adding badge (#28)

* adding badge

* docfix

* removing html

* add whiteline

* adding command to build docs

* adding dev deps

* add endline

.github/actions/setup_lint_and_test/action.yml

@@ -14,6 +14,9 @@ runs:
   - name: run linting
     run: make lint-check
     shell: bash
+  - name: Check docs building
+    run: make docs-build
+    shell: bash
   - name: Test with pytest
     run: |
       echo pytest tests

.gitignore

@@ -167,4 +167,6 @@ HOSTNAME
 __version__.py
 
 .ruff_cache/
-.vscode/
+.vscode/
+
+html/

Makefile

@@ -10,6 +10,10 @@ build:
 clean:
 	rm -rf build dist
 
+docs-build:
+	cd docs && \
+	python -m sphinx -T -W --keep-going -b html -d _build/doctrees -D language=en . ./html
+
 lint-check:
 	ruff check ./qcog_python_client && \
 	mypy ./qcog_python_client

README.md

@@ -1,7 +1,8 @@
 # Qognitive Python Client Library
 
 [![Documentation Status](https://readthedocs.com/projects/qognitive-qcog-python-client/badge/?version=latest)](https://qognitive-qcog-python-client.readthedocs-hosted.com/en/latest/?badge=latest)
-[![.github/workflows/merge_to_develop.yml](https://github.com/qognitive/qcog-python-client/actions/workflows/merge_to_develop.yml/badge.svg?branch=develop)](https://github.com/qognitive/qcog-python-client/actions/workflows/merge_to_develop.yml)
+
+[![.github/workflows/pypi.yml](https://github.com/qognitive/qcog-python-client/actions/workflows/pypi.yml/badge.svg)](https://github.com/qognitive/qcog-python-client/actions/workflows/pypi.yml)
 
 The Qognitive Python Client library is a programmatic way of interacting with Qognitive's cloud computation API for running Quantum Cognition Machine Learning (QCML) models.
 

docs/examples/coil20.rst

@@ -122,6 +122,7 @@ Training the Model
 Now set some training specific parameters and execute the training.
 
 .. code-block:: python
+
     from qcog_python_client.schema.parameters import AnalyticOptimizationParameters, LOBPCGFastStateParameters
 
     qcml = qcml.train(

docs/examples/timeseries.rst

@@ -242,6 +242,7 @@ Training the Model
 Now set some training specific parameters and execute the training.
 
 .. code-block:: python
+
     from qcog_python_client.schema.parameters import LOBPCGStateParameters, GradOptimizationParameters
 
     qcml = qcml.train(
@@ -280,6 +281,7 @@ If you are running in the same session you can skip the next step, but if you ar
 With our trained model loaded into the client, we can now run inference on the dataset.
 
 .. code-block:: python
+
     from qcog_python_client.schema.parameters import LOBPCGFastStateParameters
 
     result_df = qcml.inference(

docs/examples/wisconsin.rst

@@ -94,6 +94,7 @@ Training the Model
 Now set some training specific parameters and execute the training.
 
 .. code-block:: python
+
     from qcog_python_client.schema.parameters import GradOptimizationParameters, LOBPCGFastStateParameters
 
     qcml = qcml.train(

docs/requirements.txt

@@ -5,3 +5,4 @@ pandas
 numpy
 aiohttp
 typing_extensions
+pydantic

pyproject.toml

@@ -29,6 +29,8 @@ dev = [
   "pandas-stubs",
   "types-requests",
   "setuptools_scm[toml]>=8",
+  "sphinx",
+  "sphinx_rtd_theme"
 ]
 examples = [
   "scikit-learn",