Update app.py

app.py

@@ -1,26 +1,11 @@
-import numpy as np # Library for math operations
-import pandas as pd # Library for data handling
-import sklearn # The machine learning library we will be using in this entire course
-from sklearn import tree # Tree function is used for visualizing decision tree
-from sklearn.metrics import * # Importing function that can be used to calculate different metrics
-from sklearn.model_selection import train_test_split # Importing function that can split a dataset into training and testing set
-from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler # Importing functions for scaling the data
-from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier
-from sklearn.model_selection import GridSearchCV, RandomizedSearchCV # Importing GridSearchCV and RandomSearchCV
-from sklearn.pipeline import make_pipeline
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.linear_model import LogisticRegression
-from xgboost import XGBClassifier # Importing the XGBoost Classifier
-from sklearn.neural_network import MLPClassifier
-
-import matplotlib.pyplot as plt # Importing the package for plotting
-plt.style.use('fivethirtyeight') # Use the styling from FiveThirtyEight Website
-import seaborn as sns # Importing another package for plotting
-
-plt.rcParams['figure.figsize'] = [10, 6] ## plot size
-plt.rcParams['axes.linewidth'] = 2.0 #set the value globally
-
+import pickle
+import pandas as pd
+import shap
+from shap.plots._force_matplotlib import draw_additive_plot
 import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
 
 def greet (name):
     return "Hello" + name + "!"