Delete app.py

app.py

@@ -1,145 +0,0 @@
-# Code source: Gaël Varoquaux
-# License: BSD 3 clause
-
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn import svm
-import gradio as gr
-plt.switch_backend("agg")
-
-kernels = ["linear", "poly", "rbf"]
-
-font1 = {'family':'DejaVu Sans','size':20}
-
-cmaps = {'Set1': plt.cm.Set1, 'Set2': plt.cm.Set2, 'Set3': plt.cm.Set3, 
-         'tab10': plt.cm.tab10, 'tab20': plt.cm.tab20}
-
-# fit the model
-def clf_kernel(kernel, cmap, dpi = 300, use_random = False):    
-    
-    #example data
-    if use_random == False:
-        X = np.c_[
-        (0.4, -0.7),
-        (-1.5, -1),
-        (-1.4, -0.9),
-        (-1.3, -1.2),
-        (-1.5, 0.2),
-        (-1.2, -0.4),
-        (-0.5, 1.2),
-        (-1.5, 2.1),
-        (1, 1),
-        # --
-        (1.3, 0.8),
-        (1.5, 0.5),
-        (0.2, -2),
-        (0.5, -2.4),
-        (0.2, -2.3),
-        (0, -2.7),
-        (1.3, 2.8),
-        ].T
-    else:
-        #emulate some random data
-        x = np.random.uniform(-2, 2, size=(16, 1))
-        y = np.random.uniform(-2, 2, size=(16, 1))
-        X = np.hstack((x, y))
-    
-    Y = [0] * 8 + [1] * 8
-    
-    clf = svm.SVC(kernel=kernel, gamma=2)
-    clf.fit(X, Y)
-
-    # plot the line, the points, and the nearest vectors to the plane
-    fig= plt.figure(figsize=(10, 6), facecolor = 'none', 
-                    frameon = False, dpi = dpi)
-    ax = fig.add_subplot(111)
-    
-    ax.scatter(
-        clf.support_vectors_[:, 0],
-        clf.support_vectors_[:, 1],
-        s=80,
-        facecolors="none",
-        zorder=10,
-        edgecolors="k",
-    )
-    ax.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=cmap, edgecolors="k")
-
-    ax.axis("tight")
-    x_min = -3
-    x_max = 3
-    y_min = -3
-    y_max = 3
-
-    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
-    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
-
-    # Put the result into a color plot
-    Z = Z.reshape(XX.shape)
-    ax.pcolormesh(XX, YY, Z > 0, cmap=cmap)
-    ax.contour(
-        XX,
-        YY,
-        Z,
-        colors=["k", "k", "k"],
-        linestyles=["--", "-", "--"],
-        levels=[-0.5, 0, 0.5],
-    )
-
-    ax.set_xlim(x_min, x_max)
-    ax.set_ylim(y_min, y_max)
-
-    ax.set_xticks(())
-    ax.set_yticks(())
-    ax.set_title('Type of kernel: ' + kernel, 
-                 color = "white", fontdict = font1, pad=20,  
-                 bbox=dict(boxstyle="round,pad=0.3", 
-                           color = "#6366F1"))
-    
-    plt.close()
-    return fig
-
-intro = """<h1 style="text-align: center;">🤗 Introducing SVM-Kernels 🤗</h1>
-"""
-desc = """<h3 style="text-align: center;">Three different types of SVM-Kernels are displayed below. 
-The polynomial and RBF are especially useful when the data-points are not linearly separable. </h3>
-"""
-notice = """<div style = "text-align: left;"> <em>Notice: Run the model on example data or press
-<strong>Randomize data</strong> button to check out the model on emulated data-points.</em></div>"""
-
-made ="""<div style="text-align: center;">
-  <p>Made with ❤</p>"""
-
-link = """<div style="text-align: center;">
-<a href="https://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py" target="_blank" rel="noopener noreferrer">
-Demo is based on this script from scikit-learn documentation</a>"""
-
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo",
-                                    secondary_hue="violet",
-                                    neutral_hue="slate",
-                                    font =  gr.themes.GoogleFont("Inter")),
-               title="SVM-Kernels") as demo:
-    
-    gr.HTML(intro)
-    gr.HTML(desc)
-    with gr.Column():
-        kernel = gr.Radio(kernels, label="Select kernel:",
-                                show_label = True, value = 'linear') 
-        plot = gr.Plot(label="Plot")
-    
-    with gr.Accordion(label = "More options", open = True):
-        cmap = gr.Radio(['Set1', 'Set2', 'Set3', 'tab10', 'tab20'], label="Choose color map: ", value = 'Set2')
-        dpi = gr.Slider(50, 150, value = 75, step = 1, label = "Set the resolution: ")
-        gr.HTML(notice)
-        random = gr.Button("Randomize data").style(full_width = False)
-        
-    cmap.change(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot)
-    dpi.change(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot) 
-    kernel.change(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot)
-    
-    random.click(fn=clf_kernel, inputs=[kernel,cmap,dpi,random], outputs=plot)
-    
-    demo.load(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot)
-    gr.HTML(made)
-    gr.HTML(link)
-    
-demo.launch()