Upload reva.py

reva.py

@@ -7,7 +7,7 @@ import requests
 # from PyQt5 import QtCore
 # from PyQt5 import QtGui, QtWidgets
 # from PyQt5.QtWidgets import *
-import openpyxl
+# import openpyxl
 import warnings
 import sys
 import numpy as np
@@ -22,7 +22,7 @@ from Bio.SeqUtils import IsoelectricPoint as IP
 from rdkit import Chem
 from rdkit.Chem import rdMolDescriptors
 from rdkit.Chem import Descriptors
-from rdkit.Chem.rdMolDescriptors import CalcMolFormula
+# from rdkit.Chem.rdMolDescriptors import CalcMolFormula
 from Bio.SeqUtils.ProtParam import ProteinAnalysis
 import numpy as np
 from scipy.constants import e, epsilon_0
@@ -36,18 +36,18 @@ from rdkit.Chem import Descriptors
 from scipy.constants import e
 import pandas as pd
 from rdkit.Chem import rdMolTransforms
-from collections import Counter
+# from collections import Counter
 # from qiskit.algorithms.optimizers import COBYLA
 # from qiskit.circuit.library import TwoLocal, ZZFeatureMap
-from qiskit.utils import algorithm_globals
-from qiskit import BasicAer
-from qiskit.utils import QuantumInstance
+# from qiskit.utils import algorithm_globals
+# from qiskit import BasicAer
+# from qiskit.utils import QuantumInstance
 from qiskit_machine_learning.algorithms import VQC, VQR
-import qiskit
+# import qiskit
 import urllib
 # from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Estimator, Session
 
-algorithm_globals.random_seed = 42
+# algorithm_globals.random_seed = 42
 warnings.filterwarnings('ignore')
 
 asset_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'asset')
@@ -2994,7 +2994,7 @@ def main():
     target_folder = os.path.join("result", "result_"+generate_filename_with_timestamp_and_random())
     create_folder(target_folder)
     reva = ReVa(sequence, get_base_path(),os.path.join(target_folder, generate_filename_with_timestamp_and_random()), n_receptor, n_adjuvant, blast_activate, llm_url, alphafold_url)
-    qreva = QReVa(sequence, get_base_path(),os.path.join(target_folder, generate_filename_with_timestamp_and_random("quantum")), n_receptor, n_adjuvant, blast_activate, llm_url=llm_url, alphafold_url=alphafold_url)
+    qreva = QReVa(sequence, get_base_path(),os.path.join(target_folder, generate_filename_with_timestamp_and_random("quantum")), n_receptor, n_adjuvant, blast_activate=blast_activate ,qibm_api="", backend_type="ibmq_qasm_simulator",llm_url=llm_url, alphafold_url=alphafold_url)
     res1, res2, res3 = reva.predict()
     qres1, qres2, qres3 = qreva.predict()