# 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("Prediction of Maximum Number of Repairs")
st.sidebar.header('Enter the Components Details here')
st.write("""This model helps to know the probable maximum number of times a component can be repaired.
After which, we can straight away replace it with a new component""")
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)
    
# 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
  component_age = st.sidebar.slider('Component Age (in hours)', 100,250, 300 )
  total_operating_hours = st.sidebar.slider('Total Operating Hours)', 400,1500, 500 )
  operating_temperature = st.sidebar.slider('Operating Temperature', 70,80, 75 )  
  humidity = st.sidebar.slider('Humidity', 50,70, 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)
  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)  
  load_factor = st.sidebar.slider('Load Factor',3,10,4)
  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, 
       '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      
  }
  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 predict_max_number_of_repairs(user_data):
    
    # encode the manufacturer using the loaded LabelEncoder object
    #manufacturer_encoded = le.transform([manufacturer])[0]
    
    
    
    # make the prediction using the loaded model and input 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 = int(predict_max_number_of_repairs(user_data))
st.write("Click here to see the Predictions")
if st.button("Predict"):
    st.subheader(f"Maximun Number of Repairs is {y_pred} ")