#python-flask

[vexacarnivorous]

day 25 | 12/6/23 | monday

i watched the new spiderverse movie with a friend today (IT WAS REALLY GOOD), then i checked out this video by youtuber NetworkChuck, where he shows you how to make a website that collects & displays memes with flask. he has this very enthusiastic presentation style that makes it fun to watch him teach.

sadly, i wasn't able to get mine to work, but i'll try to see what's going wrong tomorrow and hopefully i'll be able to fix it. from what i can tell, i've gotten some kind of JSONDecodeError (whatever that means lmao, but that's something to look up for future me).

[antoniosos]

Right now I'm not sure if I need

A big hug

A cigarette

Or to punch some annoying motherfucker in the face

All of these would uplift my current mood.

Yes I was learning Flask, why?

[aprilmayblossoms]

Exciting (for me at least lol) News!

Hey everyone! It's been a while since I last logged in to Tumblr, which seems weird because it was such a huge part of my teenage life lol. I've been super busy focusing on my coding projects and, well, life in general 😅. But I have some awesome updates to share!

🎉 My New Website & YouTube Channel! 🎉

I've launched a brand new website where I'll be sharing blog posts about my current and upcoming projects, behind-the-scenes insights, and more! Plus, my YouTube channel is up and running, featuring code-along videos of my latest projects, including a Tarot reading app, a (as of yet unfinished) Stardew Valley clone, and more!

🔗 Check Them Out Here:

Website: aprilmaycodes.com

YouTube Channel: April May Codes

On my website, you'll find detailed blog posts about the projects I'm working on, tutorials, my #100DaysofCode progress, and even some personal reflections on my coding journey. And on my YouTube channel, you can follow along as I build apps and games from scratch!

I'm so excited to connect with all of you and share what I've been working on. Your support means the world to me, and I can't wait to hear your thoughts on my new content!

Thanks for all your support, and happy coding! 💻✨

[newcodesociety]

URL Shortner

Image Caption Generator

Weather Forecast App

Music Player

Sudoku Solver

Web Scraping with BeautifulSoup

Chatbot

Password Manager

Stock Price Analyzer

Automated Email Sender

[pythondjangoflask]

remove specific element in python list - remove index based element

[tenshokustories]

初めまして。現役エンジニアとして大阪のシステム会社に3年ほど勤めているニシ_CWと申します。プログラマー歴は4年目となります。

今回の記事は初心者向けにPythonのWebフレームワークであるFlaskについて解説する記事です。初学者にもわかるようにできるだけ簡単に記載していくので是非参考にしていただければ幸いです。

本記事でわかること

Flaskでできること

FlaskとDjangoとの違い

Flaskの将来性

Flaskの使い方

Flaskを学ぶ

Flaskの学習法

[thenetoar]

Exciting news. I’ve delivered the second project successfully 🙌🏻.

I’m very happy. It has been hard. At first, I thought I would be easier than the previous project as I had learnt a lot in the first project about routes, decorators, paths, etc. Oh my God, I was wrong.

This time the project was split in backend and frontend, what makes sense, for sure. But I’m not an expert in frontend so to start I was struggling to send the data properly.

Unfortunately they didn’t explain well the project and I hadn’t full information of how the webpage I had to built worked. Often, when you have to do this kind of projects where you have to complete some functionalities they do a brief demo of the final solution so you can make up your mind and see how it should work in the end. That wasn’t the case this time.

It just made it more difficult to follow. They recommend start doing the backend at the beginning and test it in the terminal using CURL.

Well, following that instruction I did it has I was said. But when I tried to join it with the frontend, for nobody surprise, it didn’t work.

There were bugs to fix in the frontend before it started to work. That was annoying because that wasn’t part of the course and required an experienced person to help.

In the end, I could implement everything, do the needed fixes in the original code and push it to GitHub successfully.

But there was another part to do as part of the course. It was very interesting for me as it was something I heard a lot and I’ve never could apply it in my job. This is nothing but test your code by coding.

