify bottlenecks. Tools like Flask Profiler can help with profiling Flask applications. Implementing these strategies can help to reduce load times and improve user experience. You might start with the optimizations that are quickest to implement and have the largest impact, then continue refining as needed. Absolutely, I can help you refine your approach to manage data more efficiently. Here's a step-by-step guide to make the changes. Load initial data segment one. Load a default patient code. You already have a default patient code defined. Make sure it is a valid one that has associated data. Two, load a specific time segment. Modify your load patient data function to take a time range parameter and only load data for that range. For the initial load, you could define a default time range. Absolutely, I can help you refine your approach to manage data more efficiently. Here's a step-by-step guide to make the changes. Load initial data segment one. Load a default patient code. You already have a default patient code defined. Make sure it is a valid one that has associated data. Two, load a specific time segment. Modify your load patient data function to take a time range parameter and only load data for that range. For the initial load, you could define a default time range. Implement a scroller on the front end. One, add a time range selector. Include a UI element that lets the user choose the time range or move the scroller to load different segments of data. Two, handle the time range selection. Modify your JavaScript fetch call to pass the selected time range to your flask backend. Separate windows for stimulus and ECOG data. One, create two chart elements. You'll need two canvas-wine elements in your HTML. One for the stimulus chart and one for the ECOG data chart. Two, modify your JavaScript. You'll need to adjust your chart initialization code to target the two different canvas elements and initialize two separate charts. Split waveforms in the ECOG data chart. One, adjust your chart dataset. Instead of plotting all the ECOG data in a single dataset, split it into multiple datasets, each representing a channel from the ECOG data. This will ensure that they are not overlaid on top of one another. Two, modify the chart options. Adjust the chart options to handle multiple datasets appropriately, ensuring each has a unique color and is distinguishable from the others. Here's a rough implementation of how these changes could be reflected in your flask backend and JavaScript code. Flask backend. Additions. Python at app.route load segment. Methods get. Def load segment. Patient code equals request.args.get patient code. Start time equals int request.args get start zero. Starting time index end time equals int request.args get end end. Ending time index default into 10 seconds. Data equals load patient data. If data is not none, slice the data arrays to only include the time range. Stim equals data stim. Start time end time. ECOG data. E equals data dent time. S-rate equals data srate. Return JSONify stim.stim.tollist ecodata.tollist.Srate. Else return JSONify error F. Data could not be Ensure that the chart update function correctly handles creating a new chart or updating an existing one. Implement the time range selector. It's funny how a github copy doesn't look at the the other relevant files. Well it's not funny I don't understand why it doesn't. So I essentially had to copy paste the the changes in the html essentially all the body of the html into the script js and it actually generated the code of it obviously we'll be able to tell in a second if it's actually working or not. It's missing the chart configurations. It's not good. Now the question we obviously need to modify the Python code as well but can you modify this based on the recent changes in html and the script js? Okay it seems to actually understand what's what's what. So I have load the data load segments so in theory the load data would not be necessary would it? The other thing it seems that the co-pilot when you have a commented out code below like this it might actually take some bits and pieces from the commented out code currently I don't see any placeholders.