Create app.py

app.py

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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","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_hour,
+      '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(X_test_encoded)
+  x_model = pickle.load(open('repair_time_model.pkl','rb'))
+  pred = x_model.predict(X_test_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 Repairs the Component is {y_pred} in hours")