Update app.py

app.py

@@ -8,14 +8,11 @@ from sklearn.metrics import mean_squared_error, r2_score
 def perform_linear_regression(X, Y):
     X = np.array(X).reshape(-1, 1)  # Reshape for sklearn
     Y = np.array(Y)
-    
+
     model = LinearRegression()
     model.fit(X, Y)
-    
-    slope = model.coef_[0]
-    intercept = model.intercept_
-    
-    return model, slope, intercept
+
+    return model
 
 # Streamlit interface
 st.title("Linear Regression Visualization")
@@ -42,10 +39,10 @@ except ValueError:
 
 # Proceed only if both vectors are valid and have 3 elements each
 if len(x_values) == 3 and len(y_values) == 3:
-    model, slope, intercept = perform_linear_regression(x_values, y_values)
-    
-    slope = round(slope, 2)
-    intercept = round(intercept, 2)
+    model = perform_linear_regression(x_values, y_values)
+
+    slope = round(model.coef_[0], 2)
+    intercept = round(model.intercept_, 2)
 
     st.write(f"Slope: {slope}")
     st.write(f"Intercept: {intercept}")
@@ -54,31 +51,34 @@ if len(x_values) == 3 and len(y_values) == 3:
     with col2:
         plt.figure(figsize=(8, 6))
         plt.scatter(x_values, y_values, color='red', label='Data Points')
-        
+
         x_range = np.linspace(min(x_values), max(x_values), 100)
         y_range = slope * x_range + intercept
         plt.plot(x_range, y_range, color='blue', label='Regression Line')
-        
+
         plt.xlabel("X")
         plt.ylabel("Y")
         plt.legend()
         plt.title("Linear Regression Visualization")
-        
+
         st.pyplot(plt)
 
     # Prompt for a test value of X and predict its corresponding Y
     test_x_input = st.number_input("Enter a test value for X", value=0.0)
-    
+
     if test_x_input:
         predicted_y = model.predict(np.array([[test_x_input]]))[0]
         st.write(f"Predicted Y for X = {test_x_input}: {predicted_y}")
-    
+
     # Performance metrics
+    X = np.array(x_values).reshape(-1, 1)
+    Y = np.array(y_values)
+
     y_predicted = model.predict(X)
     mse = mean_squared_error(Y, y_predicted)
-    #r2 = r2_score(Y, y_predicted)
+    r2 = r2_score(Y, y_predicted)
 
     st.write(f"Mean Squared Error: {mse}")
-    #st.write(f"R^2 Score: {r2}")
+    st.write(f"R^2 Score: {r2}")
 else:
     st.write("Please provide valid inputs with 3 values each.")