Upload app.py

app.py

@@ -0,0 +1,175 @@
+# 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
+from matplotlib.colors import ListedColormap
+plt.switch_backend("agg")
+
+font1 = {'family':'DejaVu Sans','size':20}
+
+def create_data(random, size_num, x_min, x_max, y_min, y_max):
+    #emulate some random data
+    if random:
+        size_num = int(size_num)
+        x = np.random.uniform(x_min, x_max, size=(size_num, 1))
+        y = np.random.uniform(y_min, y_max, size=(size_num, 1))
+        
+        X = np.hstack((x, y))
+        Y = [0] * int(size_num/2) + [1] * int(size_num/2)
+    else:
+        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
+
+        Y = [0] * 8 + [1] * 8
+    return X, Y
+
+# fit the model
+def clf_kernel(color1, color2, dpi, size_num = None, x_min = None, 
+                x_max = None, y_min = None,
+                y_max = None, random = False):
+
+    if size_num is not None or x_min is not None or x_max is not None or y_min is not None or y_max is not None:
+        random = True
+
+    X, Y = create_data(random, size_num, x_min, x_max, y_min, y_max)
+
+    kernels = ["linear", "poly", "rbf"]
+          
+    # plot the line, the points, and the nearest vectors to the plane  
+    fig, axs = plt.subplots(1,3, figsize = (16,8), facecolor='none', dpi = res[dpi])
+    
+    cmap = ListedColormap([color1, color2], N=2, name = 'braincell')
+    for i, kernel in enumerate(kernels):
+        clf = svm.SVC(kernel=kernel, gamma=2)
+        clf.fit(X, Y)
+        axs[i].scatter(
+            clf.support_vectors_[:, 0],
+            clf.support_vectors_[:, 1],
+            s=80,
+            facecolors="none",
+            zorder=10,
+            edgecolors="k",
+        )
+        axs[i].scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=cmap, edgecolors="k")
+
+        axs[i].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)
+        axs[i].pcolormesh(XX, YY, Z > 0, cmap=cmap)
+        axs[i].contour(
+            XX,
+            YY,
+            Z,
+            colors=["k", "k", "k"],
+            linestyles=["--", "-", "--"],
+            levels=[-0.5, 0, 0.5],
+        )
+
+        axs[i].set_xlim(x_min, x_max)
+        axs[i].set_ylim(y_min, y_max)
+
+        axs[i].set_xticks(())
+        axs[i].set_yticks(())
+        axs[i].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, np.round(X, decimals=2)
+
+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 = """<br><div style = "text-align: left;"> <em>Notice: Run the model on example data or use <strong>Randomize data</strong> 
+button below to check out the model on randomized data-points. Any changes to visual parameters will reset the data!</em></div>"""
+
+notice2 = """<br><div style = "text-align: left;"> <em>Notice:  The data points are categorized into two distinct classes, and they are evenly distributed on the plots to visually represent these classes.</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>"""
+
+res = {'Small': 50, 'Medium': 75, 'Large': 100}
+
+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.Tab("Plotted results"):
+        plot = gr.Plot(label="Kernel comparison:")
+    with gr.Tab("Data coordinates"):
+        gr.HTML(notice2)
+        X = gr.Numpy(headers = ['x','y'], interactive=False)
+    
+    with gr.Column():
+        
+        with gr.Accordion(label = 'Randomize data'):
+            gr.HTML(notice)
+            samples = gr.Slider(4, 16, value = 8, step = 2, label = "Number of samples:")
+            x_min = gr.Slider(-3, 0, value=-2, step=0.1, label="X Min:")
+            x_max = gr.Slider(0, 3, value=2, step=0.1, label="X Max:")
+            y_min = gr.Slider(-3, 0, value=-2, step=0.1, label="Y Min:")
+            y_max = gr.Slider(0, 3, value=2, step=0.1, label="Y Max:")
+            random = gr.Button("Randomize data")
+        
+
+        
+        
+        with gr.Accordion(label = "Visual parameters"):
+            with gr.Row():
+                color1 = gr.ColorPicker(label = 'Pick color one:', value = '#9abfd8')
+                color2 = gr.ColorPicker(label = 'Pick color two:', value = '#371c4b')
+            #dpi = gr.Slider(50, 100, value = 75, step = 1, label = "Set the resolution: ")
+            dpi = gr.Radio(list(res.keys()), value = 'Medium', label = "Select the plot size:")
+      
+    params2 = [color1, color2, dpi]
+
+    random.click(fn=clf_kernel, inputs=[color1, color2, dpi,samples, x_min, x_max, y_min, y_max], outputs=[plot,X]) 
+
+    for i in params2:
+        i.change(fn=clf_kernel, inputs=[color1, color2,dpi], outputs=[plot, X])
+    
+    demo.load(fn=clf_kernel, inputs=[color1, color2, dpi], outputs=[plot,X]) 
+    gr.HTML(made)
+    gr.HTML(link)
+    
+demo.launch()