import tensorflow as tf from tensorflow import keras import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.model_selection import train_test_split from sklearn import preprocessing import seaborn as sns from sklearn.preprocessing import LabelEncoder import streamlit as st st.title("Rouge Component Model") #Reading Dataset df = pd.read_csv('identify_rogue_50K_ALL.csv') print("Dataset Size:",df.shape) st.sidebar.header('Enter the Components Details here') # Dropping the SRU serial number df.drop(['SRU serial number','Date of Manufacture','Last Maintenance Date','date of last failure'], axis = 1, inplace=True) # DATA from user def user_report(): manufacturer = st.sidebar.selectbox("Manufacturer", ("JKL Company", "GHI Company","AGS Company","ABC Company","ABC Company","XYZ Company" )) if manufacturer=='JKL Company': manufacturer=3 elif manufacturer=="GHI Company": manufacturer=2 elif manufacturer=="AGS Company": manufacturer=1 elif manufacturer=="ABC Company": manufacturer =0 else: manufacturer=4 component_age = st.sidebar.slider('Component Age (in hours)', 500,2000, 600 ) total_operating_hours = st.sidebar.slider('Total Operating Hours)', 50,2000, 500 ) usage_intensity = st.sidebar.slider('Usage Intensity hours/day', 0,9, 5 ) last_maintance_type = st.sidebar.selectbox('Last Mantainence Type', ("Preventive","Corrective") ) if last_maintance_type=="Preventive": last_maintance_type=1 else: last_maintance_type=0 previous_number_of_repairs = st.sidebar.number_input('Enter the Previous Number of Repairs Undergone 0 to 5 )',min_value=0,max_value=5,step=1) operating_temperature = st.sidebar.slider('Operating Temperature', 10,25, 15 ) humidity = st.sidebar.slider('Humidity', 20,105, 25 ) Vibration_Level = st.sidebar.slider('Vibration Level', 2,7, 2 ) Pressure = st.sidebar.slider('Pressure', 200,550, 250 ) Power_Input_Voltage= st.sidebar.slider('Power Input Voltage (V)',100,133,115) repair_type = st.sidebar.selectbox('Repair Type', ("Hardware","Software") ) if repair_type=='Hardware': repair_type=0 else: repair_type=1 number_of_inspection = st.sidebar.selectbox('Number of Inspections',('1','2')) if number_of_inspection=='1': number_of_inspection=1 else: number_of_inspection=2 number_of_inspection_6months = st.sidebar.selectbox('Number of Inspections in last 6 Months',('0','1')) if number_of_inspection_6months=='0': number_of_inspection_6months=0 else: number_of_inspection_6months=1 prior_maintainence = st.sidebar.selectbox('Prior Maintainence',("Regular","Irregular")) if prior_maintainence =='Regular': prior_maintainence=1 else: prior_maintainence=0 user_report_data = { 'Manufacturer':manufacturer, 'Component_Age':component_age, 'Total Operating Hours':total_operating_hours, 'Usage Intensity (hours/day)':usage_intensity, 'Last Maintenance Type': last_maintance_type, 'Previous number of repairs':previous_number_of_repairs, 'Operating Temperature':operating_temperature, 'Humidity': humidity, 'Vibration Level':Vibration_Level, 'Pressure':Pressure, 'Power Input Voltage (V)':Power_Input_Voltage, 'repair type':repair_type , 'total number of inspection':number_of_inspection, 'No. of Inspections in Last 6 Months':number_of_inspection_6months, 'Prior Maintenance':prior_maintainence } report_data = pd.DataFrame(user_report_data, index=[0]) return report_data #Customer Data user_data = user_report() st.header("Component Details") st.write(user_data) def label_encoder(df): le = LabelEncoder() cat = df.select_dtypes(include='O').keys() categ = list(cat) df[categ] = df[categ].apply(le.fit_transform) return df def preprocess_dataset(X): x = X.values #returns a numpy array min_max_scaler = preprocessing.MinMaxScaler() x_scaled = min_max_scaler.fit_transform(x) X_df = pd.DataFrame(x_scaled) return X_df def prediction(df): #X = df.loc[:,df.columns!= "Rogue LRU/SRU (Target)"] #y = df["Rogue LRU/SRU (Target)"] #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) #print(X_train.shape) #print(X_test.shape) X_test_encoded = label_encoder(df) X_test_df = preprocess_dataset(X_test_encoded) x_model = loaded_model = tf.keras.models.load_model('my_model') y_pred = x_model.predict(X_test_df) #predicition = [] #for i in list(y_pred): if y_pred ==0: st.write('Rouge Component is Good') else: st.write('Rouge Component is not good') #X_test['Actual_time_to_repair'] = y_test #X_test['Predicted_time_to_repair'] = predicition # X_test.to_csv(r'/content/drive/MyDrive/Colab Notebooks/HAL/rogue_test_data.csv') #print(X_test.head()) prediction(user_data)