add google colab and github buttons to machine learning tutorial

README.md

@@ -1,6 +1,11 @@
 # Machine Learning Tutorial 
 
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-tutorial-machine-learning/HEAD?labpath=index.ipynb)
+<a target="_blank" href="https://colab.research.google.com/github/danforthcenter/plantcv-tutorial-machine-learning.git">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+[![Static Badge](https://img.shields.io/badge/Open%20on%20GitHub-black?logo=github)](https://github.com/danforthcenter/plantcv-tutorial-machine-learning.git)
+
 
 The naive Bayes multiclass approach is an extension of the naive Bayes approach. It can be trained to output binary images given an input color image. Unlike the naive Bayes method, the naive Bayes multiclass approach can be trained to classify two or more classes, defined by the user. Additionally, the naive Bayes multiclass method is trained using colors sparsely sampled from images rather than the need to label all pixels in a given image.
 

index.ipynb

@@ -3,7 +3,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "# Machine Learning Tutorial \n",
@@ -43,6 +46,36 @@
     "Once we have the `plantcv-train` output file, we can classify pixels in a color image in PlantCV. In the example image for this tutorial we have already collected pixels and created the probability density functions for each class. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Detect notebook environment\n",
+    "def detect_notebook_environment():\n",
+    "    if 'COLAB_GPU' in os.environ:\n",
+    "        print('Google Colaboratory detected.')\n",
+    "        %pip install \"altair>=5\" ipympl plantcv\n",
+    "        \n",
+    "        # Give access and mount your Google Drive (need a Google Account)\n",
+    "        from google.colab import drive\n",
+    "        drive.mount('/content/gdrive')\n",
+    "        \n",
+    "        # Enable widget feature with matplotlib\n",
+    "        from google.colab import output\n",
+    "        output.enable_custom_widget_manager()\n",
+    "        \n",
+    "        #View working directory, may need to change\n",
+    "        %pwd\n",
+    "    else:\n",
+    "        print('Binder detected.')\n",
+    "\n",
+    "environment = detect_notebook_environment()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -539,9 +572,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (PlantCV 4)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "plantcv4"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -553,9 +586,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }