Create app.py

app.py

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+
+### (1) Generate simulated data
+
+import numpy.random as rnd
+
+import gradio as gr
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+
+
+np.random.seed(42)
+
+m = 100
+X1 = 6 * np.random.rand(m,1) - 3
+
+X2 = X1 ** 2
+
+y = X1 +  0.5 * X2 + 2 + np.random.randn(m,1)
+
+m = 20
+X1_test = 6 * np.random.rand(m,1) - 3
+X2_test = X1_test ** 2
+y_test = X1_test + 0.5 * X2_test + 2 + np.random.randn(m,1)
+
+
+import pandas as pd
+
+data = pd.DataFrame({'y': y.flatten(), 'X1': X1.flatten(), 'X2': X2.flatten()}, columns=['y', 'X1', 'X2'],)
+data
+
+
+
+
+def draw_polynomial(degree=2):
+    fig = plt.figure(figsize=(12,20))
+
+    plt.subplot(2,1,1)
+
+    from sklearn.preprocessing import PolynomialFeatures
+    poly_features = PolynomialFeatures(degree=degree, include_bias=False)
+    X_poly = poly_features.fit_transform(X1)
+    
+    from sklearn.linear_model import LinearRegression
+    lin_reg = LinearRegression()
+    lin_reg.fit(X_poly, y)
+    lin_reg.intercept_, lin_reg.coef_
+    
+    ## Visualized fitted quadratic line on training/testing data
+    X_new=np.linspace(-3, 3, 100).reshape(100, 1)
+    X_new_poly = poly_features.transform(X_new)
+    y_new = lin_reg.predict(X_new_poly)
+    plt.plot(X1, y, "b.")
+    plt.plot(X1_test, y_test, "g.", markersize=12)
+    plt.plot(X_new, y_new, "r-", linewidth=2, label="Predictions")
+    plt.xlabel("$x_1$", fontsize=18)
+    plt.ylabel("$y$", rotation=0, fontsize=18)
+    plt.legend(loc="upper left", fontsize=14)
+    plt.axis([-3, 3, 0, 10])
+
+    plt.subplot(2,1,2)
+    
+    plt.plot(polynomial_regression['lin_reg'].coef_[0])
+    plt.xlabel("Parameters", fontsize=14)
+    plt.ylabel("Values", fontsize=14)
+
+    #plt.show()
+    fig.tight_layout()
+    plt.savefig('plot_line.png', dpi=300)
+    return 'plot_line.png'
+
+
+#### Define input component
+input_degree = gr.inputs.Slider(1, 50, step=1, default=2, label='Degree of Polynomial Regression')
+
+#### Define output component
+output_plot1 = gr.outputs.Image(label="Regression plot")
+
+
+
+### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
+interface = gr.Interface(fn=gradient_descent, 
+                         inputs=[input_degree], 
+                         outputs=[output_plot1],
+                         title="CSCI4750/5750: Polynomial Regression models \n (Model Complexity)", 
+                         theme = 'huggingface',
+                         layout = 'vertical'
+                         )
+                         
+interface.launch(debug=True)