import gradio as gr
import pandas as pd
import numpy as np
def greet(year,co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission):
    data1=pd.read_excel("FINAL_DATASET.xlsx")
    df1 = data1.drop(['YEAR'], axis=1)
    x=df1.iloc[:,1:].values
    y=df1.iloc[:,0].values
    np.reshape(y,(-1,1))


    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)
    from sklearn.linear_model import LinearRegression
    reg = LinearRegression().fit(X_train, y_train)
    #Equation
    total1="2.29209688*(x1)+(-17.24834114)*(x2)+(-34.46449984)*(x3)+441.88734541*(x4)+(-10.5704468)*(x5)+3032.3276611889232"
    eq1=2.29209688*(co2_emission)+(-17.24834114)*(No2_emission)+(-34.46449984)*(so2_emission)+441.88734541*(Global_Warming)+(-10.5704468)*(Methane_emission)+3032.3276611889232
    if(year==1996):
        return total1,eq1

    

    
    
    
    
    
  
  



  

  
 




 





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(inline=False)