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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 pickle
import streamlit as st
st.title('Repair Time Prediction')
#DLoading the ataset
#df = pd.read_csv('repair_time_sample_50k_modified2.csv')
#new_data = df
#df.drop(['SRU serial number','Date of Manufacture', 'Snag Description'], axis = 1, inplace=True)
# DATA from user
def user_report():
Aircraft_Type = st.sidebar.selectbox('Aircraft Type',("AH-64","UH-60","UH-63","UH-62","UH-61","AH-65"))
if Aircraft_Type=="AH-64":
Aircraft_Type=0
elif Aircraft_Type=="UH-60":
Aircraft_Type=2
elif Aircraft_Type=="UH-63":
Aircraft_Type=5
elif Aircraft_Type=="UH-62":
Aircraft_Type=4
elif Aircraft_Type=="UH-61":
Aircraft_Type=3
else:
Aircraft_Type=1
manufacturer = st.sidebar.selectbox("Manufacturer",
("JKL Company", "GHI Company","AGS 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 )
Issue_category= st.sidebar.selectbox("Issue Category",
("Display", "Unservicable","Bootup Problem","Engine Failure","Electrical Fault" ))
if Issue_category=='Display':
Issue_category=1
elif Issue_category=="Unservicable":
Issue_category=4
elif Issue_category=="Bootup Problem":
Issue_category=0
elif Issue_category=="Engine Failure":
Issue_category=3
else:
Issue_category=2
Snag_Severity = st.sidebar.selectbox("Snag Severity",
("Low", "Medium","High" ))
if Snag_Severity =='Low':
Snag_Severity=1
elif Snag_Severity=="Medium":
Snag_Severity =2
else:
Snag_Severity=0
Customer= st.sidebar.selectbox("Customer",
("IAF", "ARMY","NAVY" ))
if Customer =='IAF':
Customer=1
elif Customer=="ARMY":
Customer =0
else:
Customer=2
Technician_Skill_level= st.sidebar.selectbox("Technician Skill level",
("Expert", "Intermediate","Novice" ))
if Technician_Skill_level =='Expert':
Technician_Skill_level=0
elif Technician_Skill_level=="Intermediate":
Technician_Skill_level =1
else:
Technician_Skill_level=2
prior_maintainence = st.sidebar.selectbox('Prior Maintainence',("Regular","Irregular"))
if prior_maintainence =='Regular':
prior_maintainence=1
else:
prior_maintainence=0
Logistics_Time = st.sidebar.slider('Logistics Time (hr)', 2,21, 5 )
total_operating_hours = st.sidebar.slider('Total Operating Hours)', 50,2000, 500 )
operating_temperature = st.sidebar.slider('Operating Temperature', 10,25, 15 )
previous_number_of_repairs = st.sidebar.number_input('Enter the Previous Number of Repairs Undergone 0 to 3 )',min_value=0,max_value=3,step=1)
Power_Input_Voltage= st.sidebar.slider('Power Input Voltage (V)',100,133,115)
user_report_data = {
'Aircraft Type':Aircraft_Type,
'Manufacturer':manufacturer,
'Component_Age':component_age,
'Issue_category':Issue_category,
'Snag Severity': Snag_Severity,
'Customer':Customer,
'Technician Skill level':Technician_Skill_level,
'Prior Maintenance': prior_maintainence,
'Logistics Time (hr)':Logistics_Time,
'total_operating_hours':total_operating_hours,
'operating_temperature':operating_temperature,
'previous_number_of_repairs':previous_number_of_repairs,
'Power_Input_Voltage':Power_Input_Voltage
}
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 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 label_encoding(data):
le = LabelEncoder()
cat = data.select_dtypes(include='O').keys()
categ = list(cat)
data[categ] = data[categ].apply(le.fit_transform)
# X = data.loc[:,data.columns!= "Time required for repair (in hours)"]
# y = data['Time required for repair (in hours)']
# return X,y
return data
def prediction(df):
#X = df.loc[:,df.columns!= "Time required for repair (in hours)"]
#y = df['Time required for repair (in hours)']
#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_encoding(df)
#X_test_df = preprocess_dataset(df)
x_model = pickle.load(open('repair_time_model.pkl','rb'))
pred = x_model.predict(df)
#X_test['Actual_time_to_repair'] = y_test
#X_test['Predicted_time_to_repair'] = pred
#X_test.to_csv(r'/content/drive/MyDrive/Colab Notebooks/HAL/repair_time_prediction_results.csv')
#print(X_test.head())
return pred
y_pred = prediction(user_data)
if st.button("Predict"):
st.subheader(f"Time required to Repair the Component is {y_pred[0]} hours") |