Update app.py

app.py

@@ -2,6 +2,32 @@ 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("/content/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"
+  
     data=pd.read_excel("bara shigiri - Copy.xlsx")
     x=data.iloc[:,1:].values
     y=data.iloc[:,0].values