Update app.py

app.py

@@ -26,6 +26,14 @@ y = np.concatenate(
     [np.ones((2 * n_samples), dtype=int), -np.ones((n_outliers), dtype=int)]
 )
 
+def load_hf_model_hub():
+    repo_id="sklearn-docs/anomaly-detection"
+    download_repo = "downloaded-model"
+    hub_utils.download(repo_id=repo_id, dst=download_repo)
+    time.sleep(2)
+    loaded_model = pickle.load(open('./downloaded-model/isolation_forest.pkl', 'rb'))
+    return loaded_model
+
 #Visualize the data as a scatter plot
 
 def visualize_input_data():
@@ -36,6 +44,7 @@ def visualize_input_data():
     plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
     plt.title("Gaussian inliers with \nuniformly distributed outliers")
     # plt.show()
+    # plt.clear()
     return fig
 
 
@@ -44,7 +53,9 @@ def visualize_input_data():
 from sklearn.inspection import DecisionBoundaryDisplay
 
 def plot_decision_boundary():
-
+  # progress(0, desc="Starting...")
+  # plt.clear()
+  plt.clf()
   time.sleep(1)
 
   disp = DecisionBoundaryDisplay.from_estimator(
@@ -54,7 +65,8 @@ def plot_decision_boundary():
     alpha=0.5,
 )
   fig1 = plt.figure(1, facecolor="w", figsize=(5, 5))
-  scatter = plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k") 
+  scatter = plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
+  # disp.ax_.
   disp.ax_.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
   handles, labels = scatter.legend_elements()
   disp.ax_.set_title("Binary decision boundary \nof IsolationForest")
@@ -65,6 +77,26 @@ def plot_decision_boundary():
 
   return fig1
 
+def plot_path_length():
+  plt.clf()
+
+  time.sleep(1)
+  disp = DecisionBoundaryDisplay.from_estimator(
+    loaded_model,
+    X,
+    response_method="decision_function",
+    alpha=0.5,
+  )
+  fig2 = plt.figure(1, facecolor="w", figsize=(5, 5))
+  scatter = disp.ax_.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
+  handles, labels = scatter.legend_elements()
+  disp.ax_.set_title("Path length decision boundary \nof IsolationForest")
+  plt.axis("square")
+  plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
+  plt.colorbar(disp.ax_.collections[1])
+  # plt.savefig('plot_path.png',dpi=300, bbox_inches = "tight")
+  return fig2
+
 
 
 title = " An example using IsolationForest for anomaly detection."
@@ -74,25 +106,20 @@ with gr.Blocks(title=title) as demo:
     
 
     gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py**")
+    
+    loaded_model = load_hf_model_hub()
 
+    with gr.Tab("Visualize Input dataset"):
+      btn = gr.Button(value="Visualize input dataset")
+      btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )
 
-        
-    btn = gr.Button(value="Visualize input dataset")
-    btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )
-
-    # download
-    repo_id="sklearn-docs/anomaly-detection"
-    download_repo = "downloaded-model"
-    hub_utils.download(repo_id=repo_id, dst=download_repo)
-
-    time.sleep(2)
-  
-
-
-    print(os.listdir(download_repo))
-    loaded_model = pickle.load(open('./downloaded-model/isolation_forest.pkl', 'rb'))
-    btn_decision = gr.Button(value="Plot decision boundary")
-    btn_decision.click(plot_decision_boundary, outputs= gr.Plot(label='Plot decision boundary') )
+    with gr.Tab("Plot Decision Boundary"):
+      btn_decision = gr.Button(value="Plot decision boundary")
+      btn_decision.click(plot_decision_boundary, outputs= gr.Plot(label='Plot decision boundary') )
+    
+    with gr.Tab("Plot Path"):
+      btn_path = gr.Button(value="Path length decision boundary")
+      btn_path.click(plot_path_length, outputs= gr.Plot(label='Path length decision boundary') )
     
     
     gr.Markdown( f"## Success")