# -*- coding: utf-8 -*-
"""Gradio-regression.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1qmfhcPafAIfczazACroyAYyRohdQbklK
"""
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
np.random.seed(2)

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.1, random_state=42)

def build_model(alpha):
  r_reg = Ridge(alpha=alpha)
  r_reg.fit(X_train, y_train)
  return r_reg

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.ylabel("Y")
  plt.xlabel("X")
  return plt

inputs = gr.Number()
outputs = gr.Plot()
gr.Interface(fn = predict, inputs = inputs, outputs = outputs).launch()