app.py

import gradio as gr
import numpy as np
import pandas as pd
def greet(year,co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission):
    
  #1996
  
  #data collection
  data1=pd.read_excel("FINAL_DATASET.xlsx")
  df1 = data1.drop(['YEAR'], axis=1)



  #data indexing
  x=df1.iloc[:,1:].values
  y=df1.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred1=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total1="2.29209688*(x1)+(-17.24834114)*(x2)+(-34.46449984)*(x3)+441.88734541*(x4)+(-10.5704468)*(x5)+3032.3276611889232"
  

  #1997

  #data collection
  data2=pd.read_excel("ans1 (1).xlsx")
  df2 = data2.drop(['YEAR '], axis=1)



  #data indexing
  x=df2.iloc[:,1:].values
  y=df2.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred2=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total2="1.87191609*(x1)+19.64115875*(x2)+91.64048224*(x3)+188.38350818*(x4)+23.55498894*(x5)-10954.252919457198"
  


   #1998

  #data collection
  data3=pd.read_excel("ans2.xlsx")
  df3 = data3.drop([' YEAR '], axis=1)



  #data indexing
  x=df3.iloc[:,1:].values
  y=df3.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred3=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total3="-16.16933055*(x1)+222.22199705*(x2)+137.12332335*(x3)+325.31073157*(x4)+(-123.63496668)*(x5)+56972.685015326366"
  


  #1999

  #data collection
  data4=pd.read_excel("ans3.xlsx")
  df4 = data4.drop([' YEAR '], axis=1)



  #data indexing
  x=df4.iloc[:,1:].values
  y=df4.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred4=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total4="24.45036879*(x1)+(-30.15985323)*(x2)+40.89603753*(x3)+102.95011027*(x4)+(-26.35323684)*(x5)+7934.309705068432"
  


  #2000

  #data collection
  data5=pd.read_excel("ans4.xlsx")
  df5 = data5.drop([' YEAR '], axis=1)



  #data indexing
  x=df5.iloc[:,1:].values
  y=df5.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred5=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total5="9.64446417*(x1)+1536.50170104*(x2)+(-43.51829088)*(x3)+(-209.86341104)*(x4)+(-56.82344007)*(x5)+21672.006533155447"
  


    #2001

  #data collection
  data6=pd.read_excel("ans5.xlsx")
  df6 = data6.drop([' YEAR '], axis=1)



  #data indexing
  x=df6.iloc[:,1:].values
  y=df6.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred6=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total6="(-17.95980305)*(x1)+397.29027184*(x2)+332.85116421*(x3)+176.63505073*(x4)+(-100.69005777)*(x5)+47882.75497380103"
 

    #2002

  #data collection
  data7=pd.read_excel("ans6.xlsx")
  df7 = data7.drop([' YEAR '], axis=1)



  #data indexing
  x=df7.iloc[:,1:].values
  y=df7.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred7=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total7="4.08573322*(x1)+531.87792204*(x2)+(-17.3614085 )*(x3)+(-11.17919737)*(x4)+(-53.48796076)*(x5)+22953.88111229325"
  

    #2003

  #data collection
  data8=pd.read_excel("ans7.xlsx")
  df8 = data8.drop([' YEAR '], axis=1)



  #data indexing
  x=df8.iloc[:,1:].values
  y=df8.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred8=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total8="31.82512443*(x1)+(-521.96868383 )*(x2)+(-43.51829088)*(x3)+ 205.27514768 *(x4)+(-97.91577198)*(x5)+37973.451433772294"
  


    #2004

  #data collection
  data9=pd.read_excel("ans8.xlsx")
  df9 = data9.drop([' YEAR '], axis=1)



  #data indexing
  x=df9.iloc[:,1:].values
  y=df9.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred9=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total9="9.64446417*(x1)+1536.50170104*(x2)+(-43.51829088)*(x3)+(-209.86341104)*(x4)+(-56.82344007)*(x5)+21672.006533155447"
  


    #2005

  #data collection
  data10=pd.read_excel("ans9.xlsx")
  df10 = data10.drop([' YEAR '], axis=1)



  #data indexing
  x=df10.iloc[:,1:].values
  y=df10.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred10=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total10="(-46.41395388)*(x1)+27.19076539*(x2)+(442.44336049)*(x3)+(-205.61881527)*(x4)+120.39426307*(x5)-46289.48823133327"
  


    #2006

  #data collection
  data11=pd.read_excel("ans10.xlsx")
  df11 = data11.drop([' YEAR '], axis=1)



  #data indexing
  x=df11.iloc[:,1:].values
  y=df11.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred11=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total11="(-15.45736104)*(x1)+23.92398419*(x2)+334.30252317*(x3)+151.55678804*(x4)+(-66.42769537)*(x5)+29294.014037250927"
  