I always test my code, of course. But I do it by testing the final result and document it in somewhere: jira, excel, any specific file… but never using a code to test. I usually write the septs I follow and also the result wether it is the expected or not.

That blew my mind. It is very useful! I really liked it even though I got some unexpected errors in the meantime that I needed to solve before doing the proper test and check the result.

To sum up my experience in this second project:

Positive things:

- I tested my code by coding. I liked this way of working a lot.

- I customised, with some mentor help, the VSCode properly to run the tests and debug my code.

- I installed a theme for the terminal that helps me a lot to read the errors and everything inside.

- I applied my previous learning with databases, Flask and Python.

- I learnt how to identify when an error is in the frontend or in the backend.

- I used a little bit of JavaScript to fix the frontend bugs.

Not that positive things:

- I felt stressed and powerless a lot during the project. The course content wasn’t aligned with what they requested in the final checklist. This bad feeling was bigger as I really enjoyed the videos and explanations. Before starting I felt well prepared to start coding and in the end that wasn’t true.

- The effort was bigger than expected. It took a lot of hours to finish and deliver the project.

- I faced issues when uploading the repo to GitHub. In this case, it as totally my fault because I did it wrong from the beginning when I clone the repo.

Well, three more projects left!

Actually, I have to start the third project this week. I already finished watching the videos and I guess I’m ready to start coding. Let’s see how it goes this time. I’ll back here to share my conclusions.

[ketul99]

Python Web Frameworks in 2025: Which One Will Dominate?

The future of Python web development is here! Check out the top frameworks of 2025 and choose the right one to supercharge your web app projects.

[shineinfosoft-xamarin]

The Ultimate Guide to Python AI Backend Website Performance Improvement with Shine Infosoft

"Performance is not just about speed; it's about efficiency, scalability, and reliability." – This statement holds especially true in the world of AI-powered applications. In today’s fast-paced digital landscape, slow response times, inefficient processing, and performance bottlenecks can significantly hinder user experience and business growth.

Many businesses leverage Python-based AI backends to power intelligent applications, but performance issues often arise due to slow API response times, unoptimized machine learning model inference, excessive memory consumption, and inefficient database queries. These challenges not only affect website speed but also impact user engagement, operational costs, and scalability.

What if your Python AI backend could deliver lightning-fast responses, seamless AI-powered interactions, and optimized resource utilization? By implementing cutting-edge performance optimization strategies, businesses can significantly enhance the efficiency of their AI-driven applications.

At Shine Infosoft, we specialize in Python AI backend optimization, helping businesses improve website speed, scalability, and overall performance. Whether it’s optimizing AI model inference, streamlining database queries, implementing caching strategies, or leveraging cloud-based scalability solutions, our expert team ensures that your AI-driven web applications run at peak efficiency.

In this blog, we’ll explore why performance optimization is crucial for AI-powered websites, common bottlenecks in Python backends, and advanced strategies to enhance AI processing speed and efficiency. Let’s dive in! 🚀

Why Website Performance Matters for AI-Driven Applications

A high-performing Python AI backend ensures faster processing, improved user engagement, and cost-effective operations. Here’s why it’s essential:

Better User Experience & Engagement

AI-powered applications rely on real-time processing; delays frustrate users.

Faster responses lead to improved customer retention and satisfaction.

Quick AI model inference enhances interactive user experiences in chatbots, recommendation systems, and predictive analytics.

Higher SEO & Search Engine Rankings

Search engines prioritize faster websites in their rankings.

Optimized backend APIs ensure quicker content delivery and better indexing.

Speed improvements lead to higher page dwell time and lower bounce rates.

Reduced Operational Costs & Resource Utilization

Efficient backend architecture reduces server resource consumption.

Optimized AI processing minimizes unnecessary computing expenses.

Reduced cloud service costs due to better memory and CPU management.

Scalability & Business Growth

A well-optimized AI backend supports increased user traffic.

Enhanced efficiency allows seamless scaling without performance degradation.

Supports business expansion by maintaining fast and reliable AI-driven experiences.

