Upload 2 files

app.py

@@ -0,0 +1,140 @@
+# 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
+import matplotlib
+matplotlib.use('Agg')
+
+kernels = ["linear", "poly", "rbf"]
+
+font1 = {'family':'Consolas','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"))
+    
+    return fig
+
+intro = """<h1 style="text-align: center;">Introducing <strong>SVM-Kernels</strong></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 check 
+<strong>Randomize data</strong> 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="neutral",
+                                    font =  gr.themes.GoogleFont("Inter")),
+               title="SVM-Kernels") as demo:
+    gr.HTML(intro)
+    gr.HTML(desc)
+    with gr.Box():
+        with gr.Row():
+            kernel = gr.Dropdown([i for i in kernels], label="Select kernel:",
+                                    show_label = True, value = 'linear') 
+        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 = 100, step = 1, label = "Set the resolution: ")
+            gr.HTML(notice)
+            random = gr.Checkbox(label="Randomize data", value = False)
+        
+        btn = gr.Button('Make plot!').style(full_width=True)    
+    plot = gr.Plot(label="Plot")
+    btn.click(fn=clf_kernel, inputs=[kernel,cmap,dpi,random], outputs=plot)
+    gr.HTML(made)
+    gr.HTML(link)
+    
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+matplotlib
+numpy
+scikit-learn