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app.py

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+import numpy as np
+import pandas as pd
+import pickle
+import sys
+import sklearn 
+import sklearn.neighbors._base
+sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base
+import xgboost
+from missingpy import MissForest
+import warnings
+warnings.simplefilter(action="ignore", category=FutureWarning)
+from warnings import filterwarnings
+
+filterwarnings("ignore")
+seed=42
+
+annotations = pd.read_csv("annotations_dataset.csv")
+annotations = annotations.set_index("Gene")
+imputer = MissForest(random_state=seed)
+data = imputer.fit_transform(annotations)
+
+model = pickle.load(open('fitted_model.sav', 'rb'))
+
+predictions = list(model.predict(data))
+
+output = pd.Series(data=predictions, index=annotations.index, name="XGB_Score")
+df_total = pd.concat([annotations, output], axis=1)