examples/github_issue_summarization/notebooks/IssueSummarization.py

23 lines
757 B
Python

from __future__ import print_function
import dill as dpickle
import numpy as np
from keras.models import load_model
from seq2seq_utils import Seq2Seq_Inference
class IssueSummarization(object):
def __init__(self):
with open('body_pp.dpkl', 'rb') as f:
body_pp = dpickle.load(f)
with open('title_pp.dpkl', 'rb') as f:
title_pp = dpickle.load(f)
self.model = Seq2Seq_Inference(encoder_preprocessor=body_pp,
decoder_preprocessor=title_pp,
seq2seq_model=load_model('seq2seq_model_tutorial.h5'))
def predict(self, X, feature_names):
return np.asarray([[self.model.generate_issue_title(body[0])[1]] for body in X])