Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import numpy as np
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
+from sklearn.metrics import mean_squared_error, r2_score
 
 # Function for linear regression
 def perform_linear_regression(X, Y):
@@ -70,7 +71,14 @@ if len(x_values) == 3 and len(y_values) == 3:
     
     if test_x_input:
         predicted_y = model.predict(np.array([[test_x_input]]))[0]
-        predicted_y = round(predicted_y, 2)
-        st.write(f"Predicted Y for X = {test_x_input} is given as {predicted_y} by above linear regression model")
+        st.write(f"Predicted Y for X = {test_x_input}: {predicted_y}")
+    
+    # Performance metrics
+    y_predicted = model.predict(X)
+    mse = mean_squared_error(Y, y_predicted)
+    #r2 = r2_score(Y, y_predicted)
+
+    st.write(f"Mean Squared Error: {mse}")
+    #st.write(f"R^2 Score: {r2}")
 else:
     st.write("Please provide valid inputs with 3 values each.")