Merge branch 'main' into 'manar_branch1'

# Conflicts:
#   GUI_Tutorial.ipynb

Classification.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8459055e",
+   "metadata": {},
+   "source": [
+    "# Classification\n",
+    "\n",
+    "**_NOTE_** autosklearn only will run in linux (feb 26, 2022)\n",
+    "\n",
+    "Example coming from [here](https://automl.github.io/auto-sklearn/master/examples/20_basic/example_classification.html#sphx-glr-examples-20-basic-example-classification-py)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c69433ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "from pprint import pprint\n",
+    "\n",
+    "import sklearn.datasets\n",
+    "import sklearn.metrics\n",
+    "import pickle\n",
+    "\n",
+    "import autosklearn.classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b1e1930",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split the dataset\n",
+    "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n",
+    "X_train, X_test, y_train, y_test = \\\n",
+    "    sklearn.model_selection.train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15e5f821",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fit the classifier\n",
+    "automl = autosklearn.classification.AutoSklearnClassifier(\n",
+    "    time_left_for_this_task=120,\n",
+    "    per_run_time_limit=30,\n",
+    "    tmp_folder='/tmp/autosklearn_classification_example_tmp',\n",
+    ")\n",
+    "automl.fit(X_train, y_train, dataset_name='breast_cancer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4e4d9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Different Models run by autosklearn\n",
+    "print(automl.leaderboard())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72e580e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show the different models\n",
+    "pprint(automl.show_models(), indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "027039cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict the test labels\n",
+    "predictions = automl.predict(X_test)\n",
+    "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acd372ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export the model with the highest rank\n",
+    "clf = automl.show_models()[7]['sklearn_classifier']\n",
+    "pickle.dump(clf,open('model.pickle','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3324782",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "021b7159",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

GAMA_AutoML_Tutorial.ipynb

@@ -26,6 +26,19 @@
     "Image source: https://github.com/openml-labs/gama/raw/master/images/logos/Logo-With-Grey-Name-Transparent.png"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "481fae1c",
+   "metadata": {},
+   "source": [
+    "## View Documentation At:\n",
+    "* https://openml-labs.github.io/gama/master/\n",
+    "* https://github.com/openml-labs/gama\n",
+    "* https://openml-labs.github.io/gama/master/index.html\n",
+    "* https://openml-labs.github.io/gama/master/user_guide/index.html#dashboard\n",
+    "* https://openml-labs.github.io/gama/master/api/index.html"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "25258b82",
@@ -49,7 +62,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# pip install gama"
+    "# pip install gama\n",
+    "#     *or*\n",
+    "# pip3 install gama "
    ]
   },
   {
@@ -88,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "00a14ad0",
    "metadata": {},
    "outputs": [],
@@ -162,32 +177,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a43e62f3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting `fit` which will take roughly 3 minutes...\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_25136/719449767.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mautoml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGamaClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_total_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m180\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nothing\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Starting `fit` which will take roughly 3 minutes...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mautoml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0mlabel_predictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mautoml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\REPOS\\DataTools_Tutorial_Demo\\envs_gama\\lib\\site-packages\\gama\\GamaClassifier.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, *args, **kwargs)\u001b[0m\n\u001b[0;32m    132\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_label_encoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluation_library\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetermine_sample_indices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstratify\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_encode_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\REPOS\\DataTools_Tutorial_Demo\\envs_gama\\lib\\site-packages\\gama\\gama.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, warm_start)\u001b[0m\n\u001b[0;32m    537\u001b[0m             )\n\u001b[0;32m    538\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_post_processing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdynamic_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 539\u001b[1;33m             self.model = self._post_processing.post_process(\n\u001b[0m\u001b[0;32m    540\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\REPOS\\DataTools_Tutorial_Demo\\envs_gama\\lib\\site-packages\\gama\\postprocessing\\best_fit.py\u001b[0m in \u001b[0;36mpost_process\u001b[1;34m(self, x, y, timeout, selection)\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIndividual\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     ) -> object:\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_selected_individual\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselection\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_selected_individual\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_breast_cancer\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -210,6 +203,14 @@
     "print(\"log loss:\", log_loss(y_test, probability_predictions))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1bfbb41b",
+   "metadata": {},
+   "source": [
+    "Some issues with the classifier receiving \"ValueError: population must be at least of size 3 for a pair to be selected\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "de9afff2",
@@ -222,31 +223,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "35f68b4c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting `fit` which will take roughly 3 minutes...\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_25136/3966760049.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mautoml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGamaRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_total_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m180\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nothing\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Starting `fit` which will take roughly 3 minutes...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mautoml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mautoml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\REPOS\\DataTools_Tutorial_Demo\\envs_gama\\lib\\site-packages\\gama\\gama.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, warm_start)\u001b[0m\n\u001b[0;32m    537\u001b[0m             )\n\u001b[0;32m    538\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_post_processing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdynamic_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 539\u001b[1;33m             self.model = self._post_processing.post_process(\n\u001b[0m\u001b[0;32m    540\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\REPOS\\DataTools_Tutorial_Demo\\envs_gama\\lib\\site-packages\\gama\\postprocessing\\best_fit.py\u001b[0m in \u001b[0;36mpost_process\u001b[1;34m(self, x, y, timeout, selection)\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIndividual\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     ) -> object:\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_selected_individual\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselection\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_selected_individual\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_boston\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -282,25 +262,6 @@
     "\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ffdabb07",
-   "metadata": {},
-   "source": [
-    "## References"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "500eb430",
-   "metadata": {},
-   "source": [
-    "* https://github.com/openml-labs/gama\n",
-    "* https://openml-labs.github.io/gama/master/index.html\n",
-    "* https://openml-labs.github.io/gama/master/user_guide/index.html#dashboard\n",
-    "* https://openml-labs.github.io/gama/master/api/index.html"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e0a28b97",
@@ -326,7 +287,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,

GUI_Tutorial.ipynb

@@ -31,14 +31,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 1,
+   "id": "cdfdbb3e",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.9.7\r\n"
+      "Python 3.8.2\r\n"
      ]
     }
    ],
@@ -46,9 +49,18 @@
     "!python --version"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b8de36df",
+   "metadata": {},
+   "source": [
+    "On your terminal,"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
+   "id": "8f5edb3b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +76,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
+   "id": "227461d8",
    "metadata": {
     "scrolled": false
    },
@@ -84,7 +97,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
+   "id": "92c83d6b",
    "metadata": {
     "scrolled": true
    },
@@ -93,7 +107,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7860/\n",
+      "Running on local URL:  http://127.0.0.1:7860\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -101,19 +115,10 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"900\"\n",
-       "            height=\"500\"\n",
-       "            src=\"http://127.0.0.1:7860/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fcaf03ab5b0>"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -121,13 +126,9 @@
     },
     {
      "data": {
-      "text/plain": [
-       "(<fastapi.applications.FastAPI at 0x7fcac199eee0>,\n",
-       " 'http://127.0.0.1:7860/',\n",
-       " None)"
-      ]
+      "text/plain": []
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,6 +157,71 @@
   },
   {
    "cell_type": "markdown",
+   "id": "d4bd4273",
+   "metadata": {},
+   "source": [
+    "### Adding additional Inputs:\n",
+    "\n",
+    "Suppose you had a more complex function, with multiple inputs and outputs. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number. Take a look how you pass a list of input and output components.\n",
+    "\n",
+    "\n",
+    "Source: https://gradio.app/quickstart/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dcc87cd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def greet(name, is_morning, temperature):\n",
+    "    salutation = \"Good morning\" if is_morning else \"Good evening\"\n",
+    "    greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n",
+    "    celsius = (temperature - 32) * 5 / 9\n",
+    "    return greeting, round(celsius, 2)\n",
+    "\n",
+    "demo = gr.Interface(\n",
+    "    fn=greet,\n",
+    "    inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n",
+    "    outputs=[\"text\", \"number\"],\n",
+    ")\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46e5071f",
    "metadata": {},
    "source": [
     "### Uses of Gradio"
@@ -187,11 +253,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "An example of a question could be, what is the conclusion of the sentence.....\n",
-    "\n",
-    "Today will be a good day. \n",
+    "An example of a question could be, what is the conclusion of the sentence: Today will be a good day.\n",
     "\n",
-    "For that you would be using a text generation model called GPT-2 and the example given user's Text Interface. \n",
+    "A user could use Gardio to create an input field, then use the information from that field to generate a response.  For example, the input could be sent to a text generation model such as GPT-2 to generate a response. \n",
     "\n",
     "For a visual from data, you could create a cat/dog image classification model and Gradio demo to upload new images for class prediction. The model will be a Keras convolutional neural network (CNN) that would be trained on images of cats and dogs as features and their class names as labels"
    ]
@@ -204,12 +268,15 @@
     "- For projects with time series modeling\n",
     "    * Hope Village\n",
     "    * Argonne\n",
+    "  (Include example of implemntation here)\n",
     "- For image analysis\n",
     "    * Neogen\n",
     "    * Ford\n",
+    "  (Include example of implemntation here)\n",
     "- Machine learning group\n",
     "    * Kelloggs\n",
-    "    * AFRL"
+    "    * AFRL\n",
+    "  (Include example of implemntation here)\n"
    ]
   },
   {
@@ -239,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,

