 We'll continue from where we left off with the ECG feature extraction. Let's need to check a few things first. Yeah, if we have the mouse, we have the mouse. That's good to refresh this page for some reason, to want to refresh by itself. We'll share this quickly just in case someone wants to join in. Let's see how it does. Just quickly do it on a different screen. Pause that quickly. Let's check the sound again. Hopefully we'll be publishing this tool today. So I just need to change a few things on the Cloudflare. By the way, Cloudflare keeps telling me that I have about 30,000 requests to the website. I have about 2.6 unique visitors to Bionicaos.com. It's actually quite unusual. I think those numbers are inflated. I don't know what percentage of them are bots. GPT4 currently cap of 50 messages every three hours. Pause that and that meant to be a moving window as well. It shouldn't be a problem. In the past, I used to hit that limit quite a bit, but not recently. So I have here 30K. Yeah, so I can't see the IP addresses anymore. Because it's Proxit in Cloudflare, so that's not helpful. Let's get back to this Python code. So we had the code already in some description. Let's take the code because it works pretty well. I don't want to start from scratch. I'll just say this is for reference only. So we have it working. Okay, enabling the moved. Pop that in. See what we get. Okay, let's increase the fonts now. Use annotations instead of legends. Can annotate each point directly on the graph. Now, legend placement. Avoid over-plotting. That doesn't seem to be a problem. Subplots. It would be nice to have them all overlaid on top of one another. So the question is like, how will it chop them up? And I think it's potentially suggesting taking the app. Because that's what we're finding first. That's actually a feature of New York it too. Something inbuilt. The rest is done by an app. And you can see it's not great. So we also need to see how to improve it. We'll probably add some controls for the timing there. To say try and improve the T-Wave placement. And that green label meant to be here at the bottom. So let's go ahead and do that. Let's say try and improve the T-Wave placement and that green label meant to be here at the peak. So I don't know what's happening there. But this could help as well if we just overlay, chop them up and overlay them. Let's see what it says. Certainly. You can overlay all hardbeats on a single plot by extracting segments of the ECG around each RPK and plotting them together. Here is an improved version of your code. Hardbeat overlay. Extract segments of the ECG signal around each RPK and plot them on the same axes to overlay all hardbeats. Average hardbeats. Calculate the mean of all extracted hardbeats segments and plot it in red to show the average hardbeats clearly. Visualization enhancements. Adds alpha transparency to individual hardbeats plots for better visualization and adds a vertical line at the RPK for reference. Each hardbeats is extracted from the ECG signal from 150 samples before to 150 samples after each RPK. These values can be adjusted according to the specific needs or characteristics of your data. All extracted hardbeats are then overlaid on the same plot and their average is also calculated and plotted. The average hardbeat can give an overview of the typical shape of the hardbeats in the ECG signal. You can further enhance this plot by adding labels or annotations for adjusting the colors and styles of the lines to make specific features more visible. The pre-peak and post-RPK are peak. The pre-peak, the pre-peak and post-peak would ideally be detected automatically. Certainly, we can adapt the code to automatically detect the start and end of each hardbeat segment based on the detected peaks. One approach could be to identify the nearest points where the ECG amplitude crosses the threshold or changes direction. Below is an updated version of your code with automated detection of the pre-peak and post-peak segments. Automated segmentation. It automatically detects the start and end of each hardbeat segment by finding the minimum points before and after each RPK. It's a basic method and might need refinement based on actual ECG data characteristics. Interpolated average hardbeat. Since hardbeats might have different lengths, we interpolate each hardbeat to have the same length before averaging. The code finds the start and end points of each hardbeat by looking for the minimum value before and after each RPK. This is a simplistic approach and might need to be adapted based on the actual characteristics of your ECG data or by implementing a more robust method to identify the start and end points of each hardbeat. The individual hardbeats are then plotted on the same graph and their average is calculated after interpolating each hardbeat to have the same number of points. The average hardbeat is plotted in red. You can adjust... Yeah, so... So we're still... ...detecting a change... ...a bit processing or a change in direction. Let's read this again. Automated segmentation. It automatically detects the start and end of each hardbeat segment by finding the minimum points before and after each RPK. It's a basic method and might need refinement based on actual ECG data characteristics. We also don't like any magic numbers. So if there are any values in the code, can we have input on the chart screen to be able to adjust the input parameters, all timings, etc. Amplitude, thresholds, etc. Note that this approach is more suited for a Jupyter notebook environment. Here's an enhanced interactive example. Why is it more suitable for a Jupyter notebook environment? We would like to