Flask in AWS Lambda not showing favicon

I have an AWS lambda built using Flask, served through API Gateway. This is deployed to AWS using Terraform. I am unable to get the favicon to load correctly when deployed through this method. The favicon works flawlessly on my local machine.

Following the advice discovered here, I am able to get the icon URL to no longer return a 502; it returns a 200. However, the icon is unable to be displayed. I can navigate directly to the icon in the browser, but I still have the same undisplayed image.



I have tried using a PNG instead of ICO, with the same results.

Of note, when I am able to see the icon locally, I see it loads with type image/x-icon, but remotely it loads as image/vnd.microsoft.icon.

My handler setup:

def handler(event, context):
base64contenttypes = "image/vnd.microsoft.icon", "image/x-icon"
return awsgi.response(app, event, context, base64contenttypes)


HTML link <link rel="shortcut icon" href="{{ url_for('static', filename='favicon.ico') }}">

favicon.ico is stored in the /static directory.

API specs in Terraform

resource "awsapigatewayrestapi" "api" {
body = jsonencode({
"openapi" : "3.0.1",


/r/flask
https://redd.it/1i2shcl

fruitstand: A Library for Regression Testing LLMs

I have recently been finished the first version of a library I've been working on called fruitstand



What My Project Does

fruitstand is a Python library designed to regression test large language models (LLMs). Unlike traditional deterministic functions, LLMs are inherently nondeterministic, making it challenging to verify that a model upgrade or switch maintains the desired behavior.

fruitstand addresses this by allowing developers to:

• Create a Baseline: Capture responses from a current LLM for specific test queries.

• Test New Models: Compare responses from other models or updated versions against the baseline.

• Set a Similarity Threshold: Ensure that new responses are sufficiently similar to the baseline, thereby maintaining consistent application behavior.

This is particularly useful for tasks like intent detection in chatbots or other applications where maintaining a consistent response is critical during model updates.



Target Audience

fruitstand is primarily aimed at developers and data scientists working with LLMs in production environments. It is useful for:

• Ensuring Consistency: For applications where consistent behavior across LLM versions is critical, like chatbots or automated customer support.

• Regression Testing: Those who want to automate the process of verifying that new model versions do not degrade the performance of their systems.

• LLM Comparison: Anyone looking to switch between different LLM providers

/r/Python
https://redd.it/1i2u3nh

I wrote optimizers for TensorFlow and Keras

What My Project Does:

This library implements optimizers for TensorFlow and Keras that are used in the same way as Keras optimizers. This library contains optimizers that Keras doesn't include. You can use these optimizers on models built with TensorFlow or Keras.

Target Audience:

This library is helpful for anyone using TensorFlow and Keras.

Comparison:

This library implements optimizers not included in Keras.


https://github.com/NoteDance/optimizers

/r/Python
https://redd.it/1i2r7z0

My first django project

Hi, I am a college 2nd year trying to get more experience and I learned Django for around 3ish months. With learning, I made this simple project and I wanted to get feedback on where I can improve and if this is good enough to put on my resume. The only thing I'm worried about is if these projects are overdone and putting on my resume is worth it. Thank you! This is the Github: https://github.com/Ryan11c/mathify the live version is also linked inside the repo!



/r/django
https://redd.it/1i2u65w

Building a Machine Learning Model from Scratch in Python

Model Architecture, Evaluation, Data Prep, and more covered in a tutorial: https://codedoodles.substack.com/p/build-your-own-machine-learning-model



/r/Python
https://redd.it/1i2z59j

Best practices for Flask and DynamoDB?

I've built a few side projects with Flask and DynamoDB and, while it's not a complicated feat, I feel like things are always a bit more cumbersome than they should be. Perhaps it's by Django background, but I think there has to be a better way to do what I'm doing. Does anyone have a favorite resource (tutorial, course, book) to learn best practices for Flask+DynamoDB?

/r/flask
https://redd.it/1i2x4i2

Show Django forms inside a modal using HTMX
https://joshkaramuth.com/blog/django-htmx-modal-forms/

/r/djangolearning
https://redd.it/1i18ruq

Feedback on my first Flask site

https://redd.it/1i2n7xw
@pythondaily

Friday Daily Thread: r/Python Meta and Free-Talk Fridays

# Weekly Thread: Meta Discussions and Free Talk Friday 🎙️

Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!

## How it Works:

1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.

## Guidelines:

All topics should be related to Python or the /r/python community.
Be respectful and follow Reddit's Code of Conduct.

