Initial commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -binary

CapstoneDeploymentTest.ipynb

@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f81d089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import gradio as gr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pathlib import Path\n",
+    "from sklearn.preprocessing import MinMaxScaler, PowerTransformer\n",
+    "\n",
+    "pd.options.mode.chained_assignment = 'warn'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a4a28281",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d4ec3046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modelPath = Path('trainedModel/')\n",
+    "\n",
+    "model = tf.keras.models.load_model(modelPath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ef122845",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_names = [ 'setting1'\n",
+    "             , 'T30', 'T50','P2', 'P15', 'P30', 'Nf', 'Nc', 'Ps30'\n",
+    "             , 'phi', 'NRf', 'NRc', 'BPR','htBleed',\n",
+    "             'W31', 'W32']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "950efda4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testData1 = pd.read_csv('data/testData.csv')\n",
+    "testData1.drop(columns='Unnamed: 0', inplace=True)\n",
+    "testData1.columns = col_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9600ddda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testDataNonNormal = pd.read_csv('data/unnormalizedTestData.csv')\n",
+    "testDataNonNormal.drop(columns='Unnamed: 0', inplace=True)\n",
+    "testDataNonNormal.columns = col_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3ca99247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_names = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 'T2', 'T24'\n",
+    "             , 'T30', 'T50','P2', 'P15', 'P30', 'Nf', 'Nc', 'epr', 'Ps30'\n",
+    "             , 'phi', 'NRf', 'NRc', 'BPR','farB', 'htBleed', 'Nf_dmd',\n",
+    "             'PCNfR_dmd','W31', 'W32', 's22', 's23']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "715ff95e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testData = pd.read_csv(\"data/test.txt\", sep=' ', names=col_names)\n",
+    "testDataNoDrop = pd.read_csv(\"data/test.txt\", sep=' ', names=col_names)\n",
+    "testData  = testData.drop(['id', 'cycle', 'setting2', 'setting3', 'T2',\n",
+    "                          'T24', 'epr', 'farB', 'Nf_dmd', 'PCNfR_dmd', 's22', 's23'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "403d5e69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen = MinMaxScaler(feature_range=(0,1))\n",
+    "pt = PowerTransformer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0e635ec3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(engineId, NRc, T30, P30):\n",
+    "    # W31 is index 15, T30 is index 1, P30 is index 5\n",
+    "    engineIdx = testDataNoDrop.index[testDataNoDrop['id'] == engineId].tolist()\n",
+    "    engineIdx = engineIdx[int(len(engineIdx)/2)]\n",
+    "    \n",
+    "    testData.loc[engineIdx,'NRc'] = NRc\n",
+    "    testData.loc[engineIdx,'T30'] = T30\n",
+    "    testData.loc[engineIdx,'P30'] = P30\n",
+    "    \n",
+    "    testDf = gen.fit_transform(testData)\n",
+    "    testDf = pd.DataFrame(testDf)\n",
+    "    testDf = np.nan_to_num(testDf)\n",
+    "    testDf = pt.fit_transform(testDf)\n",
+    "    testDf = np.array(testDf)\n",
+    "    # truncData = np.array(testData.iloc[engineIdx[0],:])\n",
+    "    # truncData.W31 = W31\n",
+    "    # truncData.T30 = T30\n",
+    "    # truncData.P30 = P30\n",
+    "    # truncData[1] = T30\n",
+    "    # truncData[5] = P30\n",
+    "    # truncData[15] = W31\n",
+    "    # print(truncData)\n",
+    "    \n",
+    "    # truncData = gen.fit_transform(np.array(truncData).reshape(-1,1))\n",
+    "    # print(truncData)\n",
+    "    # truncData = pt.fit_transform(truncData)\n",
+    "    # print(truncData)\n",
+    "    data = testDf[engineIdx]\n",
+    "    data = data.reshape(1, 16)\n",
+    "    # print(data)\n",
+    "    pred = int(model.predict(data))\n",
+    "    \n",
+    "    if pred > 30:\n",
+    "        maintReq = 'No '\n",
+    "    \n",
+    "    return pred\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "94a919e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "defaultNrc = int(max(testData['NRc']) - (max(testData['NRc'])-8075)/2)\n",
+    "defaultT = int(max(testData['T30']) - (max(testData['T30'])-1580)/2)\n",
+    "defaultP = int(max(testData['P30']) - (max(testData['P30'])-550)/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "67a89db6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = [gr.inputs.Slider(1, 100, step=1, label='Engine ID'),\n",
+    "         gr.inputs.Slider(8075, max(testData['NRc']), default=defaultNrc, step=0.1, label='Corrected Engine Core Speed (rpm)'),\n",
+    "         gr.inputs.Slider(1580, max(testData['T30']), default=defaultT, label='Total Temperature at HPC Outlet (\\N{DEGREE SIGN}R)'),\n",
+    "         gr.inputs.Slider(550, max(testData['P30']), default=defaultP, label='Total Pressure at HPC Outlet (psi)')]\n",
+    "\n",
+    "output = [gr.outputs.Textbox(type='number', label=\"Remaining Engine Cycles\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "56acae73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860/\n",
+      "Running on public URL: https://46768.gradio.app\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"900\"\n",
+       "            height=\"500\"\n",
+       "            src=\"https://46768.gradio.app\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x200411ef220>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<fastapi.applications.FastAPI at 0x20033b30fd0>,\n",
+       " 'http://127.0.0.1:7860/',\n",
+       " 'https://46768.gradio.app')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iface = gr.Interface(fn=predict, inputs=input, outputs=output, live=True, theme=\"dark-peach\")\n",
+    "iface.launch(debug=False, share=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "9cf77d9e31fba3236aefb4748d140888e596bc65dcef8da4aa710fb6056a88b0"
+  },
+  "kernelspec": {
+   "display_name": "ML",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/RUL.txt

@@ -0,0 +1,100 @@
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flagged/log.csv

@@ -0,0 +1,31 @@
+Engine ID,Corrected Engine Core Speed (rpm),Total Temperature at HPC Outlet (��R),Total Pressure at HPC Outlet (psi),Remaining Engine Cycles,timestamp
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