Update app.py

app.py

@@ -6,6 +6,7 @@ from sklearn.ensemble import IsolationForest
 import numpy as np
 from sklearn.model_selection import train_test_split
 import gradio as gr
+import matplotlib.pyplot as plt
 
 
 #Data preparation
@@ -23,48 +24,27 @@ y = np.concatenate(
 
 #Visualize the data as a scatter plot
 
+def visualize_input_data():
+    fig = plt.figure(1, facecolor="w", figsize=(5, 5))
+    scatter = plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
+    handles, labels = scatter.legend_elements()
+    plt.axis("square")
+    plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
+    plt.title("Gaussian inliers with \nuniformly distributed outliers")
+    # plt.show()
+    return fig
 
+title = " An example using IsolationForest for anomaly detection."
 
-# def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
-#     if passenger_class is None or embark_point is None:
-#         return None
-#     df = pd.DataFrame.from_dict(
-#         {
-#             "Pclass": [passenger_class + 1],
-#             "Sex": [0 if is_male else 1],
-#             "Age": [age],
-#             "Fare": [fare],
-#             "Embarked": [embark_point + 1],
-#             "Company": [
-#                 (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
-#             ]
-#         }
-#     )
-#     df = encode_age(df)
-#     df = encode_fare(df)
-#     pred = clf.predict_proba(df)[0]
-#     return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"# {title}")
+    
 
+    gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py**")
 
-# demo = gr.Interface(
-#     predict_survival,
-#     [
-#         gr.Dropdown(["first", "second", "third"], type="index"),
-#         "checkbox",
-#         gr.Slider(0, 80, value=25),
-#         gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
-#         gr.Number(value=20),
-#         gr.Radio(["S", "C", "Q"], type="index"),
-#     ],
-#     "label",
-#     examples=[
-#         ["first", True, 30, [], 50, "S"],
-#         ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
-#         ["third", True, 30, ["Child"], 20, "S"],
-#     ],
-#     interpretation="default",
-#     live=True,
-# )
 
-# if __name__ == "__main__":
-#     demo.launch()
+        
+    btn = gr.Button(value="Visualize input dataset")
+    btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )
+    gr.Markdown( f"## In progress")
+demo.launch()