turn it eventually into a web application. So it was called the Data Panel. What's the other name? One popular alternative to create interactive web applications from Python is Streamlit. It's intuitive and requires less boilerplate code compared to traditional web development frameworks. Another option is Dash by Polly, which is also a powerful tool for building interactive web applications using only Python. Here's a similar example of the one above but adapted for Streamlit. First, install Streamlit if you haven't already. We want a dashboard, web application designed using Flask. Certainly. You can use Flask along with Polly or Bokeh to create interactive plots in a web application. Below is a basic example using Flask and Polly. In this example, the ECG signal's amplitude threshold for detecting heartbeat segments is adjustable via an input field on the web page. Install required packages. You need to have Flask and Polly installed. You can install these packages via PIP if you haven't already. Create a new Python file for your Flask app. Here's a basic example. Let's name the file app.py. Create a new HTML file for rendering the plot. Create a new folder named Templates and inside this folder, create a new file named index, HTML. Render Flask app. You can render Flask app with the following command. This code snippet provides a basic example. You might want to include additional functionalities such as error handling, user inputs validation or other interactive features to make your web application more robust and user-friendly. Remember, Flask is just a web server gateway interface, WSGI application, for deploying in a production environment. Consider using a production-ready server like Goonicorn and put it behind a reverse proxy server like Nginx or Apache to handle client requests efficiently. We are using Apache. Using Apache is a great choice for deploying Flask applications. You can use the mod underscore proxy or mod underscore proxy underscore USGI modules to create a reverse proxy or mod underscore USGI to deploy the application directly. Below is a step-by-step process to deploy your Flask application on an Apache server. If not already installed, you can typically install it with. Create a WSGI file for your application, for example, MyApp, WSGI, and add the following. Replace MyApp with the actual name of your Python script containing your Flask app. Edit your Apache configuration file or create a new one. Add a new less than virtual host greater than entry or update the existing one with the WSGI daemon process, WSGI script alias, and other directives as needed. Here's a basic example. Adjust paths, domain names, and user group settings according to your server setup. After saving your configuration, restart the Apache server to apply the changes. Make sure that the Apache server has the necessary permissions to read and execute your Flask. We already have a few Flask applications running on an Apache server. Could you generate all the files needed to add this as an additional application? Absolutely. I'll provide a detailed step-by-step guide, including all files needed to add this new Flask application alongside your existing applications on an Apache server. We will structure the application to detect hard beats from ECG data and visualize them interactively, allowing users to adjust the amplitude threshold. Your new application directory may look something like this. Here's a Python code for the Flask app, app.py. It's a simplified example that you should adapt according to your needs. Add a new configuration or update the existing one. Adjust paths and domain names according to your setup. Right, so it's just a simple Python code. Replace the path to ECG underscore app, your user, your group, and ECG.yourdomain.com with your actual path, user, group, and domain. Ensure that the Apache server has the necessary permissions to read and execute your Flask app in WSGI file. Now, this new application should be accessible and running alongside your existing Flask applications on the Apache server. Make sure to test thoroughly and adapt the configurations and code as per your specific needs and environment. You can stop that one. Start this one. This is what it should look like. We made some more modifications, so there should be inputs for different magic numbers within the code. There's also some time intervals and amplitude thresholds. So we can play around with it and see if we can get better labeling results. Well, we obviously should be able to. Is this the full Python code? Apologies for the confusion. Here's a more comprehensive version of the Python code for the Flask app, including all the necessary imports and the complete implementation for generating the interactive ECG plot. Now, why is it... Right, so doing the simulation to begin with, I just changed the order of a few things, but this app. This app.py file needs a complete Flask application. It simulates an ECG signal, processes it to detect our peaks, and allows users to adjust the amplitude threshold interactively to visualize individual hardbeats. The indexed function handles both get and post requests. On a get request, it displays the initial plot with a default threshold. On a post request, when the user updates the threshold, it replots the ECG signal with the new threshold. The poly figure is created and updated within this function, and then it's converted to HTML to be embedded in the indexed HTML template. Make sure to replace underscore underscore main underscore underscore, with the actual application's entry point or adapt as necessary based on your application structure and requirements when deploying. The debug equals