create app.py

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import datasets as ds
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from lime.lime_tabular import LimeTabularExplainer
+
+wines = ds.load_dataset("katossky/wine-recognition", split='train')
+wines = wines.to_pandas()
+wines.columns = wines.columns.str.strip()
+
+predictor = RandomForestClassifier(
+    n_estimators=1000, max_depth=5, n_jobs=4,
+    random_state=44 # for reproducibility
+)
+
+predictor.fit( wines.drop('label', axis=1), wines['label'] )
+
+def plot_explanation(instance_part_1, instance_part_2, instance_part_3, sigma):
+  instance_pd = pd.concat([instance_part_1, instance_part_2, instance_part_3], axis=1)
+  instance_np = instance_pd.to_numpy().squeeze()
+  explainer = lime.lime_tabular.LimeTabularExplainer(
+    training_data = wines.drop('label', axis=1), #.to_numpy(),
+    feature_names = wines.columns[1:].to_list(),
+    discretize_continuous = False, kernel_width=sigma
+  )
+  explanation = explainer.explain_instance(
+    instance_np,
+    predictor.predict_proba, #,
+    top_labels=3,
+    num_features=5
+  )
+  predictions = predictor.predict_proba(instance_pd)[0]
+  label = np.argmax(predictions)
+  confidences = {i: predictions[i] for i in range(3)}
+  return (
+    confidences,
+    explanation.as_pyplot_figure(label=label)
+  )
+
+sigma_default = 0.75*(wines.shape[1]-1)**0.5
+sigma = gr.Slider(0.001, 2*sigma_default, value=sigma_default, label='σ')
+
+instance_complete = wines.sample(1)
+
+instance_part_1 = gr.Dataframe(
+  label = "Chemical properties of the wine",
+  headers = wines.columns[1:6].to_list(),
+  row_count = (1,"fixed"),
+  col_count = (5, "fixed"),
+  datatype = "number",
+  value = instance_complete.iloc[:,1:6].values.tolist()
+)
+
+instance_part_2 = gr.Dataframe(
+  label = "",
+  show_label = False, # does not work
+  headers = wines.columns[6:10].to_list(),
+  row_count = (1,"fixed"),
+  col_count = (4, "fixed"),
+  datatype = "number",
+  value = instance_complete.iloc[:,6:10].values.tolist()
+)
+
+instance_part_3 = gr.Dataframe(
+  label = "",
+  show_label = False, # does not work
+  headers = wines.columns[10:].to_list(),
+  row_count = (1,"fixed"),
+  col_count = (4, "fixed"),
+  datatype = "number",
+  value = instance_complete.iloc[:,10:].values.tolist()
+)
+
+demo = gr.Interface(
+  fn = plot_explanation,
+  inputs = [instance_part_1, instance_part_2, instance_part_3, sigma],
+  outputs = ["label", "plot"]
+)
+demo.launch()