Update app.py

app.py

@@ -9,6 +9,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 from skops import hub_utils
 import pickle
+import time
 
 
 
@@ -37,25 +38,22 @@ def visualize_input_data():
     # plt.show()
     return fig
 
-def download_model_skops():
-    repo_id="sklearn-docs/anomaly-detection"
-    download_repo = "downloaded-model"
-    hub_utils.download(repo_id=repo_id, dst=download_repo)
-    #     repo_copy = mkdtemp(prefix="skops")
-    # hub_utils.download(repo_id=repo_id, dst=repo_copy, token=token)
-    # print(os.listdir(download_repo))
+
 
 
 from sklearn.inspection import DecisionBoundaryDisplay
 
-def plot_decision_boundary(classifier):
+def plot_decision_boundary():
+
+  time.sleep(1)
+
   disp = DecisionBoundaryDisplay.from_estimator(
-    classifier,
+    loaded_model,
     X,
     response_method="predict",
     alpha=0.5,
 )
-  fig = plt.figure(1, facecolor="w", figsize=(5, 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_.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
   handles, labels = scatter.legend_elements()
@@ -64,7 +62,8 @@ def plot_decision_boundary(classifier):
   
   plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
   # plt.savefig('decision_boundary.png',dpi=300, bbox_inches = "tight")
-  return fig
+
+  return fig1
 
 
 
@@ -80,22 +79,20 @@ with gr.Blocks(title=title) as demo:
         
     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)
-    if os.listdir(download_repo):
-      # hub_utils.download(repo_id=repo_id, dst=download_repo)
-    
 
+    time.sleep(2)
   
-    
-      # print("Empty directory")
 
-      # print(os.listdir(download_repo))
-      loaded_model = pickle.load(open('isolation_forest.pkl', 'rb'))
-      btn = gr.Button(value="Plot decision boundary")
-      btn.click(plot_decision_boundary, inputs=[loaded_model], outputs= gr.Plot(label='Visualizing input dataset') )
+
+    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') )
     
     
     gr.Markdown( f"## Success")