true, argument is for development and testing and should be set to false in a production environment to avoid exposing sensitive information. Note, you need to have all the necessary libraries installed, you can install them via pip. Make sure to adapt paths and configurations as per your specific server setup and application requirements. Also, ensure to secure the application appropriately for production, including using HTTPS, validating user inputs, and handling errors gracefully. Certainly. Below is the indexed HTML template file which you should place inside a folder named templates in your application directory. This HTML template includes a form to adjust the threshold value and displays the plotly graph embedded within the web page. Form for threshold adjustment, the template includes a form with a number input field allowing users to adjust the threshold value for visualizing hard beats. When the user changes this value and submits the form, a post request is sent to the server, and the plot is updated accordingly. Displaying the plot, the plotly graph generated in the FlaskApp app.py is embedded in the HTML using plot safe. The safe filter is used to render the HTML code safely. Plotly JavaScript library, the plotly.js library is included to render the plotly graph on the web page. You can remove this if you're not using any client-side plotly functionalities. Ensure to adapt this template according to your design and functional requirements, adding any additional features, styles, or elements needed. Yeah, that's okay. That's pretty much it. That was from another Flask application, the synthetic signal generator. So the title is obviously different. Otherwise, we want to keep the plotly JSON script there. I have a bunch of other stuff that's just the template for our website. That's the header. Let me start from here at this point. Interactive dashboard. I want to use also the word feature extraction, but we'll add it later. Inputs do we have. We also set the pan minimal max and the default value for each input. Let's start with that. That's where the plot is. So essentially just need these bits. Surely we need to call a JavaScript. Do we need a JavaScript file? Okay, we do need that. Can we copy the index HTML? I'm not sure about this max. I think it misunderstood. It's not the whole thing. It's just the script. Script.js here probably needed. You can get rid of that. That's a different Flask application. Just for my document, yes. Close. We have the chart open arena. And then the index error was with do. Essentially sending requests to the server HTTP. It's this error. I don't know if I was seeing anything like this before. 304 this is the HTTP Stas code returned by the server. A 304 Stas code means not modified. In this context, it tells the client, usually a web browser, to send a request. In this case, console.png has not been modified since the last request, so the cache version can be used instead of downloading the file again. Okay, but it's also not displaying anything. So we have static script. I will sort that one last. Calling script.js in the HTML. Should be over here. That's a problem. Are there any issues with this file? Especially is script.js being called correctly? It's being called twice. This is actually a different script.js. You should use only one of them. The second method is the recommended way to include static files and Flask applications, as it ensures the correct path is generated regardless of where the application is running. For the images and other static files, consider using Flask2URL underscore for function to generate the URLs, like you did for the JavaScript file in CSS. This ensures that the URLs will always be correct, even if the application's URL structure changes. And be reached. It's not running, is it? Okay, we'll do it like we did before, will we? Just generate all the content. Template HTML, better template. I'll be based on threshold. Yeah, those thresholds meant to take in variables. Okay, generate the whole HTML code. There's something working first. Using URL underscore for static files, update of the paths to images and other static files to use Flask2URL underscore for function, ensuring that the URLs are always correct regardless of the deployment environment or URL structure. It's calling the CSS file. Yes, and again, we have two of them. Yeah, I shouldn't have given it. Okay, let's start again. Sorry. I'm using the fault. I would like to turn this code into a Flask application, eventually deployed on Apache server. You can turn the provided code into a Flask application by wrapping it inside a Flask app and generating the plot as a response to a certain endpoint. Below is a step-by-step modification of your code to turn it into a Flask app. We will create an endpoint that returns a PNG image of the plotted ECG data. If you haven't already, you need to install Flask. You can install it via PIP. Create a new Python file, for example, app.py, and open it in a text editor. Run the app.py script. It will start a local development server. You can view the ECG plot by visiting http://127.0.0.1.500. To deploy the Flask app on an Apache server, you can use a production WSGI server like mod__proxy__usgi, mod__proxy, or mod__proxy__fcgi, and you may want to use virtual NV to create an isolated Python environment. Here are general steps to deploy the Flask app to an Apache server. Install mod__proxy__usgi or other proxy modules in an ap... ...engineerate the whole Python code script. Make sure all the required libraries, Flask, Neurokit2, NumPy, and MathLolib are installed in your Python environment. You can run the app using the command pythonapp.py, and it