    #2007

  #data collection
  data12=pd.read_excel("ans11.xlsx")
  df12 = data12.drop([' YEAR '], axis=1)



  #data indexing
  x=df12.iloc[:,1:].values
  y=df12.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred12=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total12="33.41323832*(x1)+(-36.18735569)*(x2)+768.11444325*(x3)+(-182.42626044 )*(x4)+(14.70116631)*(x5)-6967.764713347897"
  


    #2008

  #data collection
  data13=pd.read_excel("ans12.xlsx")
  df13 = data13.drop([' YEAR '], axis=1)



  #data indexing
  x=df13.iloc[:,1:].values
  y=df13.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred13=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total13="180.34683409 *(x1)+49.48628012*(x2)+152.71729516*(x3)+( -174.89679207)*(x4)+(-144.40854904)*(x5)+30420.505686819404"
  

    


    #2009

  #data collection
  data14=pd.read_excel("ans13.xlsx")
  df14 = data14.drop([' YEAR '], axis=1)



  #data indexing
  x=df14.iloc[:,1:].values
  y=df14.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred14=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total14="17.11355138 *(x1)+37.59837451*(x2)+156.43469383*(x3)+(-104.8362236)*(x4)+81.10973597*(x5)-38919.678559060834"
  



    #2010

  #data collection
  data15=pd.read_excel("ans14.xlsx")
  df15 = data15.drop([' YEAR '], axis=1)



  #data indexing
  x=df15.iloc[:,1:].values
  y=df15.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred15=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total15="39.06418699 *(x1)+148.53455807*(x2)+14.69213499 *(x3)+107.43795246*(x4)+(-207.77185028)*(x5)+82358.63651384937"
  



    #2011

  #data collection
  data16=pd.read_excel("ans15.xlsx")
  df16 = data16.drop([' YEAR '], axis=1)



  #data indexing
  x=df16.iloc[:,1:].values
  y=df16.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred16=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total16="36.2551509 *(x1)+-21.16118114*(x2)+372.06856269*(x3)+(-59.04384028)*(x4)+(-49.61395171)*(x5)+18259.681897588325"
  



    #2012

  #data collection
  data17=pd.read_excel("ans16.xlsx")
  df17 = data17.drop([' YEAR '], axis=1)



  #data indexing
  x=df17.iloc[:,1:].values
  y=df17.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred17=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total17="76.15862868 *(x1)+24.66304806*(x2)+(-31.1753211)*(x3)+(-281.13550722 )*(x4)+48.76763872*(x5)-27641.15357666507"
  


    #2013

  #data collection
  data18=pd.read_excel("ans17.xlsx")
  df18 = data18.drop([' YEAR '], axis=1)



  #data indexing
  x=df18.iloc[:,1:].values
  y=df18.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred18=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total18="138.94519275 *(x1)+19.41784298*(x2)+160.13405515*(x3)+1190.40134987*(x4)+(-787.72926112)*(x5)+340350.32984524494"
  




    #2014

  #data collection
  data19=pd.read_excel("ans18.xlsx")
  df19 = data19.drop([' YEAR '], axis=1)



  #data indexing
  x=df19.iloc[:,1:].values
  y=df19.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred19=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total19="83.98184027*(x1)+61.59628945*(x2)+740.33672736*(x3)+(-347.39343539)*(x4)+(-293.6388187)*(x5)+121547.59923111903"
  




    #2015

  #data collection
  data20=pd.read_excel("ans19.xlsx")
  df20 = data20.drop([' YEAR '], axis=1)



  #data indexing
  x=df20.iloc[:,1:].values
  y=df20.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred20=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total20="25.74397202*(x1)+(-109.5936775)*(x2)+293.36826631*(x3)+(-52.97554351)*(x4)+178.24908664*(x5)-80332.13002824014"
  






    #2016

  #data collection
  data21=pd.read_excel("ans20.xlsx")
  df21 = data21.drop([' YEAR '], axis=1)



  #data indexing
  x=df21.iloc[:,1:].values
  y=df21.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred21=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total21="-9.33709575 *(x1)+(-60.54283141)*(x2)+1291.89291784*(x3)+112.70137053*(x4)+167.06117048*(x5)-76365.90014799789"
  




    #2017

  #data collection
  data22=pd.read_excel("ans21.xlsx")
  df22 = data22.drop([' YEAR '], axis=1)



  #data indexing
  x=df22.iloc[:,1:].values
  y=df22.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred22=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total22="-12.58553956 *(x1)+54.81099258*(x2)+224.41124874*(x3)+437.35226861*(x4)+(-160.78658794)*(x5)+68323.07737183299"
  




    #2018

  #data collection
  data23=pd.read_excel("ans22.xlsx")
  df23 = data23.drop([' YEAR '], axis=1)



