Merge branch 'master' into maintenance/flask-connexion-rest-api-20260918

Author: martin-martin

celery-async-tasks/README.md

@@ -3,17 +3,28 @@
 Example project for integrating Celery and Redis into a Django application.
 This repository holds the code for the Real Python [Asynchronous Tasks With Django and Celery](https://realpython.com/asynchronous-tasks-with-django-and-celery/) tutorial.
 
-## Setup (macOS)
+## Setup
 
-To try the project, set up a virtual environment and install the listed dependencies:
+The project targets Linux and macOS. Celery doesn't support Windows.
+
+To try the project, set up a virtual environment with Python 3.12 or newer and install the listed dependencies:
 
 ```sh
 $ python -m venv venv
 $ source venv/bin/activate
 (venv) $ python -m pip install -r requirements.txt
 ```
 
-You'll also need to install Redis on your system:
+You'll also need to install Redis on your system.
+
+On Linux (Debian or Ubuntu):
+
+```sh
+$ sudo apt update
+$ sudo apt install redis
+```
+
+On macOS with [Homebrew](https://brew.sh/):
 
 ```sh
 $ brew install redis
@@ -46,4 +57,4 @@ $ redis-server
 (venv) $ python -m celery -A django_celery worker -l info
 ```
 
-When all three processes are running, you can go to `localhost:8000/` and submit a feedback response. Celery will simulate a work-intensive process and send an email at the end of it. You'll see the email message show up in the log stream on the terminal window where the Celery worker is running.
+When all three processes are running, you can go to `http://localhost:8000/` and submit a feedback response. Celery will simulate a work-intensive process and send an email at the end of it. You'll see the email message show up in the log stream on the terminal window where the Celery worker is running.

celery-async-tasks/source_code_final/requirements.txt

@@ -1,18 +1,21 @@
-amqp==5.3.1
-asgiref==3.8.1
-billiard==4.2.1
-celery==5.4.0
-click==8.1.7
+amqp==5.4.0
+asgiref==3.12.1
+billiard==4.3.0
+celery==5.6.3
+click==8.5.0
 click-didyoumean==0.3.1
-click-plugins==1.1.1
-click-repl==0.3.0
-Django==5.1.3
-kombu==5.4.2
-prompt_toolkit==3.0.48
+click-plugins==1.1.1.2
+click-repl==0.4.0
+Django==6.1.1
+kombu==5.6.2
+packaging==26.3
+prompt_toolkit==3.0.53
 python-dateutil==2.9.0.post0
-redis==5.2.0
-six==1.16.0
-sqlparse==0.5.2
-tzdata==2024.2
+redis==8.1.0
+six==1.17.0
+sqlparse==0.6.0
+typing_extensions==4.16.0
+tzdata==2026.4
+tzlocal==5.4.4
 vine==5.1.0
-wcwidth==0.2.13
+wcwidth==0.9.1

celery-async-tasks/source_code_initial/requirements.txt

@@ -1,18 +1,21 @@
-amqp==5.3.1
-asgiref==3.8.1
-billiard==4.2.1
-celery==5.4.0
-click==8.1.7
+amqp==5.4.0
+asgiref==3.12.1
+billiard==4.3.0
+celery==5.6.3
+click==8.5.0
 click-didyoumean==0.3.1
-click-plugins==1.1.1
-click-repl==0.3.0
-Django==5.1.3
-kombu==5.4.2
-prompt_toolkit==3.0.48
+click-plugins==1.1.1.2
+click-repl==0.4.0
+Django==6.1.1
+kombu==5.6.2
+packaging==26.3
+prompt_toolkit==3.0.53
 python-dateutil==2.9.0.post0
-redis==5.2.0
-six==1.16.0
-sqlparse==0.5.2
-tzdata==2024.2
+redis==8.1.0
+six==1.17.0
+sqlparse==0.6.0
+typing_extensions==4.16.0
+tzdata==2026.4
+tzlocal==5.4.4
 vine==5.1.0
-wcwidth==0.2.13
+wcwidth==0.9.1

