Boost.Python.ArgumentError: Python argument types in ~did not match C++ signature, How can I handle it?

Question

I want to predict Knoell data using ppar_ligand_model. But some SMILES display this error message. How can I handle it? Is this problem of SMILE's structure? And What code should I add to exclude the material's SMILE that causes the error?

Here is soruce code and github link run_pparg_ligand_model.py

import numpy
import pandas
import sys
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.Chem import Draw
from rdkit.Chem.Draw import SimilarityMaps
from rdkit import Chem, DataStructs
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from keras.models import load_model


def main():
    """Run the main function."""
    outfile = open(sys.argv[1] + "_predictions.txt", 'w')
    outfile.write("Chemical Name\tPrediction\n")
    model = load_model("pparg_ligand_model.h5")
    dataframe = pandas.read_csv(sys.argv[1], sep="\t")
    mols = []
    fps = []

    for index, row in dataframe.iterrows():
#         try : 
#             mol = Chem.MolFromSmiles(row['SMILES'])
#             break
#         except :
#             continue
        mol = Chem.MolFromSmiles(row['SMILES'])

        fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2)
        mols.append(mol)
        fps.append(fp)

    np_fps = []
    for fp in fps:
        arr = numpy.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)

    np_fps_array = numpy.array(np_fps)
    predictions = model.predict(np_fps_array, batch_size=5)
    i = 0
    for prediction in predictions:
        y_prediction = ''
        if(prediction < 0.50):
            y_prediction = "ligand"
        else:
            y_prediction = "not_ligand"
        outfile.write(dataframe['Chemical_Name'][i] + "\t" + y_prediction + "\n")
        i += 1
    outfile.close()


if __name__ == "__main__":
    main()