will start a local development server. You can view the ECG plot by visiting http://url in your web browser. Do I need a HTML and JavaScript file? You don't necessarily need an HTML or JavaScript file if you simply want to display the plot image. The Flask app code I provided above will directly return an image when you access the ECG endpoint, which can be viewed in a web browser without any additional HTML or JavaScript. However, if you want to create a more complete web application with a user interface that includes other elements besides the plot, you might want to use HTML to structure your web page and potentially JavaScript to add interactivity. Here's an example of how you might incorporate an HTML template to display the ECG plot along with some additional content. In this example, an HTML file is created in a templates folder. Flask is configured to look for templates in a folder named templates by default. We added a new root at ap...root that renders the HTML file when the home URL is accessed. The index, HTML file contains and less than I am. You don't necessarily need an HTML or JavaScript file if you simply want to display the plot image. The Flask app code I provided above will directly return an image when you access the ECG endpoint, which can be viewed in a web browser without any additional HTML or JavaScript. Where is it sending the file? Where is it sending the file? And how do I run the app? In the script provided, the ECG plot is not saved to a file on disk, but rather held in memory using a byte's IO object. When the ECG endpoint is accessed, the plot is sent directly to the browser as a PNG image using the send underscore file function. The byte's IO object is used to create a byte stream of the image data, which is then sent as a response to the client's request. This way, you don't need to worry about file handling or cleanup. You can run app.py directly from the terminal or command prompt. Here's how you do it step by step. Ensure that you have Python installed. You should have Python installed on your computer. You can check if it's installed and which version is installed by running Python version or Python 3 version. Install necessary packages. If you haven't already, you need to install the required packages. Open your terminal or command prompt and use pip or pip3 to install them. For example, run the Flask app. Navigate to the directory containing app.py and execute the script using Python. In the terminal or command prompt, you can use the cd command to change directories. It looks like you're trying to access the root URL of your Flask app, but no root has been defined for that URL, so you're seeing a 404 error, meaning not found. Since we only defined a root for ECG, you need to access http colon slash slash 127 dot zero dot zero dot one colon five zero zero zero slash act to see the ECG plot. If you want to add a home page to your Flask app, you can define an additional root. Here's an example of how you might modify your app.py script to include a simple home page. In this modification, I've added a new root at app.root that maps to a home function. This function uses Flask render underscore template underscore string method to return a simple HTML string as the response. When you access http colon slash slash 127 dot zero dot zero dot one colon five zero zero zero slash, you should now see a welcome message and a link to the ECG root to view the ECG plot. You can replace the HTML string with more complex HTML or a template file to customize your home page further. This is not exactly what we want. Eventually, we'll have to have a full-fledged HTML and JavaScript and everything, but this should be a quick fix if we want to display anything. The page can be reached this because we are not actually running, so we see the plot. So we didn't actually needed that. I'm going to go by the AVA path. The good thing is, can we add any necessary input parameters, essentially anything that is a fixed constant number in the code would like to turn into a variable so we can control it from the application front panel. Certainly. You can use HTML forms in Flask to gather user input and then use those inputs in your Python code. Here's an updated version of your application where the duration, sampling underscore rate, hard underscore rate, and the segment underscore range around our peaks are input parameters provided by the user. In this updated version, the home root handles both get and post requests. When a get request is received, it simply returns the HTML form. When a post request is received, after the user submits the form, it gathers the form data and passes it to the ECG underscore plot function to generate the ECG plot with the user specified parameters. The ECG underscore plot function is now parameterized to accept the user's input for the ECG simulation and plotting. Ensure that your entire plot generation code inside the ECG underscore plot function uses these parameter values instead of hard coded numbers. Note, always validate and sanitize user inputs to ensure they are within expected ranges and types to prevent potential errors or security issues. Okay, a couple of things. First, do we need a JavaScript file? Second, can we make sure that default values and range for each input variable are being predefined? Could we also generate the entire Python code for up, file, index, HTML, and script.js? Okay, this might take some time, but... You don't necessarily need a JavaScript file for this simple form-based interaction. HTML and Flask can handle form submission and response rendering. However, if you want to enhance user interactivity, provide real-time feedback, or create