Update app.py

app.py

@@ -5,12 +5,11 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn import svm
 import gradio as gr
-import matplotlib
 plt.switch_backend("agg")
 
 kernels = ["linear", "poly", "rbf"]
 
-font1 = {'family':'Comic Sans SM','size':20}
+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}
@@ -96,15 +95,16 @@ def clf_kernel(kernel, cmap, dpi = 300, use_random = False):
                  bbox=dict(boxstyle="round,pad=0.3", 
                            color = "#6366F1"))
     
+    plt.close()
     return fig
 
-intro = """<h1 style="text-align: center;">Introducing SVM-Kernels</h1>
+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>
+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>"""
+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>"""
@@ -118,20 +118,27 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo",
                                     neutral_hue="slate",
                                     font =  gr.themes.GoogleFont("Inter")),
                title="SVM-Kernels") as demo:
+    
     gr.HTML(intro)
     gr.HTML(desc)
-    with gr.Box():
-        kernel = gr.Dropdown([i for i in kernels], label="Select kernel:",
+    with gr.Column():
+        kernel = gr.Radio(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)
+        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 = 100, step = 1, label = "Set the resolution: ")
+        gr.HTML(notice)
+        random = gr.Button("Randomize data").style(full_width = 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)
+    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)