## Example Topics:

1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.

Let's keep the conversation going. Happy discussing! 🌟

/r/Python
https://redd.it/1i338dp

Prevent accidentally running python scripts with missing or incorrect shebang

I do this too often so I realized I could nip it with a chmod wrapper:

#!/bin/bash
# Prevent accidentally running python scripts with missing or incorrect shebang
if [ "$1" == "+x" && "$2" =~ \.py$ ]; then
firstline=$(head -n 1 "$2")
if [[ "$firstline" != "#!"python ]]; then
echo "Error: Python file detected with invalid shebang"
exit 1
fi
fi
/usr/bin/chmod "$@"

Since it's always 1. write myscript.py, 2. chmod +x myscripy.py, 3. ./myscript.py, 4. oops.

Does anyone else make this mistake? Sometimes I even write !/bin/bash...
Some lines end up being valid bash, e.g import statements via /usr/bin/import from imagemagick, and have seen random files generated (hopefully nothing destructive!).

/r/Python
https://redd.it/1i328xc

How do I Make Calender

I want to make a calender app. Should I use fullcallenderio? I've tried to make it myself but I have limited knowledge on JS

/r/flask
https://redd.it/1i2qpa8

For those of you who user raspberry pi scripts and eink screen, what driver do you use?

It is for waveshare 7.3 inch color. I looked in their documentation and added this python code

from waveshare_epd import epd7in3g,
epd = epd7in3g.EPD()



This might be stretching what is allowed to post here, but I thought I would ask. Does that look correct. I previously had a python script working correctly with a black and white screen.

/r/Python
https://redd.it/1i3dnma

flask and underscores in headers

I am writing a server that handles request from a client app that I do not have any control over. The app sends a specific header "access_token" which my server needs to receive. Unfortunately, by default, Flask seems to throw these values away. I can see the header traveling over the network in my Wireshark output, but when it arrives at my server Flask is completely blind to it. Since I can't control the client app the general solution of "just don't use underscores" isn't going to work for me. Anyone have a solution that allows Flask to receive and process headers with underscores in them?

/r/flask
https://redd.it/1i2h8po

Function use counters in Visual Studio Code

Many language extensions in VSCode include features that show the number of references to a specific function, class, or entity.


We've just released an extension that brings this functionality to Python, displaying reference counters for top-level functions and classes. Clicking a counter reveals a list of references.


Try it out here Tooltitude for Python


P.S. This is a newly released extension, and we’d love your feedback!


P.P.S. It’s not our first VSCode extension. Check out Tooltitude for Go, which has been on the marketplace for ~2 years, offering similar functionality for Go and more, with plenty of positive reviews.







/r/Python
https://redd.it/1i3hbqd

Grokking at the Edge of Numerical Stability Research

>Grokking, the sudden generalization that occurs after prolonged overfitting, is a surprising phenomenon challenging our understanding of deep learning. Although significant progress has been made in understanding grokking, the reasons behind the delayed generalization and its dependence on regularization remain unclear. In this work, we argue that without regularization, grokking tasks push models to the edge of numerical stability, introducing floating point errors in the Softmax function, which we refer to as Softmax Collapse (SC). We demonstrate that SC prevents grokking and that mitigating SC enables grokking without regularization. Investigating the root cause of SC, we find that beyond the point of overfitting, the gradients strongly align with what we call the naïve loss minimization (NLM) direction. This component of the gradient does not alter the model's predictions but decreases the loss by scaling the logits, typically by scaling the weights along their current direction. We show that this scaling of the logits explains the delay in generalization characteristic of grokking and eventually leads to SC, halting further learning. To validate our hypotheses, we introduce two key contributions that address the challenges in grokking tasks: StableMax, a new activation function that prevents SC and enables grokking without regularization, and ⊥Grad,

/r/MachineLearning
https://redd.it/1i34keg

I made a simple python scripts that automate deletion of your ChatGPT chats

Hey! I was considering whether to post this or not, and I decided other people may have had this issue too, where you been using chatGPT all the time, there's like a thousand chats, I was in this predicament and made a program that I made on Linux for firefox with Selenium, that essentially automatically goes through and starts deleting your chats on chatGPT.

I made it on Linux, I have no clue the compatibility with windows, and it's for firefox, If anyone else who's in this predicament wants to use it feel free!
Github:

https://github.com/TheBlewish/Automated-ChatGPT-Chats-Deletion

/r/Python
https://redd.it/1i35k7g