predict.py

from models.lncRNA_lib import *

mapLocation={'cuda:0':'cpu', 'cuda:1':'cpu'}
GraphLncLoc =[]
for i in range(1,6):
    GraphLncLoc.append(lncRNALocalizer(f"checkpoints/Final_model/fold{i}.pkl", map_location=mapLocation))

def lncRNA_loc_predict(lncRNA):
    return vote_predict(GraphLncLoc,lncRNA.upper())

if __name__=="__main__":
    sequence="ACCAUUUUCAUAGAAUCAUUAUGAUGAUUAACUAAAUUGUGGAUAAAUAAAAACCAAAAUACCAGUGGAAUGAAAUAUUGUCAUUACUUGGCCAUGAGCUUUUAAGUGUGUGUGUCUAUGUUUCUAGCGGGGCACAUGCCUGUAAUCAUAGCUCUUGGUAGGAAGUGACAAGAUUAGGCCUUGAGUAGAGCAAGUUUGAGGCUAAUAUGGGCUGUGUGAGACACUAUCUCAAAGCAAACAGUAUGUUCCUGAGAUAGGGCUGUAACUCAAUGACAAAGCACUGUCUAAUGUGCGAAGCCCCCCAAGUUCAAUCCACAGCCACUCAGAGGCUCCAGAGUCCUCCCUUCUACUCCUAUUGAGUACAAGUUUCAUUAGAGGUGAUACACACCAUUUUUCCAAAUGGCUAAUUGUGCCUUUUACAUAUAGUUGAUGAUAAAAAUACAUAUUGUGAAAUAAAAUAUUGUAUAUAUGUGUGCAUGCACACAUCUACUGUUCUGAACAGAGAACCCAGGACUUUAUGAAGGACUUUAUUAACUAUUGCCCCUAAGCCAUGAAAGAUAUUUCUUUGACUGGCAUAUUCGCAGCAUGAUUUUUCCACUCUUCUGCACUCUUCAACUUGUGGUUCGUGUAUGCAUGUGUGUACAAACACACAUACACAUGCAGUGUAGAGGUGUCACUAUUUUGACCAGGCUCGUCU"
    print(lncRNA_loc_predict(sequence))