From 3143424b18f019dc271e11a5879970e2cdc7f493 Mon Sep 17 00:00:00 2001
From: Jason Jafari <me@jasonjafari.com>
Date: Sun, 16 Jun 2024 13:52:20 -0400
Subject: [PATCH] chore: bump version to 1.0.22

---
 DOCS.md      | 49 +++++++++++++++++++++++++++++++++++++++++++++++++
 package.json |  2 +-
 setup.py     |  2 +-
 3 files changed, 51 insertions(+), 2 deletions(-)

diff --git a/DOCS.md b/DOCS.md
index 24b9333..ad8f236 100644
--- a/DOCS.md
+++ b/DOCS.md
@@ -7,3 +7,52 @@ While numerous tools are available for training machine learning models, many li
 
 **[mlModelSaver](https://github.com/smartdev-ca/mlModelSaver)** fills this gap, offering an intuitive way to save machine learning models and transformers. It facilitates seamless integration with frameworks like FastAPI ([Examples](https://github.com/jafarijason/ml_models_deployments)), Flask, and Django, enabling easy deployment and serving of models in production environments. Empower your machine learning workflow with **mlModelSaver** – the easy and efficient tool for model management.
 
+## Installation
+
+You can install **mlModelSaver** via pip:
+
+```bash
+pip install mlModelSaver
+```
+
+
+# mlModelSaver Example: Simple Linear Regression
+
+In this example, we demonstrate how to use **mlModelSaver** to export a simple linear regression model based on a notebook from [ml_models_deployments](https://github.com/jafarijason/ml_models_deployments/blob/master/notebooks/001.ipynb).
+
+### Example Description
+
+This example builds a simple linear regression model to predict sales based on temperature, advertising, and discount factors. Once the model is fitted and satisfactory, **mlModelSaver** allows you to easily save and deploy the model for use in production environments.
+
+### Example Code - notebook available [here](https://github.com/jafarijason/ml_models_deployments/blob/master/notebooks/001.ipynb)
+
+
+```python
+def add_constant_columnTransformer(df):
+    # example transformer
+    df_with_const = df.copy()
+    df_with_const.insert(0, 'const', 1)
+    return df_with_const
+
+# Export the model using MlModelSaver
+loadedModel = mlModelSaverInstance.exportModel(
+    simpleLinearRegressionFittedModel, # the models is fitted and ready for usage
+    {
+        "modelName": "modelPredictSaleByTemperatureAdvertisingDiscountFit",
+        "description": "Example model predicting sales based on temperature, advertising, and discount.",
+        "modelType": "sm.OLS",  # Example model type (replace with actual type)
+        "inputs": [
+            {"name": "Temperature", "type": "float"},
+            {"name": "Advertising", "type": "float"},
+            {"name": "Discount", "type": "float"}
+        ],
+        "transformer": add_constant_columnTransformer,  # Use your transformation function here
+        "outputs": [
+            {
+                "name": "Sales",
+                "type": "float"
+            }
+        ]
+    }
+)
+```
diff --git a/package.json b/package.json
index dc7c583..4119eea 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "mlModelSaver",
-  "version": "1.0.21",
+  "version": "1.0.22",
   "description": "Make life easier for save and serving ml models",
   "main": "index.js",
   "repository": "git@github.com:smartdev-ca/mlModelSaver.git",
diff --git a/setup.py b/setup.py
index 57d8d70..ab7c8ea 100644
--- a/setup.py
+++ b/setup.py
@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='mlModelSaver',
-    version='1.0.21',
+    version='1.0.22',
     packages=find_packages(),
     description='Make life easier for saving and serving ML models',
     long_description=open('DOCS.md').read(),  # Assumes you have a README.md file