Merge branch 'master' into structural-pattern-matching

Author: bzaczynski

openai-dalle/README.md

@@ -1,14 +1,14 @@
-# Generate Images With DALL·E 2 and the OpenAI API
+# Generate Images With DALL·E and the OpenAI API
 
-Learn to use the OpenAI Python library to create images with DALL·E, a state-of-the-art latent diffusion model. In the associated tutorial on [generating images with DALL·E 2 and the OpenAI API](https://realpython.com/generate-images-with-dalle-openai-api/), you explore image creation and generating image variations. You learn how to interact with DALL·E using API calls and incorporate this functionality into your Python scripts.
+Learn to use the OpenAI Python library to create images with DALL·E, a state-of-the-art latent diffusion model. In the associated tutorial on [generating images with DALL·E and the OpenAI API](https://realpython.com/generate-images-with-dalle-openai-api/), you'll explore image creation and generating image variations. You'll learn how to interact with DALL·E using API calls and incorporate this functionality into your Python scripts.
 
 ## Setup
 
 Create and activate a virtual environment, then install the `openai` package:
 
 ```console
 $ python --version
-Python 3.11.0
+Python 3.12.5
 $ python -m venv venv
 $ source venv/bin/activate
 (venv) $ python -m pip install openai
@@ -22,14 +22,15 @@ Follow the instructions in [the tutorial](https://realpython.com/generate-images
 
 You can find the code for each of these steps in dedicated scripts:
 
-- `create.py`: Create an image from a text prompt and save the image data to a file.
+- `create_dalle3.py`: Create an image from a text prompt using DALL·E 3 and display the URL to the image.
+- `create.py`: Create an image from a text prompt using DALL·E 2 and save the image data to a file.
 - `convert.py`: Convert a Base64-encoded PNG image delivered in a JSON response to a PNG image file.
 - `vary.py`: Read Base64-encoded image data and make an API request to receive variations of that image.
 
 In the tutorial, you'll walk through each of these scripts and their functionality and output in more detail.
 
 ## Edit Images (Inpainting and Outpainting)
 
-The OpenAI Image API also allows you to [edit parts of an image](https://beta.openai.com/docs/guides/images/edits) using text prompts. For this, you need to create a mask with transparent image data in the area where you want to edit the image.
+The OpenAI Image API also allows you to [edit parts of an image](https://platform.openai.com/docs/guides/images/edits-dall-e-2-only) using text prompts. For this, you need to create a mask with transparent image data in the area where you want to edit the image.
 
 You can run `edit.py` to give this functionality a try.

openai-dalle/create.py

@@ -1,24 +1,24 @@
 import json
-import os
 from pathlib import Path
 
-import openai
+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)
 
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-response = openai.Image.create(
+response = client.images.generate(
+    model="dall-e-2",
     prompt=PROMPT,
     n=1,
     size="256x256",
     response_format="b64_json",
 )
 
-file_name = DATA_DIR / f"{PROMPT[:5]}-{response['created']}.json"
+file_name = DATA_DIR / f"{PROMPT[:5]}-{response.created}.json"
 
 with open(file_name, mode="w", encoding="utf-8") as file:
-    json.dump(response, file)
+    json.dump(response.to_dict(), file)

openai-dalle/create_dalle3.py

@@ -0,0 +1,14 @@
+from openai import OpenAI
+
+client = OpenAI()
+
+PROMPT = "A vaporwave computer"
+
+
+response = client.images.generate(
+    model="dall-e-3",
+    prompt=PROMPT,
+)
+
+print(response.data[0].url)
+print(response.data[0].revised_prompt)

openai-dalle/edit.py

@@ -1,8 +1,9 @@
 import json
-import os
 from pathlib import Path
 
