Update app.py

app.py

@@ -6,6 +6,12 @@ from scipy import ndimage
 from skimage import measure, color, io
 from tensorflow.keras.preprocessing import image
 from scipy import ndimage
+import skimage.io as io
+import skimage.transform as trans
+import numpy as np
+import tensorflow as tf
+from huggingface_hub.keras_mixin import from_pretrained_keras
+
 
 #Function that predicts on only 1 sample 
 def predict_sample(image):
@@ -39,7 +45,7 @@ def create_input_image(data, visualize=False):
   return input
 
 
-
+model= from_pretrained_keras("tareknaous/unet-visual-clustering")
 
 def get_instances(prediction, data, max_filter_size=1):
   #Adjust format (clusters to be 255 and rest is 0)
@@ -107,4 +113,89 @@ def get_instances(prediction, data, max_filter_size=1):
   cluster_ids = cluster_ids.astype('int8')
   cluster_ids[cluster_ids == -11] = 0
     
-  return cluster_ids
+  return cluster_ids
+  
+  
+  import gradio as gr
+from itertools import cycle, islice
+
+
+def visual_clustering(cluster_type, num_clusters, num_samples, random_state, median_kernel_size, max_kernel_size):
+
+  NUM_CLUSTERS = num_clusters
+  CLUSTER_STD = 4 * np.ones(NUM_CLUSTERS)
+
+  if cluster_type == "blobs":
+    data = datasets.make_blobs(n_samples=num_samples, centers=NUM_CLUSTERS, random_state=random_state,center_box=(0, 256), cluster_std=CLUSTER_STD)
+  
+  elif cluster_type == "varied blobs":
+    cluster_std = 1.5 * np.ones(NUM_CLUSTERS)
+    data = datasets.make_blobs(n_samples=num_samples, centers=NUM_CLUSTERS, cluster_std=cluster_std, random_state=random_state)
+
+  elif cluster_type == "aniso":
+    X, y = datasets.make_blobs(n_samples=num_samples, centers=NUM_CLUSTERS, random_state=random_state, center_box=(-30, 30))
+    transformation = [[0.8, -0.6], [-0.4, 0.8]]
+    X_aniso = np.dot(X, transformation)
+    data = (X_aniso, y)
+
+  elif cluster_type == "noisy moons":
+    data = datasets.make_moons(n_samples=num_samples, noise=.05)
+
+  elif cluster_type == "noisy circles":
+    data = datasets.make_circles(n_samples=num_samples, factor=.01, noise=.05)
+
+  max_x = max(data[0][:, 0])
+  min_x = min(data[0][:, 0])
+  new_max = 256
+  new_min = 0
+
+  data[0][:, 0] = (((data[0][:, 0] - min_x)*(new_max-new_min))/(max_x-min_x))+ new_min
+
+  max_y = max(data[0][:, 1])
+  min_y = min(data[0][:, 1])
+  new_max_y = 256
+  new_min_y = 0
+
+  data[0][:, 1] = (((data[0][:, 1] - min_y)*(new_max_y-new_min_y))/(max_y-min_y))+ new_min_y
+
+  fig1 = plt.figure()
+  plt.scatter(data[0][:, 0], data[0][:, 1], s=1, c='black')
+  plt.close()
+
+  input = create_input_image(data[0])
+  filtered = ndimage.median_filter(input, size=median_kernel_size)
+  result = predict_sample(filtered)
+  y_km = get_instances(result, data[0], max_filter_size=max_kernel_size)
+
+  colors = np.array(list(islice(cycle(["#000000", '#377eb8', '#ff7f00', '#4daf4a',
+                                             '#f781bf', '#a65628', '#984ea3',
+                                             '#999999', '#e41a1c', '#dede00' ,'#491010']),
+                                      int(max(y_km) + 1))))
+  #add black color for outliers (if any)
+  colors = np.append(colors, ["#000000"])
+  
+  fig2 = plt.figure()
+  plt.scatter(data[0][:, 0], data[0][:, 1], s=10, color=colors[y_km.astype('int8')])
+  plt.close()
+
+  return fig1, fig2
+
+iface = gr.Interface(
+    
+  fn=visual_clustering, 
+
+  inputs=[
+          gr.inputs.Dropdown(["blobs", "varied blobs",  "aniso", "noisy moons", "noisy circles" ]),
+          gr.inputs.Slider(1, 10, step=1, label='Number of Clusters'),
+          gr.inputs.Slider(10000, 1000000, step=10000, label='Number of Samples'),
+          gr.inputs.Slider(1, 100, step=1, label='Random State'),
+          gr.inputs.Slider(1, 100, step=1, label='Denoising Filter Kernel Size'),
+          gr.inputs.Slider(1,100, step=1, label='Max Filter Kernel Size')
+          ],
+
+  outputs=[
+           gr.outputs.Image(type='plot', label='Dataset'),
+           gr.outputs.Image(type='plot', label='Clustering Result')
+           ]
+           )
+iface.launch()