Update app.py

app.py

@@ -6,12 +6,11 @@ import matplotlib.pyplot as plt
 from sklearn import svm
 import gradio as gr
 import matplotlib
-matplotlib.use('Agg')
+plt.switch_backend("agg")
 
 kernels = ["linear", "poly", "rbf"]
 
-font1 = {'family':'Consolas','size':20}
-
+font1 = {'family':'Comic Sans SM','size':20}
 
 cmaps = {'Set1': plt.cm.Set1, 'Set2': plt.cm.Set2, 'Set3': plt.cm.Set3, 
          'tab10': plt.cm.tab10, 'tab20': plt.cm.tab20}
@@ -99,7 +98,7 @@ def clf_kernel(kernel, cmap, dpi = 300, use_random = False):
     
     return fig
 
-intro = """<h1 style="text-align: center;">Introducing <strong>SVM-Kernels</strong></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>
@@ -116,22 +115,21 @@ 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",
+                                    neutral_hue="slate",
                                     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') 
+        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)    
+    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)