generate-images-openai/README.md

@@ -0,0 +1,44 @@
+# Generate Images in Python With OpenAI's GPT Image API
+
+Learn to use the OpenAI Python library to create images with the GPT Image models. In the associated tutorial on [generating images with the OpenAI API](https://realpython.com/generate-images-openai/), you'll create images from text prompts, tune the size and quality of your results, and edit an image with a follow-up prompt. You'll learn how to interact with the Images API and incorporate this functionality into your Python scripts.
+
+## Setup
+
+Create and activate a virtual environment, then install the `openai` package:
+
+```console
+$ python --version
+Python 3.14.6
+$ python -m venv venv
+$ source venv/bin/activate
+(venv) $ python -m pip install openai==3.13.0
+```
+
+You need to be on Python 3.10 or higher.
+
+The scripts read your API key from the `OPENAI_API_KEY` environment variable:
+
+```console
+(venv) $ export OPENAI_API_KEY="<your-key-value-here>"
+```
+
+## Create and Edit Images
+
+Follow the instructions in [the tutorial](https://realpython.com/generate-images-openai/) to create images from text prompts, edit an image with a follow-up prompt, and convert a Base64 JSON response to a PNG image file.
+
+You can find the code for each of these steps in dedicated scripts:
+
+- `create_png.py`: Create an image from a text prompt and write it straight to a PNG file.
+- `create.py`: Create an image from a text prompt and save the whole JSON response to a file.
+- `convert.py`: Convert a Base64-encoded PNG image delivered in a JSON response to a PNG image file.
+- `edit.py`: Read Base64-encoded image data and make an API request to edit that image with a prompt.
+
+In the tutorial, you'll walk through each of these scripts and their functionality and output in more detail.
+
+Note that `convert.py` and `edit.py` read a saved response from the `responses/` directory. Run `create.py` first, then update the `JSON_FILE` and `SOURCE_FILE` constants to match the filename that it wrote.
+
+## A Note on Models
+
+The GPT Image models replaced DALL·E, which OpenAI [retired on May 12, 2026](https://developers.openai.com/api/docs/deprecations) along with the image variations endpoint. These scripts call `gpt-image-2.5-flare`.
+
+OpenAI retires image models on a regular schedule, so if a script starts failing with a message like `The model 'gpt-image-2.5-flare' does not exist`, then check the deprecations page and swap the model name.

generate-images-openai/convert.py

@@ -0,0 +1,18 @@
+import json
+from base64 import b64decode
+from pathlib import Path
+
+DATA_DIR = Path.cwd() / "responses"
+JSON_FILE = DATA_DIR / "An ec-1789379975.json"
+IMAGE_DIR = Path.cwd() / "images" / JSON_FILE.stem
+
+IMAGE_DIR.mkdir(parents=True, exist_ok=True)
+
+with open(JSON_FILE, mode="r", encoding="utf-8") as file:
+    response = json.load(file)
+
+for index, image_dict in enumerate(response["data"]):
+    image_data = b64decode(image_dict["b64_json"])
+    image_file = IMAGE_DIR / f"{JSON_FILE.stem}-{index}.png"
+    with open(image_file, mode="wb") as png:
+        png.write(image_data)

generate-images-openai/create.py

@@ -0,0 +1,24 @@
+import json
+from pathlib import Path
+
+from openai import OpenAI
+
+client = OpenAI()
+
+PROMPT = "An eco-friendly computer from the 90s in the style of vaporwave"
+DATA_DIR = Path.cwd() / "responses"
+
+DATA_DIR.mkdir(exist_ok=True)
+
+response = client.images.generate(
+    model="gpt-image-2.5-flare",
+    prompt=PROMPT,
+    n=1,
+    size="1024x1024",
+    quality="low",
+)
+
+file_name = DATA_DIR / f"{PROMPT[:5]}-{response.created}.json"
+
+with open(file_name, mode="w", encoding="utf-8") as file:
+    json.dump(response.to_dict(), file)

generate-images-openai/create_png.py

@@ -0,0 +1,18 @@
+from base64 import b64decode
+
+from openai import OpenAI
+
+client = OpenAI()
+
+PROMPT = "A vaporwave computer"
+
+response = client.images.generate(
+    model="gpt-image-2.5-flare",
+    prompt=PROMPT,
+    n=1,
+    size="1024x1024",
+    quality="low",
+)
+
+with open("vaporwave.png", mode="wb") as png:
+    png.write(b64decode(response.data[0].b64_json))

