[pull] master from vinta:master

website/build.py

@@ -755,8 +755,9 @@ def render_category(
         if parent_category:
             breadcrumbs.append((parent_category["name"], category_public_url(parent_category)))
         breadcrumbs.append((category["name"], category_url))
+        page_entries = [entry for group in entry_groups for entry in group["entries"]] if entry_groups else entries
         category_json_ld = json.dumps(
-            build_category_json_ld(category_title.removesuffix(" - Awesome Python"), category_url, category_description, entries, breadcrumbs),
+            build_category_json_ld(category_title.removesuffix(" - Awesome Python"), category_url, category_description, page_entries, breadcrumbs),
             ensure_ascii=False,
         ).replace("</", "<\\/")
         (page_dir / "index.html").write_text(

website/data/category_intros/computer-vision.md

@@ -11,7 +11,7 @@ How to choose:
 
 OpenCV comes as [four pip packages that share the `cv2` namespace](https://github.com/opencv/opencv-python), so install only one: opencv-python for the main modules, or opencv-contrib-python to add the extra modules. If you never call `cv2.imshow` or you build your GUI with another toolkit, install the headless variant of either one, which also makes Docker images smaller.
 
-Ultralytics YOLO covers the [whole life of a model](https://docs.ultralytics.com/modes/): train, validate, predict, export, and track, from Python or the `yolo` command. Its docs recommend [starting training from a pretrained model](https://docs.ultralytics.com/modes/train/). To deploy, [export it](https://docs.ultralytics.com/modes/export/) to ONNX, TensorRT, CoreML, or another format. The code and the models you train with it are [AGPL-3.0](https://www.ultralytics.com/license), so unless you open-source your whole project, you need an Enterprise License.
+Ultralytics YOLO covers the [whole life of a model](https://docs.ultralytics.com/modes/): train, validate, predict, export, and track, from Python or the `yolo` command. Its docs recommend [starting training from a pretrained model](https://docs.ultralytics.com/modes/train/). To deploy, [export it](https://docs.ultralytics.com/modes/export/) to ONNX, TensorRT, CoreML, or another format. The code and the models you train with it are [AGPL](https://www.ultralytics.com/license), so unless you open-source your whole project, you need an Enterprise License.
 
 Kornia is a [differentiable computer vision library like OpenCV, with strong GPU support](https://kornia.readthedocs.io/en/latest/get-started/introduction.html). Every operator works on PyTorch tensors and supports autograd, so vision ops can run on the GPU and sit inside your training loop.
 

website/data/category_intros/data-validation.md

@@ -1,4 +1,4 @@
-Validate API input and config with Pydantic, the Python data validation library built on type hints. Use Pandera for dataframes, jsonschema for JSON Schema.
+Declare API input and config as type hints, and Pydantic validates them. Pandera brings Python data validation to dataframes, and jsonschema covers JSON Schema.
 
 How to choose:
 

website/data/category_intros/date-and-time.md

@@ -1,4 +1,4 @@
-Most code should handle time zones with zoneinfo and pick python-dateutil, the Python date library for parsing date strings and adding months.
+Handle time zones with the built-in zoneinfo. Parsing date strings and adding months is where a Python date library earns its install: python-dateutil.
 
 How to choose:
 

website/data/category_intros/file-format-processing.md

@@ -1,4 +1,4 @@
-PDF, Word, and Excel files open with pypdf, python-docx, and openpyxl, a Python file format library for each. MarkItDown turns all three into Markdown.
+pypdf reads PDFs, python-docx Word files, and openpyxl Excel sheets: one Python file format library per format. MarkItDown turns any of them into Markdown.
 
 How to choose:
 

website/data/category_intros/game-development.md

@@ -1,4 +1,4 @@
-A 2D game needs a loop: write your own with pygame-ce, the Python game development library, or let Arcade run it. Panda3D does 3D, and Ren'Py visual novels.
+pygame-ce leaves the 2D game loop to you, while Arcade runs it for you. For 3D, Panda3D covers Python game development, and Ren'Py builds visual novels.
 
 How to choose:
 

website/data/category_intros/image-processing.md

@@ -19,7 +19,7 @@ pyvips builds a pipeline of operations and runs it only when you write the resul
 
 Wand is a [ctypes-based ImageMagick binding](https://docs.wand-py.org/en/latest/), so install ImageMagick's MagickWand library first. Its objects are resources like open files: [use them in a `with` block](https://docs.wand-py.org/en/latest/guide/resource.html) so they get closed. Wand's docs say to [never use Wand directly in an HTTP service](https://docs.wand-py.org/en/latest/guide/security.html) or on any public server. Hand the images to a background worker through a queue, and limit ImageMagick's resources and formats in its `policy.xml`.
 
-rembg runs as a [CLI, a Python library, an HTTP server, or a Docker container](https://github.com/danielgatis/rembg). In code, create a session once with `new_session()` and pass it to each `remove()` call, since `remove` otherwise [starts a new session every call](https://github.com/danielgatis/rembg/blob/main/USAGE.md). The model weights [carry their own licenses](https://github.com/danielgatis/rembg), separate from rembg's MIT license, so check the one you use before you ship it in a commercial product.
+rembg runs as a [CLI, a Python library, an HTTP server, or a Docker container](https://github.com/danielgatis/rembg). In code, create a session once with `new_session()` and pass it to each `remove()` call, since `remove` otherwise [starts a new session every call](https://github.com/danielgatis/rembg/blob/main/USAGE.md). The model weights [carry their own licenses](https://github.com/danielgatis/rembg#models), separate from rembg's MIT license, so check the one you use before you ship it in a commercial product.
 
 thumbor is an HTTP server: you [set the size and crop in the image URL](https://github.com/thumbor/thumbor), and it [detects faces and important features](https://thumbor.readthedocs.io/en/latest/) to crop around them. Set a `SECURITY_KEY` so [every URL is signed](https://thumbor.readthedocs.io/en/latest/security.html) and nobody can tamper with it, and build those URLs in Python with [libthumbor](https://thumbor.readthedocs.io/en/latest/libraries.html). In production, [turn off `ALLOW_UNSAFE_URL`](https://thumbor.readthedocs.io/en/latest/configuration.html) and run [more than one instance](https://thumbor.readthedocs.io/en/latest/hosting.html) behind a load balancer.
 

website/data/category_intros/template-engines.md

@@ -1,4 +1,4 @@
-If your templates should hold no Python code, Jinja fits: a Python template engine whose sandbox also renders untrusted templates. Mako embeds plain Python.
+Whether templates may hold Python code splits the Python template engines: Jinja keeps code out and sandboxes untrusted templates, Mako embeds plain Python.
 
 How to choose:
 

website/data/category_intros/web-apis.md

@@ -1,4 +1,4 @@
-For a Django project, pick Django REST framework or Django Ninja. Outside Django, build on FastAPI, a Python API framework based on type hints.
+Outside Django, declare each FastAPI endpoint with type hints. Django projects add a Python API framework on top instead: Django REST framework or Django Ninja.
 
 How to choose:
 

website/data/category_intros/web-frameworks.md

@@ -1,4 +1,4 @@
-When you want an ORM, an admin, and auth built in, use Django. Flask, a smaller Python web framework, gives you a core you extend.
+How much should a Python web framework build in? Django comes with an ORM, an admin, and auth; Flask gives you a small core to extend.
 
 How to choose:
 

website/data/category_intros/web-servers.md

@@ -1,4 +1,4 @@
-In production, Gunicorn serves WSGI apps like Django, and Uvicorn serves ASGI apps like FastAPI. Waitress, a pure-Python web server, runs WSGI apps on Windows.
+In production, Gunicorn serves WSGI apps like Django, and Uvicorn ASGI apps like FastAPI. Need a Python web server on Windows? Waitress runs WSGI there.
 
