Upload app.py

app.py

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+# -*- 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 seaborn as sns
+import gradio as gr
+from sklearn.linear_model import Ridge, LinearRegression
+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()
+