Update app.py

app.py

@@ -27,6 +27,10 @@ y = np.concatenate(
 )
 
 def load_hf_model_hub():
+    '''
+    Load the directory containing pretrained model  
+    and files from the model repository
+    '''
     repo_id="sklearn-docs/anomaly-detection"
     download_repo = "downloaded-model"
     hub_utils.download(repo_id=repo_id, dst=download_repo)
@@ -50,77 +54,34 @@ 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(
-    loaded_model,
-    X,
-    response_method="predict",
-    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")
-  # 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")
-  plt.axis("square")
-  
-  plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
-  # plt.savefig('decision_boundary.png',dpi=300, bbox_inches = "tight")
-
-  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."
+description1 = "The isolation forest is an Ensemble of Isolation trees and it isolates the datapoints using recursive random partitioning."
+description2 = "In case of outliers the number of splits required is greater than those required for inliers."
+description3 = "We will use the toy dataset as given in the scikit-learn page for Isolation Forest."
 
 with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"# {title}")
     
+    gr.Markdown(f"# {title}")
+    gr.Markdown(f"# {description1}")
+    gr.Markdown(f"# {description2}")
+    gr.Markdown(f"# {description3}")
 
     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') )
 
     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') )
+        image_decision = gr.Image('./downloaded-model/decision_boundary.png')
     
     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') )
-    
+        image_path = gr.Image('./downloaded-model/plot_path.png')
+
     
     gr.Markdown( f"## Success")
 demo.launch()