Use Hatchling build-backend and fix README for PyPI (#1051)

Author: maresb

README.md

@@ -1,6 +1,6 @@
 <div align="center">
 
-![PyMC-Marketing Logo](https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/marketing-logo-light.jpg)
+![PyMC-Marketing Logo](docs/source/_static/marketing-logo-light.jpg)
 
 </div>
 
@@ -27,10 +27,10 @@ Unlock the power of **Marketing Mix Modeling (MMM)** and **Customer Lifetime Val
 This repository is supported by [PyMC Labs](https://www.pymc-labs.com).
 
 <center>
-    <img src="https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/labs-logo-light.png" width="50%" />
+    <img src="docs/source/_static/labs-logo-light.png" width="50%" />
 </center>
 
-For businesses looking to integrate PyMC-Marketing into their operational framework, [PyMC Labs](https://www.pymc-labs.com) offers expert consulting and training. Our team is proficient in state-of-the-art Bayesian modeling techniques, with a focus on Marketing Mix Models (MMMs) and Customer Lifetime Value (CLV). For more information see [here](#-schedule-a-free-consultation-for-mmm--clv-strategy).
+For businesses looking to integrate PyMC-Marketing into their operational framework, [PyMC Labs](https://www.pymc-labs.com) offers expert consulting and training. Our team is proficient in state-of-the-art Bayesian modeling techniques, with a focus on Marketing Mix Models (MMMs) and Customer Lifetime Value (CLV). For more information see [here](README.md#-schedule-a-free-consultation-for-mmm--clv-strategy).
 
 Explore these topics further by watching our video on [Bayesian Marketing Mix Models: State of the Art](https://www.youtube.com/watch?v=xVx91prC81g).
 
@@ -54,7 +54,7 @@ For a comprehensive installation guide, refer to the [official PyMC installation
 
 ### Docker
 
-We provide a `Dockerfile` to build a Docker image for PyMC-Marketing so that is accessible from a Jupyter Notebook. See [here](/scripts/docker/README.md) for more details.
+We provide a `Dockerfile` to build a Docker image for PyMC-Marketing so that is accessible from a Jupyter Notebook. See [here](scripts/docker/README.md) for more details.
 