Common Performance Bottlenecks in Python AI Backends

Despite Python’s flexibility and extensive AI libraries, certain issues can degrade performance:

Slow API Response Times

Inefficient API endpoints lead to sluggish user interactions.

Blocking I/O operations cause delays in request processing.

Poorly optimized REST and GraphQL APIs add latency to data retrieval.

Unoptimized Database Queries

Poor indexing and redundant queries slow down database retrieval.

Large datasets can overwhelm servers, affecting AI model efficiency.

Improper joins and missing indexes in relational databases create bottlenecks.

High Memory & CPU Utilization

Poorly optimized machine learning models consume excessive resources.

Inefficient looping and recursive functions increase processing time.

Large neural networks without optimization slow down inference speeds.

Lack of Caching Mechanisms

Repeated AI model inference for similar requests slows performance.

No caching leads to unnecessary database queries and computations.

Absence of distributed caching solutions causes redundant API calls.

Inefficient AI Model Inference

Large deep learning models result in slower prediction times.

Lack of hardware acceleration (e.g., GPUs, TPUs) impacts efficiency.

Over-reliance on CPU-based inference leads to suboptimal performance.

Advanced Strategies for Python AI Backend Performance Optimization

To address these challenges, Shine Infosoft employs cutting-edge performance enhancement techniques:

Code Optimization & Efficient Processing

Implement asynchronous programming using Asyncio and FastAPI.

Use optimized data structures and algorithms to reduce computational overhead.

Profile code using cProfile and line_profiler to identify bottlenecks and optimize execution paths.

Database Optimization

Use indexing, partitioning, and query optimization to speed up database access.

Implement caching mechanisms such as Redis or Memcached to reduce repeated queries.

Optimize Object-Relational Mappers (ORMs) like SQLAlchemy and Django ORM for better performance.

AI Model Optimization

Apply model quantization and pruning to reduce model size and improve inference speed.

Use TensorRT or ONNX to optimize deep learning models for faster execution.

Implement batch processing and model parallelization for handling multiple requests efficiently.

Load Balancing & Scalability

Utilize microservices architecture to distribute processing workloads.

Deploy applications using Docker and Kubernetes for scalable deployment.

Implement message queues like RabbitMQ or Kafka to manage background tasks efficiently.

Hardware Acceleration & Parallel Computing

Utilize GPUs and TPUs to accelerate AI model inference.

Implement parallel processing frameworks like Dask or Ray for large-scale computations.

Offload non-essential computations to background workers.

Conclusion & Call to Action

🚀 At Shine Infosoft, we specialize in optimizing Python AI backends to ensure exceptional speed, scalability, and efficiency. Our expertise helps businesses enhance website performance, AI processing speed, and user engagement.

🔹 Ready to supercharge your Python AI backend? Contact Shine Infosoft today for a free performance audit and consultation! 🚀

Source

[techtutorialsx]

Flask and Pydantic

Introduction In this tutorial we are going to learn how to use Pydantic together with Flask to perform validation of query parameters and request bodies. Continue reading Flask and Pydantic

[antoniosos]

Web apps are hard

I've got a website on 000webhost.com and I need it to send a http request to my python flask script. (here it is in all it's glory -> http://pivonkachat.000webhostapp.com/ )

And that's where I got stuck.

I don't know how to send it to the script.

Should the python script run on something like Apache?

idk

@xiabablog If I remember correctly you develop web applications?

[hotelasian]

Flask Development Company in Australia

LDS Engineers is a leading Flask development company in Australia offering top-notch web development services across Australia, the UK, the USA, and India. With years of experience and a team of skilled professionals, we specialize in creating reliable, scalable, and cost-effective Flask applications tailored to meet your unique business needs. Whether it’s a small web-based solution or a complex…

[karandeeparora]

As a freelance Python developer, I create powerful web applications, APIs, and automation scripts. With expertise in frameworks like Django and Flask, I deliver secure, scalable, and efficient solutions tailored to your business needs. Let's collaborate and turn your ideas into reality with clean, reliable code.