Create app.py

app.py

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+import os
+
+import pandas as pd
+from sklearn.ensemble import IsolationForest
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+import gradio as gr
+
+
+#Data preparation
+n_samples, n_outliers = 120, 40
+rng = np.random.RandomState(0)
+covariance = np.array([[0.5, -0.1], [0.7, 0.4]])
+cluster_1 = 0.4 * rng.randn(n_samples, 2) @ covariance + np.array([2, 2])  # general deformed cluster
+cluster_2 = 0.3 * rng.randn(n_samples, 2) + np.array([-2, -2])  # spherical cluster
+outliers = rng.uniform(low=-4, high=4, size=(n_outliers, 2))
+
+X = np.concatenate([cluster_1, cluster_2, outliers]) #120+120+40 = 280 with 2D
+y = np.concatenate(
+    [np.ones((2 * n_samples), dtype=int), -np.ones((n_outliers), dtype=int)]
+)
+
+#Visualize the data as a scatter plot
+
+
+
+# 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])}
+
+
+# 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()