Let us assume that, I have a Machine Learning model which uses the Tensorflow library and performs a function.
And of course, the function requires input and provides some output. Which can be easily done on my terminal.
Here comes the area where I need help, IO in the terminal is not what I wanted, I want to create a web application that has multiple functionalities. Let us only consider the area where my ML model has to work, say when I click a button after entering the inputs it runs the ML model back and provides me an output which I could display it on my web application.
I can develop websites with NodeJs. Is there any possibility of integrating the ML model with my web application made of NodeJs?
I tried running on some packages like child-process,python-shell nothing works, it gives me an error when it comes to the place of importing packages.
Please give me suggestions on the Integration of the ML model with a NodeJS web application using MongoDB as a database. All I wanted is that my web application runs my ML model wherever necessary to obtain the results which I can display through my web applications.
Also, provide information about technologies that can be easily integrated with the Machine learning model.
I'm also encountering similar problems.
Further to the previous reply, I would go for the micro services architecture design.
E.g. Use a node.js app to serve the front-end request, and the Python server only to serve the machine learning tasks.
You need to build a python web application around your model to serve responses (your model’s output) to the client (the webpage your nodejs app is serving).
Flask and Django are the major players there. REST is the most popular meta-framework.
Once you’ve built a REST API around your model, you just query it like any resource via HTTP/xmlhttprequest in JavaScript from your web page.
Or you can try to pickle your model and load into into JavaScript to query from your nodejs app.
Easier than either of those would be to use IBM Cloud Functions or AWS Lambda to expose your script/model. Very easy and cost effective.
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I am planning to create a ml model the recognizes some stuff in real time video, it will be a deep learning model, What I would like to know is how could I apply that model into my web app and what should I use? I want to use the cam of the computer and the model to recognize some patterns.
I did not made anything yet but I am plannig the project. any ideas is appreciated. I know streamlit and gradio, but i would like to use node, any framework like react or django, flask etc. any idea is appreciated. thanks
The answers will vary depending on the details of the project. For example if you want build basic project, you can use basic html, css and javascript for Frontend but if you need state management between the pages, you can use React.
You can use FastAPI or Flask for accessing model prediction and send to Frontend, you can search like this "Deploy Machine Learning Model using Flask" on the Internet. If you want to access webcam you can use VideoCapture in OpenCV.
Learn A JavaScript framework!
A JavaScript framework is a pre-written code library that provides a structured solution to a common problem. It helps developers to create dynamic and interactive web applications by providing pre-built code, tools, and utilities. Frameworks often follow a specific architecture and design pattern that promotes code organization, modularity, and reusability.
Some popular JavaScript frameworks include Angular, React.js, Vue.js, and many more. These frameworks provide a range of features such as component-based architecture, declarative programming, state management, and data binding, which can help developers to build complex applications more efficiently and effectively.
Using a JavaScript framework can help developers to save time and effort by abstracting away some of the underlying complexities of building web applications. However, learning a framework requires a certain level of expertise and knowledge of JavaScript, and it's important to choose the right framework for the specific requirements of your project.
I am about to design an app that requires to do some medium level of computation (training some small machine learning model).
I am thinking about designing the app with django. Was wondering if django can allow that some of these computations can be done in the client‘s browser, i.e. using its memory and cpu. Some initial tips of where to start looking at will be much appreciated.
Thanks
That's the reason Django is mostly used for building APIs. If you want the View (Template in Django) to update, a request would be made to the server's Controller (View in Django) to render a new version of the webpage. I would highly recommend a front end framework and just use Django for your api if you want to run client side computations.
Yes, You would need to use Javascript and call views you made in Django.
You can use any javascript library, also Vue if you need a framework.
I am implementing a social networking App Backend and I am using Node.js for development but in my app one feature is feed like in Instagram following and explore feed.
I have implemented backend in node js but for a feed algorithm I use python so how to integrate python function into node and also I want to know is it a good approach to in one single application you use two different languages.I use python because in future we use some machine learning so we all know no language is better than python for machine learning.
Please explain to me what is the right way to do it
I am developing a chatbot using DialogFlow, as my natural language processing handler, and Python as my client.
My application aims to talk with a human in a python environment (I am currently using a Jupyter Notebook), send the request to DialogFlow, get the response, then calculate the data using some python libraries and show the results to the user.
All the process described above is already working.
Now I must to find a way that lets the people uses my chatbot on line.
Here is my problem, I don't know how to model this.
I think I should put my chatbot in a webpage and make it communicate with my python application stored in a server.
Did anybody make something similar?
Given your current architecture, you'll have to do the following:
Write a client for your chatbot in HTML and JavaScript
Write a server in Python that contains your application logic and makes the API calls to Dialogflow
This is a pretty normal architecture for a web application. Given that you're using Python, you might find Flask or Django helpful.
There should be plenty of samples out there that can help you figure out what to do; I just found this blog post that demonstrates how to build a simple chat client/server with Flask and websockets.
If you're willing to change your architecture so that the user interacts directly with Dialogflow, and all of your application logic lives in the Dialogflow fulfillment webhook, you can make use of Dialogflow's Web Demo integration that provides a pre-built chat widget you can embed into an HTML page.
I am trying to design a web based app at the moment, that involves requests being made by users to trigger analysis of their previously entered data. The background analysis could be done on the same machine as the web server or be run on remote machines, and should not significantly impede the performance of the website, so that other users can also make analysis requests while the background analysis is being done. The requests should go into some form of queueing system, and once an analysis is finished, the results should be returned and viewable by the user in their account.
Please could someone advise me of the most efficient framework to handle this project? I am currently working on Linux, the analysis software is written in Python, and I have previously designed dynamic sites using Django. Is there something compatible with this that could work?
Given your background and the analysys code already being written in Python, Django + Celery seems like an obvious candidate here. We're currently using this solution for a very processing-heavy app with one front-end django server, one dedicated database server, and two distinct celery servers for the background processing. Having the celery processes on distinct servers keeps the djangon front responsive whatever the load on the celery servers (and we can add new celery servers if required).
So well, I don't know if it's "the most efficient" solution but it does work.