generate-images-openai/edit.py

@@ -0,0 +1,29 @@
+import json
+from base64 import b64decode
+from pathlib import Path
+
+from openai import OpenAI
+
+client = OpenAI()
+
+DATA_DIR = Path.cwd() / "responses"
+SOURCE_FILE = DATA_DIR / "An ec-1789379975.json"
+EDIT_PROMPT = "Add a large potted plant growing out of the computer"
+
+with open(SOURCE_FILE, mode="r", encoding="utf-8") as json_file:
+    saved_response = json.load(json_file)
+    image_data = b64decode(saved_response["data"][0]["b64_json"])
+
+response = client.images.edit(
+    model="gpt-image-2.5-flare",
+    image=("image.png", image_data),
+    prompt=EDIT_PROMPT,
+    n=3,
+    size="1024x1024",
+    quality="low",
+)
+
+new_file_name = f"edit-{SOURCE_FILE.stem[:5]}-{response.created}.json"
+
+with open(DATA_DIR / new_file_name, mode="w", encoding="utf-8") as file:
+    json.dump(response.to_dict(), file)

generate-images-openai/requirements.txt

@@ -0,0 +1,14 @@
+annotated-types==0.8.0
+anyio==4.15.1
+h11==0.16.0
+httpcore2==2.12.0
+httpx2==2.12.0
+idna==3.19
+jiter==0.17.0
+openai==3.13.0
+pydantic-core==2.46.5
+pydantic==2.13.5
+sniffio==1.3.1
+truststore==0.10.4
+typing-extensions==4.16.0
+typing-inspection==0.4.4

hello-jev/README.md

@@ -0,0 +1,67 @@
+# Hello Jev: Getting Started With Jev in Python
+
+This folder provides the code examples for *Get Started With Jev in Python*,
+a video on the [Real Python YouTube channel](https://www.youtube.com/@realpython).
+
+Both scripts run the same train-station info desk. The desk asks a visitor
+whether they lost something and then points them at the lost-and-found counter
+or asks what else it can help with. The difference is how the answer is read:
+
+- **`plain_python.py`** handles the input by hand. It only accepts an uppercase
+  `Y` or `N`, so anything else sends the visitor back around the loop.
+- **`jev_noul.py`** replaces that branching with a single `Noul` question. Jev
+  scores how affirmative the answer is on a scale from `0` to `1`, so the script
+  compares thresholds instead of strings and understands replies like
+  `yeah, I've lost something`.
+
+## Setup
+
+Install [uv](https://docs.astral.sh/uv/), then create the virtual environment.
+Because `pyproject.toml` pins `requires-python = ">=3.14"`, uv downloads
+CPython 3.14 for you if you don't have it yet:
+
+```sh
+$ uv sync
+```
+
+`jev_noul.py` reaches Jev through [OpenRouter](https://openrouter.ai/), which
+lets you try the model without opening an account with TypeSafe AI. Grab an
+OpenRouter API key, then create a `.env` file next to the scripts and add it:
+
+```dotenv
+OPENROUTER_API_KEY=<YOUR-OPENROUTER-KEY>
+```
+
+Keep `.env` out of version control. An API key belongs in your environment, not
+in your repository.
+
+## Running the Examples
+
+Start with the plain script to see where hand-written input handling gives up:
+
+```sh
+$ uv run plain_python.py
+Did you lose something? (Y/N)
+```
+
+Only an uppercase `Y` or `N` gets you past the prompt. Try `yes` or `y` and the
+script asks again.
+
+Now run the Jev version. The `--env-file` flag tells uv to load your API key
+from `.env` before it starts the script:
+
+```sh
+$ uv run --env-file .env jev_noul.py
+Did you lose something?
+```
+
+Answer in your own words. `jev_noul.py` treats a Noul above `0.8` as a yes and
+below `0.2` as a no, and asks again for anything in between.
+
+## Where to Go Next
+
+`Noul` is one of three Jev primitives. `Score` rates something on a scale, and
+`Choice` picks from a list of options. The
+[TypeSafe SDK on PyPI](https://pypi.org/project/typesafe-sdk/) documents all
+three, and the TypeSafe AI blog explains the idea behind them in
+[Introducing System One Models and Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev).