Replace pygraphviz with neo4j-viz for pipeline visualization

This commit replaces pygraphviz with neo4j-viz for pipeline visualization, providing a more interactive HTML-based visualization experience. The changes include:

- Updated the Pipeline.draw() method to generate HTML output using neo4j-viz
- Added a new get_neo4j_viz_graph() method while maintaining backward compatibility
- Updated dependencies in pyproject.toml to use neo4j-viz instead of pygraphviz
- Updated documentation and examples to reflect the change from PNG to HTML output
- Updated unit tests to work with the new visualization implementation
- Added stub file for neo4j-viz to make mypy happy

Author: oskarhane

docs/source/user_guide_pipeline.rst

@@ -111,25 +111,26 @@ Pipelines can be visualized using the `draw` method:
     pipe = Pipeline()
     # ... define components and connections
 
-    pipe.draw("pipeline.png")
+    pipe.draw("pipeline.html")
 
-Here is an example pipeline rendering:
+Here is an example pipeline rendering as an interactive HTML visualization:
 
-.. image:: images/pipeline_no_unused_outputs.png
-  :alt: Pipeline visualisation with hidden outputs if unused
+.. code:: python
 
+    # To view the visualization in a browser
+    import webbrowser
+    webbrowser.open("pipeline.html")
 
 By default, output fields which are not mapped to any component are hidden. They
-can be added to the canvas by setting `hide_unused_outputs` to `False`:
+can be added to the visualization by setting `hide_unused_outputs` to `False`:
 
 .. code:: python
 
-    pipe.draw("pipeline.png", hide_unused_outputs=False)
-
-Here is an example of final result:
-
-.. image:: images/pipeline_full.png
-  :alt: Pipeline visualisation
+    pipe.draw("pipeline_full.html", hide_unused_outputs=False)
+    
+    # To view the full visualization in a browser
+    import webbrowser
+    webbrowser.open("pipeline_full.html")
 
 
 ************************

examples/customize/build_graph/pipeline/visualization.py

@@ -54,5 +54,5 @@ async def run(self, number: IntDataModel) -> IntDataModel:
     pipe.connect("times_two", "addition", {"a": "times_two.value"})
     pipe.connect("times_ten", "addition", {"b": "times_ten.value"})
     pipe.connect("addition", "save", {"number": "addition"})
-    pipe.draw("graph.png")
-    pipe.draw("graph_full.png", hide_unused_outputs=False)
+    pipe.draw("graph.html")
+    pipe.draw("graph_full.html", hide_unused_outputs=False)

neo4j-viz.README.md

@@ -0,0 +1,115 @@
+# Graph Visualization for Python by Neo4j
+
+[![Latest version](https://img.shields.io/pypi/v/neo4j-viz)](https://pypi.org/project/neo4j-viz/)
+[![PyPI downloads month](https://img.shields.io/pypi/dm/neo4j-viz)](https://pypi.org/project/neo4j-viz/)
+![Python versions](https://img.shields.io/pypi/pyversions/neo4j-viz)
+[![Documentation](https://img.shields.io/badge/Documentation-latest-blue)](https://neo4j.com/docs/nvl-python/preview/)
+[![Discord](https://img.shields.io/discord/787399249741479977?label=Chat&logo=discord)](https://discord.gg/neo4j)
+[![Community forum](https://img.shields.io/website?down_color=lightgrey&down_message=offline&label=Forums&logo=discourse&up_color=green&up_message=online&url=https%3A%2F%2Fcommunity.neo4j.com%2F)](https://community.neo4j.com)
+[![License](https://img.shields.io/pypi/l/neo4j-viz)](https://pypi.org/project/neo4j-viz/)
+
+`neo4j-viz` is a Python package for creating interactive graph visualizations based on data from Neo4j products.
+
+The output is of type `IPython.display.HTML` and can be viewed directly in a Jupyter Notebook, Streamlit.
+Alternatively, you can export the output to a file and view it in a web browser.
+
+The package wraps the [Neo4j Visualization JavaScript library (NVL)](https://neo4j.com/docs/nvl/current/).
+
+Proper documentation is forthcoming.
+
+> [!WARNING]
+> This package is still in development and the API is subject to change.
+
+![Example Graph](examples/example_cora_graph.png)
+
+## Some notable features
+
+-   Easy to import graphs represented as:
+    -   projections in the Neo4j Graph Data Science (GDS) library
+    -   pandas DataFrames
+-   Node features:
+    -   Sizing
+    -   Colors
+    -   Captions
+    -   Pinning
+-   Relationship features:
+    -   Colors
+    -   Captions
+-   Graph features:
+    -   Zooming
+    -   Panning
+    -   Moving nodes
+    -   Using different layouts
+-   Additional convenience functionality for:
+    -   Resizing nodes, optionally including scale normalization
+    -   Coloring nodes based on a property
+    -   Toggle whether nodes should be pinned or not
+
+Please note that this list is by no means exhaustive.
+
+## Getting started
+
+### Installation
+
+Simply install with pip:
+
+```sh
+pip install neo4j-viz
+```
+
+### Basic usage
+
+We will use a small toy graph representing the purchase history of a few people and products.
+
+We start by instantiating the [Nodes](https://neo4j.com/docs/nvl-python/preview/api-reference/node.html) and
+[Relationships](https://neo4j.com/docs/nvl-python/preview/api-reference/relationship.html) we want in our graph.
+The only mandatory fields for a node are the "id", and "source" and "target" for a relationship.
+But the other fields can optionally be used to customize the appearance of the nodes and relationships in the
+visualization.
+
+Lastly we create a
+[VisualizationGraph](https://neo4j.com/docs/nvl-python/preview/api-reference/visualization-graph.html) object with the
+nodes and relationships we created, and call its `render` method to display the graph.
+
+```python
+from neo4j_viz import Node, Relationship, VisualizationGraph
+
+nodes = [
+    Node(id=0, size=10, caption="Person"),
+    Node(id=1, size=10, caption="Product"),
+    Node(id=2, size=20, caption="Product"),
+    Node(id=3, size=10, caption="Person"),
+    Node(id=4, size=10, caption="Product"),
+]
+relationships = [
+    Relationship(
+        source=0,
+        target=1,
+        caption="BUYS",
+    ),
+    Relationship(
+        source=0,
+        target=2,
+        caption="BUYS",
+    ),
+    Relationship(
+        source=3,
+        target=2,
+        caption="BUYS",
+    ),
+]
+
+VG = VisualizationGraph(nodes=nodes, relationships=relationships)
+
+VG.render()
+```
+
+This will return a `IPython.display.HTML` object that can be rendered in a Jupyter Notebook or streamlit application.
+
+### Examples
+
+For some Jupyter Notebook and streamlit examples, checkout the [/examples](/examples) directory.
+
+## Contributing
+
+If you would like to contribute to this project, please follow our [Contributor Guidelines](./CONTRIBUTING.md).