GoogleSheetsTutorial.ipynb

@@ -33,10 +33,12 @@
     "    1. This can be found at  https://console.developers.google.com/projectselector2/apis/credentials?pli=1&supportedpurview=project \n",
     "    (you must use your personal email not your MSU email)\n",
     "2. Inside that project enable the Google sheets API and Google Drive for your new project\n",
-    "    1. This is found by using the search bar to search for Google API and Google Drive\n",
+    "    1. Use the  search bar to find and click on the Google Sheets API.\n",
+    "    2. Enable the API by selecting the blue button.\n",
+    "    3. Repeat the first two steps for the Google Drive API.\n",
     "3. Create a Google service account through the Google developer portal\n",
     "4. Go to “APIs & Services > Credentials” and choose “Create credentials > Service account key”.\n",
-    "5. Fill out the form (making sure to add editor privledges to the service account)\n",
+    "5. Once you open the "Create service account" screen, fill out the name and ID fields and click "Create and Continue" to move to the next section. After filling out "1. Service account details" under "2. Grant this service account access to project" navigate to the "Role" drop down and choose "Basic" -> 'Editor." After you select the role, hit "Continue." You can skip the next section, "Grant users access to this service account" and click done. \n",
     "6. Click “Create” and “Done”.\n",
     "7. Press “Manage service accounts” above Service Accounts.\n",
     "8. Press on ⋮ near recently created service account and select “Manage keys” and then click on “ADD KEY > Create new key”.\n",
@@ -229,7 +231,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -243,7 +245,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,

Video-Image-Data-Tutorial/Ford_Video_Analysis.ipynb

 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Ford_Video_Analysis.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
+<<<<<<< HEAD
   "cells": [
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "OqSmFS3lZj9Y"
+      },
       "source": [
         "# <center> Video Image Data </center>\n",
         "#### CMSE 495 Ford Group\n",
         "\n",
         "This tutorial teaches the user how to input a video file, such a mp4 and convert each frame of the video into a jpeg image using python, primarily in a Jupyter notebook."
-      ],
-      "metadata": {
-        "id": "OqSmFS3lZj9Y"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pathakis/DataTools_Tutorial_Demo/blob/main/Video-Image-Data-Tutorial/Ford_Video_Analysis.ipynb)"
-      ],
       "metadata": {
         "id": "uey1neRTkRwd"
-      }
+      },
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pathakis/DataTools_Tutorial_Demo/blob/main/Video-Image-Data-Tutorial/Ford_Video_Analysis.ipynb)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "GQIJxk_kdjT1"
+      },
       "source": [
         "<b> Environment Setup (Makefile):</b>\n",
         "- Use the command 'make innit' automatically set up the environment for you.\n",
@@ -61,33 +49,35 @@
         "<b> Usage Instructions:</b>\n",
         "\n",
         "- The example call shows the format in which this func may be used.\n"
-      ],
-      "metadata": {
-        "id": "GQIJxk_kdjT1"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "This process uses 2  packages called [os](https://docs.python.org/3/library/os.html)  and [cv2](https://pypi.org/project/opencv-python/). Os provides  miscellaneous operating system interfaces such as opening and reading the files."
-      ],
       "metadata": {
         "id": "f0aUW4PLdobE"
-      }
+      },
+      "source": [
+        "This process uses 2  packages called [os](https://docs.python.org/3/library/os.html)  and [cv2](https://pypi.org/project/opencv-python/). Os provides  miscellaneous operating system interfaces such as opening and reading the files."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# !pip install opencv-python"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "JGkN_k3BgXV8"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "# !pip install opencv-python"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PCbpVR-HZzmt"
+      },
+      "outputs": [],
       "source": [
         "import cv2\n",
         "import os\n",
@@ -128,28 +118,29 @@
         "  \n",
         "  #releasing the threads\n",
         "    vidcap.release()\n"
-      ],
-      "metadata": {
-        "id": "PCbpVR-HZzmt"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "hBfLcvMhh6v7"
+      },
       "source": [
         "DEMO FOR THE **avi_to_frames** \n",
         "\n",
-        "1. To download a sample avi file that you want to work with, use the following code `urllib.request.urlretrieve('https://www.engr.colostate.edu/me/facil/dynamics/files/drop.avi', 'testing.mp4')` \n",
+        "1. To download a sample avi file that you want to work with, use the following code `urllib.request.urlretrieve('https://file-examples.com/wp-content/uploads/2018/04/file_example_AVI_480_750kB.avi', 'testing.mp4')` \n",
         "\n",
         "2. After the video has been downloaded `avi_frames(./testing.mp4, path_to_where_you_want_the_frames, False)` this will create a folder with frames from the video."
-      ],
-      "metadata": {
-        "id": "hBfLcvMhh6v7"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-N-hJD11jJjo"
+      },
+      "outputs": [],
       "source": [
         "# Making a Video From Frames\n",
         "def frames_to_video(directory_path, fps, width, height):\n",
@@ -163,15 +154,13 @@
         "\n",
         "  cv2.destroyAllWindows()\n",
         "  video.release()"
-      ],
-      "metadata": {
-        "id": "-N-hJD11jJjo"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "sIhvc4DplLkE"
+      },
       "source": [
         "DEMO FOR THE **frames_to_video** \n",
         "\n",
@@ -180,46 +169,317 @@
         "2. Run the following command (**make the necessary changes in the function call**)`frames_to_video(where_the_frames_are, fps, width, height)`\n",
         "\n",
         "3. The video will show up in the current directory."
-      ],
-      "metadata": {
-        "id": "sIhvc4DplLkE"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "**The code below will put the image arrays into a list.** This snippet of code utilizes glob but packages like os can also be used."
-      ],
       "metadata": {
         "id": "bnfzxPNJeZVS"
-      }
+      },
+      "source": [
+        "**The code below will put the image arrays into a list.** This snippet of code utilizes glob but packages like os can also be used."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mLbqPW7SeJd-"
+      },
+      "outputs": [],
       "source": [
         "path = glob.glob(\"./*.jpg\")\n",
         "images = []\n",
         "for img in path:\n",
         "  n = cv2.imread(img)\n",
         "  images.append(n)"
-      ],
-      "metadata": {
-        "id": "mLbqPW7SeJd-"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "GIq-_h4wdxWM"
+      },
       "source": [
         "<b> References:</b>\n",
         "- [Managing Environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)\n",
         "- [Open CV in python](https://pypi.org/project/opencv-python/)\n",
         "- [Colab Button](https://www.youtube.com/watch?v=RoGZIbwzG5w)"
-      ],
-      "metadata": {
-        "id": "GIq-_h4wdxWM"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Ford_Video_Analysis.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.15"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "4f3567101b31d35f97cf2856951ebbba1e09a3f852422478e736adda2bb3beee"
       }
     }
-  ]
-}
+  },
+  "nbformat": 4,
\ No newline at end of file
+  "nbformat_minor": 0
+=======
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OqSmFS3lZj9Y"
+   },
+   "source": [
+    "# <center> Video Image Data </center>\n",
+    "#### CMSE 495 Ford Group\n",
+    "\n",
