bug fix

- slider values in inputs arguments when calling change methods
- use of partial to call the main function

app.py

@@ -1,5 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
+
+from functools import partial
 from scipy import linalg
 from sklearn.datasets import make_sparse_spd_matrix
 from sklearn.covariance import GraphicalLassoCV, ledoit_wolf
@@ -125,15 +127,15 @@ title = "Sparse inverse covariance estimation"
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown("Estimating covariance and sparse precision from a small number of samples using GraphicalLasso and Ledoit-Wolf algorithms.")
-    n_samples = gr.Slider(minimum=20, maximum=100, step=5, 
+    n_samples = gr.Slider(minimum=20, maximum=100, step=5, value=60,
     label = "Number of Samples")
-    n_features = gr.Slider(minimum=10, maximum=100, step=5, 
+    n_features = gr.Slider(minimum=10, maximum=100, step=5, value=20,
     label = "Number of features")
-    alpha = gr.Slider(minimum=0, maximum=1, step=0.1, 
+    alpha = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.98,
     label = "sparsity coefficient (alpha)")
-    smallest_coef = gr.Slider(minimum=0, maximum=1, step=0.1, 
+    smallest_coef = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.4,
     label = "minimum correlation value")
-    largest_coef = gr.Slider(minimum=0, maximum=1, step=0.1, 
+    largest_coef = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7,
     label = "maximum correlation value")
 
     models = ['empirical', 'lasso', 'leidot-wolf']
@@ -142,11 +144,12 @@ with gr.Blocks(title=title) as demo:
 
         model = models[model_counter]
         plot = gr.Plot(label=model)
-        n_samples.change(fn=compute_and_plot, inputs=[0.98, 0.4, 0.7, 20, 60, 'covariance', model], outputs=plot)
-        n_features.change(fn=compute_and_plot, inputs=[0.98, 0.4, 0.7, 20, 60, 'covariance', model], outputs=plot)
-        alpha.change(fn=compute_and_plot, inputs=[0.98, 0.4, 0.7, 20, 60, 'covariance', model], outputs=plot)
-        smallest_coef.change(fn=compute_and_plot, inputs=[0.98, 0.4, 0.7, 20, 60, 'covariance', model], outputs=plot)
-        largest_coef.change(fn=compute_and_plot, inputs=[0.98, 0.4, 0.7, 20, 60, 'covariance', model], outputs=plot)
+        fn = partial(compute_and_plot, model=input_model, measure='covariance')
+        n_samples.change(fn=fn, inputs=[alpha, smallest_coef, largest_coef, n_features, n_samples], outputs=plot)
+        n_features.change(fn=fn, inputs=[alpha, smallest_coef, largest_coef, n_features, n_samples], outputs=plot)
+        alpha.change(fn=fn, inputs=[alpha, smallest_coef, largest_coef, n_features, n_samples], outputs=plot)
+        smallest_coef.change(fn=fn, inputs=[alpha, smallest_coef, largest_coef, n_features, n_samples], outputs=plot)
+        largest_coef.change(fn=fn, inputs=[alpha, smallest_coef, largest_coef, n_features, n_samples], outputs=plot)
     
     
 demo.launch()