 ## In-depth Bayesian Marketing Mix Modeling (MMM) in PyMC
 
@@ -111,12 +111,12 @@ mmm.fit(X,y)
 mmm.plot_components_contributions();
 ```
 
-![](https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/mmm_plot_components_contributions.png)
+![](docs/source/_static/mmm_plot_components_contributions.png)
 
 Once the model is fitted, we can further optimize our budget allocation as we are including diminishing returns and carry-over effects in our model.
 
 <center>
-    <img src="/docs/source/_static/mmm_plot_plot_channel_contributions_grid.png" width="80%" />
+    <img src="docs/source/_static/mmm_plot_plot_channel_contributions_grid.png" width="80%" />
 </center>
 
 Explore a hands-on [simulated example](https://pymc-marketing.readthedocs.io/en/stable/notebooks/mmm/mmm_example.html) for more insights into MMM with PyMC-Marketing.
@@ -166,19 +166,19 @@ beta_geo_model.fit()
 
 Once fitted, we can use the model to predict the number of future purchases for known customers, the probability that they are still alive, and get various visualizations plotted.
 
-![](https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/expected_purchases.png)
+![](docs/source/_static/expected_purchases.png)
 
 See the Examples section for more on this.
 
 ## Why PyMC-Marketing vs other solutions?
 
 PyMC-Marketing is and will always be free for commercial use, licensed under [Apache 2.0](LICENSE). Developed by core developers behind the popular PyMC package and marketing experts, it provides state-of-the-art measurements and analytics for marketing teams.
 
-Due to its open-source nature and active contributor base, new features are constantly added. Are you missing a feature or want to contribute? Fork our repository and submit a pull request. If you have any questions, feel free to [open an issue](https://github.com/your-repo/issues).
+Due to its open-source nature and active contributor base, new features are constantly added. Are you missing a feature or want to contribute? Fork our repository and submit a pull request. If you have any questions, feel free to [open an issue](https://github.com/pymc-labs/pymc-marketing/issues).
 
 ### Thanks to our contributors!
 
-[![https://github.com/pymc-devs/pymc/graphs/contributors](https://contrib.rocks/image?repo=pymc-labs/pymc-marketing)](https://github.com/pymc-labs/pymc-marketing/graphs/contributors)
+[![https://github.com/pymc-labs/pymc-marketing/graphs/contributors](https://contrib.rocks/image?repo=pymc-labs/pymc-marketing)](https://github.com/pymc-labs/pymc-marketing/graphs/contributors)
 
 
 ## Marketing AI Assistant: MMM-GPT with PyMC-Marketing

pymc_marketing/version.py

@@ -13,16 +13,4 @@
 #   limitations under the License.
 """Version of the package."""
 
-import os
-
-here = os.path.dirname(os.path.realpath(__file__))
-
-
-def read_version() -> str:
-    """Read the version from the version file."""
-    version_file = os.path.join(here, "version.txt")
-    with open(version_file, encoding="utf-8") as buff:
-        return buff.read().splitlines()[0]
-
-
-__version__ = read_version()
+__version__ = "0.9.0"

pymc_marketing/version.txt

@@ -1,14 +1,13 @@
 [build-system]
-build-backend = "setuptools.build_meta"
-requires = ["setuptools>=61.0"]
+build-backend = "hatchling.build"
+requires = ["hatchling<2", "hatch-fancy-pypi-readme"]
 
 [project]
 name = "pymc-marketing"
 description = "Marketing Statistical Models in PyMC"
 requires-python = ">=3.10"
-readme = "README.md"
 license = { file = "LICENSE" }
-dynamic = ["version"]
+dynamic = ["version", "readme"]
 maintainers = [{ name = "PyMC Labs", email = "[email protected]" }]
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -80,17 +79,37 @@ test = [
     "mlflow>=2.0.0",
 ]
 
-[tool.setuptools]
-packages = [
-    "pymc_marketing",
-    "pymc_marketing.mmm",
-    "pymc_marketing.mmm.components",
-    "pymc_marketing.clv",
-    "pymc_marketing.clv.models",
+[tool.hatch.build.targets.sdist]
+exclude = [
+    "docs",
 ]
 
-[tool.setuptools.dynamic]
-version = { file = "pymc_marketing/version.txt" }
+[tool.hatch.version]
+path = "pymc_marketing/version.py"
+
+# <https://github.com/hynek/hatch-fancy-pypi-readme>
+[tool.hatch.metadata.hooks.fancy-pypi-readme]
+content-type = "text/markdown"
+
+[[tool.hatch.metadata.hooks.fancy-pypi-readme.fragments]]
+path = "README.md"
+
+[[tool.hatch.metadata.hooks.fancy-pypi-readme.substitutions]]
+# Image links should go to the raw content on GitHub
+# <https://stackoverflow.com/a/46875147>
+pattern = '\[(.*?)\]\(((?!https?://)\S+\.(png|jpe?g|svg|gif))\)'
+replacement = '[\1](https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/\g<2>)'
+
+[[tool.hatch.metadata.hooks.fancy-pypi-readme.substitutions]]
+# Handle also HTML image tags
+pattern = '''(<img\b[^>]*\bsrc=)(['"])((?!https?://)[^'"]+)(['"][^>]*>)'''
+replacement = '<img src="https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/\g<3>\g<4>'
+
+[[tool.hatch.metadata.hooks.fancy-pypi-readme.substitutions]]
+# Remaining non-image relative links map to the normal absolute GitHub URL
+# <https://stackoverflow.com/a/46875147>
+pattern = '\[(.*?)\]\(((?!https?://)\S+)\)'
+replacement = '[\1](https://github.com/pymc-labs/pymc-marketing/tree/main/\g<2>)'
 
 [project.urls]
 repository = "https://github.com/pymc-labs/pymc-marketing"