import numpy as np
import pandas as pd
import gradio as gr

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"
  

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




demo = gr.Interface(
    fn=greet,
    inputs=['number','number','number','number','number','number'],
    outputs=['text','number'],
    title="BARA SHIGRI",
) 
    
demo.launch()