Upload CapstoneDeploymentTest.ipynb

CapstoneDeploymentTest.ipynb

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+{
+ "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
+}