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 ")