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()