ATE api update 2

ATEapi/main.py

@@ -3,8 +3,9 @@ import torch
 import torch.nn.functional as F
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-from utils import *
-from canonical_utils import *
+from .utils import *
+from .canonical_utils import *
+
 from flask import Flask, request, jsonify
 from transformers import AutoTokenizer, AutoModelForTokenClassification 
 
@@ -12,12 +13,13 @@ from transformers import AutoTokenizer, AutoModelForTokenClassification
 app = Flask(__name__)
 
 label_list=["n", "B-T", "T"]
-tokenizer = AutoTokenizer.from_pretrained('./model/term_extractor/')
-model = AutoModelForTokenClassification.from_pretrained('./model/term_extractor/', num_labels=len(label_list)).to(device)
+tokenizer = AutoTokenizer.from_pretrained('/app/model/term_extractor/')
+model = AutoModelForTokenClassification.from_pretrained('/app/model/term_extractor/', num_labels=len(label_list)).to(device)
 
 @app.route('/predict',methods=['POST'])
 def predict():
     frame = read_conll(request.files['file'])
+    # print(frame)
     sequences = [' '.join(x) for x in frame.word]
     lemma, pos, msd = frame.lemma, frame.pos, frame.msd
     preds = []