 How to choose:
 

website/data/category_intros/websocket.md

@@ -1,4 +1,4 @@
-With Django, Channels is the pick; with Flask and Socket.IO clients, Flask-SocketIO. Standalone apps run on websockets, a Python WebSocket library.
+Your Python WebSocket library follows your framework: Channels on Django, Flask-SocketIO on Flask with Socket.IO clients. websockets needs no framework.
 
 How to choose:
 

website/fetch_pypi_downloads_via_clickpy.py

@@ -84,12 +84,7 @@ def collect_names(readme_text: str) -> list[str]:
 
 def fetch_clickpy(names: list[str]) -> dict[str, int]:
     in_list = ", ".join(f"'{name}'" for name in names)
-    query = (
-        "SELECT project, sum(count) AS downloads "
-        "FROM pypi.pypi_downloads_per_day "
-        f"WHERE project IN ({in_list}) AND date >= today() - 30 "
-        "GROUP BY project FORMAT JSON"
-    )
+    query = f"SELECT project, sum(count) AS downloads FROM pypi.pypi_downloads_per_day WHERE project IN ({in_list}) AND date >= today() - 30 GROUP BY project FORMAT JSON"
     resp = httpx.post(CLICKPY_URL, content=query, timeout=60)
     resp.raise_for_status()
     return {row["project"]: int(row["downloads"]) for row in resp.json()["data"]}

website/static/main.js

@@ -145,7 +145,9 @@ function applyFilters() {
 
   collapseAll();
 
-  rows.forEach(function (row) {
+  // Number rows in their sorted DOM order, not the load order `rows` keeps
+  const orderedRows = tbody ? tbody.querySelectorAll("tr.row") : rows;
+  orderedRows.forEach(function (row) {
     let show = true;
 
     if (query) {

website/static/style.css

@@ -535,7 +535,12 @@ kbd {
 
 .category-intro code,
 .guide-body code {
+  font-family: ui-monospace, "SFMono-Regular", "Menlo", monospace;
   font-size: 0.9em;
+  padding: 0.08rem 0.4rem;
+  border-radius: 0.4rem;
+  background: var(--hero-line);
+  color: var(--hero-text);
 }
 
 .jump-links {

website/templates/index.html

@@ -241,7 +241,7 @@ <h2 class="sr-only">Results</h2>
           <td class="col-arrow"><span class="arrow">&rarr;</span></td>
         </tr>
         {% if entry.description %}
-        <tr class="desc-row" aria-hidden="true" hidden>
+        <tr class="desc-row" hidden>
           <td class="col-num"></td>
           <td colspan="6">
             <div class="desc-text">{{ entry.description | safe }}</div>

website/tests/test_build.py

@@ -679,6 +679,39 @@ def test_category_page_contains_json_ld(self, tmp_path):
             {"@type": "ListItem", "position": 2, "name": "Widgets", "item": "https://awesome-python.com/categories/widgets/"},
         ]
 
+    def test_category_json_ld_follows_page_order(self, tmp_path):
+        readme = textwrap.dedent("""\
+            # Awesome Python
+
+            Intro.
+
+            ## Projects
+
+            **Tools**
+
+            ## Widgets
+
+            _Widget libraries._
+
+            - [zeta](https://example.com/zeta) - Listed first.
+            - [alpha](https://example.com/alpha) - Listed second.
+
+            # Contributing
+
+            Help!
+        """)
+        (tmp_path / "README.md").write_text(readme, encoding="utf-8")
+        self._copy_real_templates(tmp_path)
+        build(tmp_path)
+
+        category_html = (tmp_path / "website" / "output" / "categories" / "widgets" / "index.html").read_text(encoding="utf-8")
+        marker = '<script type="application/ld+json">'
+        start = category_html.index(marker) + len(marker)
+        data = json.loads(category_html[start : category_html.index("</script>", start)])
+        collection = next(node for node in data["@graph"] if node["@type"] == "CollectionPage")
+        items = collection["mainEntity"]["itemListElement"]
+        assert [(item["position"], item["name"]) for item in items] == [(1, "zeta"), (2, "alpha")]
+
     def test_group_page_falls_back_to_default_description_in_json_ld(self, tmp_path):
         readme = textwrap.dedent("""\
             # T