Merge branch 'master' into python-lists-tuples-update

Author: brendaweles

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

web-scraping-with-scrapy-and-mongodb/README.md

@@ -0,0 +1,41 @@
+# Web Scraping With Scrapy and MongoDB
+
+[Web Scraping With Scrapy and MongoDB](https://realpython.com/web-scraping-with-scrapy-and-mongodb/) is an example project for building a robust web scraper for static sites leveraging Scrapy and MongoDB.
+
+## Installation and Setup
+
+1. Create a Python virtual environment
+
+```sh
+$ python -m venv ./venv
+$ source venv/bin/activate
+(venv) $
+```
+
+2. Install the requirements
+
+```sh
+(venv) $ pip install -r requirements.txt
+```
+
+You'll also need to [set up a MongoDB collection](https://realpython.com/web-scraping-with-scrapy-and-mongodb/#set-up-a-mongodb-collection-on-your-computer) like described in the tutorial.
+
+## Run the Scraper
+
+Navigate into the `books/` project directory.
+
+Then you can start crawling the site:
+
+```sh
+(venv) $ scrapy crawl book
+```
+
+If set up correctly, this will populate your MongoDB collection with the book information scraped from the example site.
+
+## About the Author
+
+Martin Breuss - Email: [email protected]
+
+## License
+
+Distributed under the MIT license. See ``LICENSE`` for more information.

web-scraping-with-scrapy-and-mongodb/books/books/__init__.py

@@ -0,0 +1,8 @@
+import scrapy
+
+
+class BooksItem(scrapy.Item):
+    _id = scrapy.Field()
+    url = scrapy.Field()
+    title = scrapy.Field()
+    price = scrapy.Field()