Update app.py

app.py

@@ -28,14 +28,16 @@ def predict(alpha):
   ridge_reg = build_model(alpha)
   preds = ridge_reg.predict(X_test)
   fig = plt.figure()
-  plt.plot(X_test, y_test, "r-")
-  plt.plot(X_test, preds, "b--")
-  plt.title("Effect of regularization parameter on Ridge regression")
+  plt.plot(X_train, y_train, "g-", label="train data")
+  plt.plot(X_test, y_test, "r-", label="test data")
+  plt.plot(X_test, preds, "b--", label="regularized model")
   plt.ylabel("Y")
   plt.xlabel("X")
   return plt
 
-inputs = gr.Number()
+inputs = gr.Slider(-10, 20, label='alpha', default=1)
 outputs = gr.Plot()
-gr.Interface(fn = predict, inputs = inputs, outputs = outputs).launch()
+title = "Effect of regularization using Ridge regression"
+description = "Alpha is the regularization parameter which basically restricts model. The idea is that using regularization the model even if performs poorly on the training data, it would provide a better fit for generalizing data. Try out yourself by increasing or decreasing the value of alpha."
+gr.Interface(fn = predict, inputs = inputs, outputs = outputs, title = title, description = description).launch()