piskvorky · jicruz96 · Jul 18, 2024 · Jul 18, 2024 · Jul 18, 2024 · Jul 22, 2024
diff --git a/setup.py b/setup.py
@@ -16,6 +16,7 @@
 import shutil
 import sys
 from collections import OrderedDict
+from pathlib import Path
 
 from setuptools import Extension, find_packages, setup, distutils
 from setuptools.command.build_ext import build_ext
@@ -83,6 +84,18 @@ def make_cpp_ext(use_cython=False):
         )
 
 
+def get_long_description():
+    long_description = Path("README.md").read_text(encoding="utf-8")
+    #
+    # We update the image paths in the README to a GitHub URL so that they render correctly on PyPI.
+    # https://stackoverflow.com/questions/41983209/how-do-i-add-images-to-a-pypi-readme-that-works-on-github
+    #
+    docs_path = "docs/src/readme_images/"
+    raw_github_url = "https://raw.githubusercontent.com/piskvorky/gensim/master/" + docs_path
+    long_description = long_description.replace(docs_path, raw_github_url)
+    return long_description
+
+
 #
 # We use use_cython=False here for two reasons:
 #
@@ -158,112 +171,6 @@ def run(self):
     cmdclass.update(vars(wheelhouse_uploader.cmd))
 
 
-LONG_DESCRIPTION = u"""
-==============================================
-gensim -- Topic Modelling in Python
-==============================================
-
-|GA|_
-|Wheel|_
-
-.. |GA| image:: https://github.com/RaRe-Technologies/gensim/actions/workflows/tests.yml/badge.svg?branch=develop
-.. |Wheel| image:: https://img.shields.io/pypi/wheel/gensim.svg
-
-.. _GA: https://github.com/RaRe-Technologies/gensim/actions
-.. _Downloads: https://pypi.org/project/gensim/
-.. _License: https://radimrehurek.com/gensim/intro.html#licensing
-.. _Wheel: https://pypi.org/project/gensim/
-
-Gensim is a Python library for *topic modelling*, *document indexing* and *similarity retrieval* with large corpora.
-Target audience is the *natural language processing* (NLP) and *information retrieval* (IR) community.
-
-Features
----------
-
-* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core)
-* **Intuitive interfaces**
-
-  * easy to plug in your own input corpus/datastream (simple streaming API)
-  * easy to extend with other Vector Space algorithms (simple transformation API)
-
-* Efficient multicore implementations of popular algorithms, such as online **Latent Semantic Analysis (LSA/LSI/SVD)**,
-  **Latent Dirichlet Allocation (LDA)**, **Random Projections (RP)**, **Hierarchical Dirichlet Process (HDP)** or **word2vec deep learning**.
-* **Distributed computing**: can run *Latent Semantic Analysis* and *Latent Dirichlet Allocation* on a cluster of computers.
-* Extensive `documentation and Jupyter Notebook tutorials <https://github.com/RaRe-Technologies/gensim/#documentation>`_.
-
-
-If this feature list left you scratching your head, you can first read more about the `Vector
-Space Model <https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
-document analysis <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
-
-Installation
-------------
-
-This software depends on `NumPy and Scipy <https://scipy.org/install/>`_, two Python packages for scientific computing.
-You must have them installed prior to installing `gensim`.
-
-It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as MKL, `ATLAS <https://math-atlas.sourceforge.net/>`_ or `OpenBLAS <https://xianyi.github.io/OpenBLAS/>`_ is known to improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don't need to do anything special.
-
-Install the latest version of gensim::
-
-    pip install --upgrade gensim
-
-Or, if you have instead downloaded and unzipped the `source tar.gz <https://pypi.org/project/gensim/>`_ package::
-
-    python setup.py install
-
-
-For alternative modes of installation, see the `documentation <https://radimrehurek.com/gensim/#install>`_.
-
-Gensim is being `continuously tested <https://radimrehurek.com/gensim/#testing>`_ under all `supported Python versions <https://github.com/RaRe-Technologies/gensim/wiki/Gensim-And-Compatibility>`_.
-Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.
-
-
-How come gensim is so fast and memory efficient? Isn't it pure Python, and isn't Python slow and greedy?
---------------------------------------------------------------------------------------------------------
-
-Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured).
-
-Memory-wise, gensim makes heavy use of Python's built-in generators and iterators for streamed data processing. Memory efficiency was one of gensim's `design goals <https://radimrehurek.com/gensim/intro.html#design-principles>`_, and is a central feature of gensim, rather than something bolted on as an afterthought.
-
-Documentation
--------------
-* `QuickStart`_
-* `Tutorials`_
-* `Tutorial Videos`_
-* `Official Documentation and Walkthrough`_
-
-Citing gensim
--------------
-
-When `citing gensim in academic papers and theses <https://scholar.google.cz/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:u-x6o8ySG0sC>`_, please use this BibTeX entry::
-
-  @inproceedings{rehurek_lrec,
-        title = {{Software Framework for Topic Modelling with Large Corpora}},
-        author = {Radim {\\v R}eh{\\r u}{\\v r}ek and Petr Sojka},
-        booktitle = {{Proceedings of the LREC 2010 Workshop on New
-             Challenges for NLP Frameworks}},
-        pages = {45--50},
-        year = 2010,
-        month = May,
-        day = 22,
-        publisher = {ELRA},
-        address = {Valletta, Malta},
-        language={English}
-  }
-
-----------------
-
-Gensim is open source software released under the `GNU LGPLv2.1 license <https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html>`_.
-Copyright (c) 2009-now Radim Rehurek
-
-.. _Official Documentation and Walkthrough: https://radimrehurek.com/gensim/
-.. _Tutorials: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials
-.. _Tutorial Videos: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#videos
-.. _QuickStart: https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/core/run_core_concepts.html
-
-"""
-
 distributed_env = ['Pyro4 >= 4.27']
 
 visdom_req = ['visdom >= 0.1.8, != 0.1.8.7']
@@ -342,8 +249,8 @@ def run(self):
     name='gensim',
     version='4.3.3',
     description='Python framework for fast Vector Space Modelling',
-    long_description=LONG_DESCRIPTION,
-
+    long_description=get_long_description(),
+    long_description_content_type='text/markdown',
     ext_modules=ext_modules,
     cmdclass=cmdclass,
     packages=find_packages(),