def post(self):
input_string = request.get_data().decode('UTF-8')
my_extractor = extractor()
my_extractor.from_string(input_string)
print ("9")
my_responser = responser()
print ("9")
obj1, obj2, predicates = my_extractor.get_params()
print ("9")
print ("len(obj1), len(obj2)", len(obj1), len(obj2))
print ("obj1, obj2, predicates", obj1, obj2, predicates)
response = make_response(jsonify(first_object = obj1, second_object = obj2, preds = predicates))
return response
#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#LM_CAM = load_cam_model(device)
#Cam = diviner(tp = 'cam', model = LM_CAM, device = device)
#print ("loaded cam")
device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")
LM_SMALL = load_small_model(device)
GPT2Small = diviner(tp='small', model=LM_SMALL, device=device)
#GPT2Small_vs = diviner(tp = 'small_vs', model = LM_SMALL, device = device)
#GPT2Small_vs_str = diviner(tp = 'small_vs_str', model = LM_SMALL, device = device)
print("loaded gpt2")
Templ = diviner(tp='templates', model='', device=device)
my_extractor = extractor(my_device=3)
print("loaded extractor")
def main():
df = pd.DataFrame(
columns=['Object 1', 'Object 2', 'Question', 'Best Answer', 'Answers'])
with open('yahoo_answers_positive_questions.csv', 'r') as file:
reader = csv.reader(file)
for ind, row in enumerate(reader):
d = {
'Object 1': row[0],
'Object 2': row[1],
'Question': row[2],
'Best Answer': row[3],