a dynamic user interface without requiring a page reload, JavaScript can be very helpful. You can define default values and acceptable ranges for the input variables directly in the HTML file using the value, min, and max attributes of the less than input greater than element. If later you decide you want to add more interactivity to your web page, you might create a static slash script.js file and link it in your HTML, but for the current functionality you requested, it's not needed. Note, always ensure that the input from users is validated and handled safely to avoid potential security risks or errors in your application. In a production environment, consider adding error handling, input validation, and security measures such as escaping user input and setting up course policies if applicable. If later you decide you want to add more interactivity to your web page, you might create a static slash script.js file and link it in your HTML, but for the current functionality you requested, it's not needed. Bit odd. Can it get derailed? Generate the whole Python script? Certainly. Here's a complete updated app.py script with all the parameterized inputs, taking user input from the form submission. This is not the input variables that I meant, but okay, we can have those as well. This is what we have in the previous application. I was mainly talking about the different time intervals and thresholds. This script is a complete flask application that takes user input from a form to generate an ECG plot based on the specified parameters. Ensure to replace the other hard-coded numbers with variables or inputs as necessary to fully parameterize your application. Make sure that flask and other necessary libraries are installed and always handle user inputs safely to avoid potential errors or security issues. Or the ECG signal feature extraction. This is actually better. This is better. It's because we don't need that. Generate ECG plot. Sampling rate can definitely be 250. For flask 4, the whole HTML script. Certainly. Here's a complete HTML template that includes input fields for duration, sampling rate, forward rate, and segment range. The users can fill in these fields and submit the form to generate an ECG plot based on their input. Create a new folder named templates, and inside that folder, create a new file named index, HTML. Insert the following code into index, HTML. In this HTML code, four input fields are created to accept user inputs for duration, sampling rate, forward rate, and segment range. Each input field has specified default values, minimum, and maximum values to ensure that users provide inputs with unacceptable ranges. The required attribute is added to each input field to ensure that all fields must be filled before the form can be submitted. The step attribute in the duration input field allows users to enter decimal values. A little bit of CSS is added for better visual spacing between the labels. Make sure to adjust the min, max, step, and default values according to your specific requirements and constraints. You can also enhance the CSS styles to make the form visually more appealing. Can you generate a separate CSS file? Certainly. You can create a separate CSS file to style your HTML page. Place the CSS file in a folder named static, as Flask serves the static files, like CSS, JS, images, etc., from this folder by default. Here's an example of a CSS file named styles.css placed inside the static folder. This CSS file contains styles that enhance the look of the form and the page overall. Add the following line inside the less than head greater than tag of your index, HTML file to link the CSS file. Now your application will have a separate CSS file to style the HTML page, making the web page more maintainable and visually appealing. You can add or update the CSS styles and the styles that CSS file is needed. HTML. Add the following line inside the less than head greater than tag of your index, HTML file to link the CSS file. Now your application will have a separate CSS file to style the HTML page, making the web page more maintainable and visually appealing. You can add or update the CSS styles and the styles that CSS file is needed. Do I have a chase on script? No. First I'm getting that error. This log message indicates that a post request was made to the ECG root and received a 404 not found response. This is happening because there's no explicit root handler defined for ECG in your Flask application. In the example I provided earlier, the form action is said to submit the post request to ECG, but there is no corresponding root in the Flask app to handle this endpoint. The ECG underscore plot function should be explicitly associated with a root to fix this issue. Here's how to correct it in the app.py file. In the index function, after processing the post request, I used redirect and url underscore for to redirect to the ECG underscore plot root, passing the form values as query parameters. Added a new root handler for ECG that maps to the ECG underscore plot function. The ECG underscore plot function now retrieves its parameters from the query string using request, arts.get. These changes should fix the 404 error by providing a root handler for the ECG endpoint and ensuring the post request's parameters are properly passed to the ECG underscore plot function. Make sure to update the form's action attribute in the HTML file if needed. So why on earth wasn't doing it to begin with? How many lines is it? 75. Can you regenerate a whole Python script with necessary updates and all the required parts? Absolutely, I understand your request. Here's the updated code for each part of your application.