  #data indexing
  x=df23.iloc[:,1:].values
  y=df23.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred23=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total23="73.20314723*(x1)+158.24671048*(x2)+(-3876.80695302)*(x3)+356.25236863*(x4)+(-195.73184137)*(x5)+85757.9509512224"
  



    #2019

  #data collection
  data24=pd.read_excel("ans23.xlsx")
  df24 = data24.drop([' YEAR '], axis=1)



  #data indexing
  x=df24.iloc[:,1:].values
  y=df24.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred24=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total24="104.06131346*(x1)+110.40576115*(x2)+(-3143.30201973)*(x3)+(-466.5687285)*(x4)+(-40.30732688)*(x5)+6946.199087391373"
  

  


    #2020

  #data collection
  data25=pd.read_excel("ans24.xlsx")
  df25 = data25.drop([' YEAR '], axis=1)



  #data indexing
  x=df25.iloc[:,1:].values
  y=df25.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred25=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total25="22.78813682*(x1)+46.1536507*(x2)+78.00814512*(x3)+(-71.38031119)*(x4)+(-37.57839411)*(x5)+12559.184605195129"
  




    #2021

  #data collection
  data26=pd.read_excel("ans25.xlsx")
  df26 = data26.drop([' YEAR '], axis=1)



  #data indexing
  x=df26.iloc[:,1:].values
  y=df26.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred26=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total26="63.70545758*(x1)+9.57432502*(x2)+1734.12898357*(x3)+(-230.53815238)*(x4)+93.1299683*(x5)-51860.81441391745"
  




    #2022

  #data collection
  data27=pd.read_excel("ans26.xlsx")
  df27 = data27.drop([' YEAR '], axis=1)



  #data indexing
  x=df27.iloc[:,1:].values
  y=df27.iloc[:,0].values
  np.reshape(y,(-1,1))

  #split the dataset
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(
      x, y, test_size=0.33, random_state=42)


  #traing the dataset
  from sklearn.linear_model import LinearRegression

  reg = LinearRegression().fit(X_train, y_train)
  

  y_pred27=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])



  

  #Equation
  total27="15.98972327*(x1)+5568.67299429*(x2)+79.28661735*(x3)+16.79333316*(x4)+(-87.10169494)*(x5)+40155.32700035415"
  



  


  #app section
  if(year==1996):
    return total1,y_pred1

  elif(year==1997):
    return total2,y_pred2

  elif(year==1998):
    return total3,y_pred3

  elif(year==1999):
    return total4,y_pred4

  elif(year==2000):
    return total5,y_pred5

  elif(year==2001):
    return total6,y_pred6

  elif(year==2002):
    return total7,y_pred7
  
  elif(year==2003):
    return total8,y_pred8

  elif(year==2004):
    return total9,y_pred9

  elif(year==2005):
    return total10,y_pred10

  elif(year==2006):
    return total11,y_pred11

  elif(year==2007):
    return total12,y_pred12

  elif(year==2008):
    return total13,y_pred13

  elif(year==2009):
    return total14,y_pred14

  elif(year==2010):
    return total15,y_pred15

  elif(year==2011):
    return total16,y_pred16

  elif(year==2012):
    return total17,y_pred17

  elif(year==2013):
    return total18,y_pred18

  elif(year==2014):
    return total19,y_pred19

  elif(year==2015):
    return total20,y_pred20

  elif(year==2016):
    return total21,y_pred21

  elif(year==2017):
    return total22,y_pred22

  elif(year==2018):
    return total23,y_pred23

  elif(year==2019):
    return total24,y_pred24

  elif(year==2020):
    return total25,y_pred25

  elif(year==2021):
    return total26,y_pred26

  elif(year==2022):
    return total27,y_pred27
      
  else:
      return "no",0


 





demo = gr.Interface(
  fn=greet,
  inputs=["number","number","number","number","number","number"],
  outputs=["text","number"],
  title="BARA SHIGRI",
  css="div {background-image: url('https://drive.google.com/uc?export=view&id=1o4Q6O7LAFTpejs4zwOo6X-BYfrjjyTVr');background-size: 2000px 2000px;}",
  description=
  "Bara Shigri feeds the Chandra River which after its confluence at Tandi with the Bhaga River is known as Chandrabhaga or Chenab."
  "According to Hugh Whistler’s 1924 writing, Shigri is applied par-excellence to one particular glacier that emerges from the mountains on the left bank of the Chenab. It is said to be several miles long, and the snout reaches right down to the river, lying athwart the customary road from Kulu to Spiti... In 1836 this glacier dammed the Chenab River, causing the formation of a large lake, which eventually broke loose and carried devastation down the valley."
  "Across the Bara Shigri is another glacier known as Chhota Shigri. It is, as the name suggests, a comparatively smaller glacier.",
  description_font_color="Black"

    
)
demo.launch()