+    "This tutorial teaches the user how to input a video file, such a mp4 and convert each frame of the video into a jpeg image using python, primarily in a Jupyter notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uey1neRTkRwd"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pathakis/DataTools_Tutorial_Demo/blob/main/Video-Image-Data-Tutorial/Ford_Video_Analysis.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GQIJxk_kdjT1"
+   },
+   "source": [
+    "<b> Environment Setup (Makefile):</b>\n",
+    "- Use the command 'make innit' automatically set up the environment for you.\n",
+    "\n",
+    "<b> Environment Setup (Manual):</b>\n",
+    "- Set up new environment using pip/conda (Conda Recommended). Use command \n",
+    "\n",
+    "    <code> conda create -n envs python=3.10 </code>\n",
+    "\n",
+    "- Activate your new environment. Use command \n",
+    "\n",
+    "    <code> conda activate envs</code>\n",
+    "\n",
+    "- Install the requisite packages.Use command \n",
+    "\n",
+    "    <code> pip install opencv-python</code> or,\n",
+    "\n",
+    "    <code> conda install -c conda-forge opencv</code>\n",
+    "\n",
+    "<b> Usage Instructions:</b>\n",
+    "\n",
+    "- The example call shows the format in which this func may be used.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f0aUW4PLdobE"
+   },
+   "source": [
+    "This process uses 2  packages called [os](https://docs.python.org/3/library/os.html)  and [cv2](https://pypi.org/project/opencv-python/). Os provides  miscellaneous operating system interfaces such as opening and reading the files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JGkN_k3BgXV8"
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "PCbpVR-HZzmt"
+   },
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import os\n",
+    "import glob\n",
+    "import urllib.request\n",
+    "\n",
+    "\n",
+    "def video_to_frames(file_path, directory_path, greyscale = False):\n",
+    "\n",
+    "  '''This function will change a video file to a frames'''\n",
+    "    \n",
+    "    #opening the video\n",
+    "  vidcap = cv2.VideoCapture(file_path) \n",
+    "    \n",
+    "  dirname = directory_path\n",
+    "  os.makedirs(dirname, exist_ok=True)\n",
+    "  \n",
+    "  #capturing a frame as well as a boolean value representing whether an image was properly opened\n",
+    "  success,image = vidcap.read()\n",
+    "  \n",
+    "  count = 0\n",
+    "  \n",
+    "  while success:\n",
+    "      \n",
+    "    #this is specifically for foam_segmented.avi\n",
+    "    if greyscale:\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
+    "        \n",
+    "    #writing the image to a the directory path that was specified, \n",
+    "    #if the path specified does not exist then it will be created\n",
+    "    #this finctionality was added so that the images could be stored in a separate folder\n",
+    "    #example of output file names: 1.jpg, 2.jpg, 3.jpg, and so on\n",
+    "    cv2.imwrite(os.path.join(dirname, str(count)+\".jpg\"), image)\n",
+    "    success,image = vidcap.read()\n",
+    "    count += 1\n",
+    "    #All the frames will be added in order\n",
+    "    cv2.waitKey(1) \n",
+    "  \n",
+    "  #releasing the threads\n",
+    "    vidcap.release()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hBfLcvMhh6v7"
+   },
+   "source": [
+    "DEMO FOR THE **avi_to_frames** \n",
+    "\n",
+    "1. To download a sample avi file that you want to work with, use the following code `urllib.request.urlretrieve('https://www.engr.colostate.edu/me/facil/dynamics/files/drop.avi', 'testing.mp4')` \n",
+    "\n",
+    "2. After the video has been downloaded `avi_frames(./testing.mp4, path_to_where_you_want_the_frames, False)` this will create a folder with frames from the video."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "-N-hJD11jJjo"
+   },
+   "outputs": [],
+   "source": [
+    "# Making a Video From Frames\n",
+    "def frames_to_video(directory_path, fps, width, height):\n",
+    "  fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
+    "  video = cv2.VideoWriter('video.avi', fourcc, fps, (width, height))\n",
+    "  num_frames = len([name for name in os.listdir(directory_path) if os.path.isfile(name)])\n",
+    "\n",
+    "  for j in range(num_frames):\n",
+    "    img = cv2.imread(str(j) + '.jpg')\n",
+    "    video.write(img)\n",
+    "\n",
+    "  cv2.destroyAllWindows()\n",
+    "  video.release()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sIhvc4DplLkE"
+   },
+   "source": [
+    "DEMO FOR THE **frames_to_video** \n",
+    "\n",
+    "1. We will be working with the frames that we created using `avi_to_frames`. If you have not created those frames feel free to look at the steps above.\n",
+    "\n",
+    "2. Run the following command (**make the necessary changes in the function call**)`frames_to_video(where_the_frames_are, fps, width, height)`\n",
+    "\n",
+    "3. The video will show up in the current directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bnfzxPNJeZVS"
+   },
+   "source": [
+    "**The code below will put the image arrays into a list.** This snippet of code utilizes glob but packages like os can also be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "mLbqPW7SeJd-"
+   },
+   "outputs": [],
+   "source": [
+    "path = glob.glob(\"./*.jpg\")\n",
+    "images = []\n",
+    "for img in path:\n",
+    "  n = cv2.imread(img)\n",
+    "  images.append(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GIq-_h4wdxWM"
+   },
+   "source": [
+    "<b> References:</b>\n",
+    "- [Managing Environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)\n",
+    "- [Open CV in python](https://pypi.org/project/opencv-python/)\n",
+    "- [Colab Button](https://www.youtube.com/watch?v=RoGZIbwzG5w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Comments:**\n",
+    "\n",
+    "The videos is not working, it only downloaded a unavailable video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Ford_Video_Analysis.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+>>>>>>> 5c328fa (this is a testing comment)
+}

social_media_scraper/requirements.txt

-#### Requirements for YouTube scraper
-pandas
-matplotlib
-pytchat
-time
\ No newline at end of file