-import openai
+from openai import OpenAI
+
+client = OpenAI()
 
 SOURCE_PATH = Path.cwd() / "images" / "An ec-1667994848"
 DESTINATION_PATH = Path.cwd() / "responses"
@@ -11,9 +12,7 @@
 SOURCE_PATH.mkdir(parents=True, exist_ok=True)
 DESTINATION_PATH.mkdir(parents=True, exist_ok=True)
 
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-response = openai.Image.create_edit(
+response = client.images.edit(
     image=open(SOURCE_PATH / "computer.png", mode="rb"),
     mask=open(SOURCE_PATH / "mask.png", mode="rb"),
     prompt=PROMPT,
@@ -23,8 +22,8 @@
 )
 
 with open(
-    DESTINATION_PATH / f"edit-{PROMPT[:5]}-{response['created']}.json",
+    DESTINATION_PATH / f"edit-{PROMPT[:5]}-{response.created}.json",
     mode="w",
     encoding="utf-8",
 ) as file:
-    json.dump(response, file)
+    json.dump(response.to_dict(), file)

openai-dalle/requirements.txt

@@ -1,16 +1,15 @@
-certifi==2022.9.24
-charset-normalizer==2.1.1
-et-xmlfile==1.1.0
-idna==3.4
-numpy==1.23.4
-openai==0.25.0
-openpyxl==3.0.10
-pandas==1.5.1
-pandas-stubs==1.2.0.62
-python-dateutil==2.8.2
-pytz==2022.6
-requests==2.28.1
-six==1.16.0
-tqdm==4.64.1
-typing_extensions==4.4.0
-urllib3==1.26.12
+annotated-types==0.7.0
+anyio==4.4.0
+certifi==2024.7.4
+distro==1.9.0
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.0
+idna==3.7
+jiter==0.5.0
+openai==1.40.6
+pydantic==2.8.2
+pydantic_core==2.20.1
+sniffio==1.3.1
+tqdm==4.66.5
+typing_extensions==4.12.2

openai-dalle/vary.py

@@ -1,27 +1,26 @@
 import json
-import os
 from base64 import b64decode
 from pathlib import Path
 
-import openai
+from openai import OpenAI
+
+client = OpenAI()
 
 DATA_DIR = Path.cwd() / "responses"
 SOURCE_FILE = DATA_DIR / "An ec-1667994848.json"
 
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
 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 = openai.Image.create_variation(
+response = client.images.create_variation(
     image=image_data,
     n=3,
     size="256x256",
     response_format="b64_json",
 )
 
-new_file_name = f"vary-{SOURCE_FILE.stem[:5]}-{response['created']}.json"
+new_file_name = f"vary-{SOURCE_FILE.stem[:5]}-{response.created}.json"
 
 with open(DATA_DIR / new_file_name, mode="w", encoding="utf-8") as file:
-    json.dump(response, file)
+    json.dump(response.to_dict(), file)

primer-on-jinja-templating/requirements.txt

@@ -1,6 +1,7 @@
-click==8.1.3
-Flask==2.1.2
-itsdangerous==2.1.2
-Jinja2==3.1.2
-MarkupSafe==2.1.1
-Werkzeug==2.1.2
+blinker==1.8.2
+click==8.1.7
+Flask==3.0.3
+itsdangerous==2.2.0
+Jinja2==3.1.4
+MarkupSafe==2.1.5
+Werkzeug==3.0.3

python-async-iterators/async_csv.py

@@ -7,17 +7,16 @@
 class AsyncCSVIterator:
     def __init__(self, path):
         self.path = path
-        self.file_was_read = False
+        self.reader = None
 
     def __aiter__(self):
         return self
 
     async def __anext__(self):
-        if not self.file_was_read:
+        if self.reader is None:
             async with aiofiles.open(self.path, mode="r") as file:
                 lines = await file.readlines()
                 self.reader = csv.reader(lines)
-                self.file_was_read = True
         try:
             return next(self.reader)
         except StopIteration:

python-async-iterators/compress.py

@@ -18,5 +18,5 @@ async def main(directory, zip_name="output.zip"):
             await archive.write(chunk)
 
 
-directory = Path()
+directory = Path.cwd()
 asyncio.run(main(directory))

python-class/car.py

@@ -34,7 +34,7 @@ def brake(self, value):
         print(f"Braking to {self.speed} km/h...")
 
     def __str__(self):
-        return f"{self.make}, {self.model}, {self.color}: ({self.year})"
+        return f"{self.make} {self.model}, {self.color} ({self.year})"
 
     def __repr__(self):
         return (