Update app.py

app.py

@@ -1,4 +1,63 @@
+
+# import required libraries
+
+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 
+import warnings
+warnings.filterwarnings('ignore')
+
+
+
+st.title("Prection of Maimum Number of Repais")
+
+import pandas as pd
+import numpy as np
+import pickle
 
+# load the saved model using pickle
+with open('max_repair_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+    
+# Load the saved manufacturer label encoder object using pickle
+with open('manufacturer_le.pkl', 'rb') as file1:
+    le = pickle.load(file1)
+    
 
-st.title("Prection of Maimum Number of Repais")
+# define the prediction function
+def predict_max_number_of_repairs(manufacturer, component_age, total_operating_hours, operating_temperature, humidity, vibration_level, pressure, power_input_voltage, previous_number_of_repairs, load_factor, engine_speed, oil_temperature):
+    
+    # encode the manufacturer using the loaded LabelEncoder object
+    manufacturer_encoded = le.transform([manufacturer])[0]
+    
+    # create a DataFrame with the input variables
+    input_data = pd.DataFrame({'Manufacturer': [manufacturer_encoded], 
+                               'Component_Age': [component_age], 
+                               'Total_Operating_Hours': [total_operating_hours], 
+                               'Operating_Temperature': [operating_temperature], 
+                               'Humidity': [humidity], 
+                               'Vibration_Level': [vibration_level], 
+                               'Pressure': [pressure], 
+                               'Power_Input_Voltage': [power_input_voltage], 
+                               'Previous_number_of_repairs': [previous_number_of_repairs], 
+                               'Load_Factor': [load_factor], 
+                               'Engine_Speed': [engine_speed], 
+                               'Oil_Temperature': [oil_temperature]})
+    
+    # make the prediction using the loaded model and input data
+    predicted_max_number_of_repairs = model.predict(input_data)
+    
+    # return the predicted max number of repairs as output
+    return np.round(predicted_max_number_of_repairs[0])
+# Function calling
+print(predict_max_number_of_repairs('ABC Company',100.00,1135,70.0,65,3.43,29.90,120,4,0.59,7398,170))