Update app.py

app.py

@@ -7,40 +7,31 @@ from skimage.data import coins
 from skimage.transform import rescale
 from sklearn.feature_extraction import image
 from sklearn.cluster import spectral_clustering
+import gradio as gr
 
 
-# load the coins as a numpy array
-orig_coins = coins()
-
-# Resize it to 20% of the original size to speed up the processing
-# Applying a Gaussian filter for smoothing prior to down-scaling
-# reduces aliasing artifacts.
-smoothened_coins = gaussian_filter(orig_coins, sigma=2)
-rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", anti_aliasing=False)
+def getClusteringPlot(algorithm):
+    # load the coins as a numpy array
+    orig_coins = coins()
 
-# Convert the image into a graph with the value of the gradient on the
-# edges.
-graph = image.img_to_graph(rescaled_coins)
+    # Pre-processing the image
+    smoothened_coins = gaussian_filter(orig_coins, sigma=2)
+    rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", anti_aliasing=False)
 
-# Take a decreasing function of the gradient: an exponential
-# The smaller beta is, the more independent the segmentation is of the
-# actual image. For beta=1, the segmentation is close to a voronoi
-beta = 10
-eps = 1e-6
-graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
+    # Convert the image into a graph 
+    graph = image.img_to_graph(rescaled_coins)
 
-# The number of segmented regions to display needs to be chosen manually.
-# The current version of 'spectral_clustering' does not support determining
-# the number of good quality clusters automatically.
-n_regions = 26
+    beta = 10
+    eps = 1e-6
+    graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
 
-# Computing a few extra eigenvectors may speed up the eigen_solver.
-# The spectral clustering quality may also benetif from requesting
-# extra regions for segmentation.
-n_regions_plus = 3
+    # The number of segmented regions to display needs to be chosen manually
+    n_regions = 26
 
-#Function for retrieving the plot
-def getClusteringPlot(algorithm):
+    # The spectral clustering quality may also benetif from requesting
+    # extra regions for segmentation.
+    n_regions_plus = 3
+    
     t0 = time.time()
     labels = spectral_clustering(
         graph,
@@ -66,11 +57,6 @@ def getClusteringPlot(algorithm):
         # To view individual segments as appear comment in plt.pause(0.5)
     return plt
 
-import gradio as gr
-
-def welcome(name):
-    return f"Welcome to Gradio, {name}!"
-
 with gr.Blocks() as demo:
     gr.Markdown(
     """
@@ -83,6 +69,5 @@ with gr.Blocks() as demo:
     plot = gr.Plot(label="Plot")
     inp.change(getClusteringPlot, inputs=inp, outputs=[plot])
     demo.load(getClusteringPlot, inputs=[inp], outputs=[plot])
-
-if __name__ == "main":
-    demo.launch()
+    
+demo.launch()