Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from datetime import datetime
+from datetime import timedelta
+from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import r2_score
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import StandardScaler 
+import streamlit as st 
+
+
+st.title("Next Failure Prediction")
+# Loading Dataset
+df1 = pd.read_csv(r'Final_Next_failure_Dataset.csv')
+
+
+# replace values in the Manufacturer column with company names
+
+replace_dict1 = {1: 'ABC Company', 2: 'DEF Company', 3: 'GHI Company', 4: 'JKL Company', 5: 'XYZ Company'}
+df1['Manufacturer'] = df1['Manufacturer'].replace(replace_dict1)
+
+
+# replace values in the Last_Maintenance_Type column again
+
+replace_dict2 = {1: 'Corrective', 2: 'Preventive'}
+df1['Last_Maintenance_Type'] = df1['Last_Maintenance_Type'].replace(replace_dict2)
+
+# replace values in the Prior_Maintenance column again
+
+replace_dict3 = {1: 'Irregular', 2: 'Regular'}
+df1['Prior_Maintenance'] = df1['Prior_Maintenance'].replace(replace_dict3)
+
+# replace values in the Repair_Type column again
+
+replace_dict4 = {1: 'Hardware', 2: 'Software'}
+df1['Repair_Type'] = df1['Repair_Type'].replace(replace_dict4)
+
+df = df1.copy()
+
+# For Manufacturer
+
+le_manu = LabelEncoder()
+df['Manufacturer'] = le_manu.fit_transform(df['Manufacturer'])
+
+
+# For Last_Maintenance_Type
+
+le_last = LabelEncoder()
+df['Last_Maintenance_Type'] = le_last.fit_transform(df['Last_Maintenance_Type'])
+
+# For Prior_Maintenance
+
+le_prior = LabelEncoder()
+df['Prior_Maintenance'] = le_prior.fit_transform(df['Prior_Maintenance'])
+
+# For Repair_Type
+
+le_repair = LabelEncoder()
+df['Repair_Type'] = le_repair.fit_transform(df['Repair_Type'])
+
+#Splitting the data train ans test data 
+X = df.drop('Time_to_Failure_(hours)', axis = 1)
+y = df['Time_to_Failure_(hours)']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 0)
+
+# Train Random Forest Regression model
+
+model = RandomForestRegressor(random_state = 0)
+model.fit(X_train, y_train)
+
+
+# Make predictions on train data
+
+y_pred_train = model.predict(X_train)
+
+# DATA from user
+def user_report():
+  manufacturer = st.sidebar.selectbox("Manufacturer",
+        ("JKL Company", "GHI Company","DEF Company","ABC Company","XYZ Company" ))
+  if manufacturer=='JKL Company':
+      manufacturer=3
+  elif manufacturer=="GHI Company":
+      manufacturer=2
+  elif manufacturer=="DEF Company":
+      manufacturer=1
+  elif manufacturer=="ABC Company":
+      manufacturer =0
+  else:
+      manufacturer=4
+  total_operating_hours = st.sidebar.slider('Total Operating Hours)', 1000,2500, 1500 )
+  Usage_Intensity = st.sidebar.slider("Usage_Intensity(hous/day)",1,10,4)  
+  Last_Maintenance_Type = st.sidebar.selectbox("Last Maintainece Type",("Corrective","Preventive"))
+  if Last_Maintenance_Type =='Corrective':
+      Last_Maintenance_Type=0
+  else:
+      Last_Maintenance_Type=1
+  Prior_Maintenance = st.sidebar.selectbox("Prior Maintainece",("Regular","Irregular"))
+  if Prior_Maintenance =='Regular':
+      Prior_Maintenance=1
+  else:
+      Prior_Maintenance=0
+              
+  Average_Temperature= st.sidebar.slider('Average Temperature', 20,40, 35 )  
+  humidity = st.sidebar.slider('Humidity', 52,70, 55 ) 
+  Vibration_Level = st.sidebar.slider('Vibration Level', 2,4, 2 )
+  Pressure = st.sidebar.slider('Pressure', 28,32, 30 ) 
+  Power_Input_Voltage= st.sidebar.slider('Power Input Voltage (V)',105,120,115)
+  Repair_Type = st.sidebar.selectbox("Repair Type",("Hardware","Software"))
+  if Repair_Type =='Software':
+      Repair_Type=1
+  else:
+      Repair_Type=0
+  load_factor = st.sidebar.number_input('Enter the Load Factor (any number between 0 to 1 )',min_value=0.0,max_value=1.0,step=0.1)     
+  engine_speed=st.sidebar.slider('Engine Speed',7000,8000,7800)
+  Oil_Temperature=st.sidebar.slider('Oil Temperature',170,185,172)  
+    
+  
+  user_report_data = {
+       'Manufacturer': manufacturer, 
+       'Total_Operating_Hours': total_operating_hours, 
+       'Usage_Intensity_(hours/day)': Usage_Intensity , 
+       'Last_Maintenance_Type': Last_Maintenance_Type,
+       "Prior_Maintenance":Prior_Maintenance,
+       'Average_Temperature':Average_Temperature,      
+       'Humidity': humidity, 
+       'Vibration_Level': Vibration_Level, 
+       'Pressure': Pressure, 
+       'Power_Input_Voltage': Power_Input_Voltage, 
+       'Repair_Type': Repair_Type , 
+       'Load_Factor': load_factor, 
+       'Engine_Speed': engine_speed, 
+       'Oil_Temperature':Oil_Temperature      
+  }
+  report_data = pd.DataFrame(user_report_data, index=[0])
+  
+  return report_data    
+    
+#Customer Data
+user_data = user_report()
+st.subheader("Component Details")
+st.write(user_data)
+
+
+# define the prediction function
+def prediction(user_data):
+
+    predicted_max_number_of_repairs = model.predict(user_data)
+    
+    # return the predicted max number of repairs as output
+    return np.round(predicted_max_number_of_repairs[0])
+# Function calling
+y_pred = prediction(user_data)
+st.write("Click here to see the Predictions")
+if st.button("Predict"):
+    st.subheader(f"Next Failure is {y_pred} hours ")