mirror of https://github.com/kubeflow/examples.git
26 lines
905 B
Python
26 lines
905 B
Python
"""Generates predictions using a stored model.
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Uses trained model files to generate a prediction.
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"""
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from __future__ import print_function
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import numpy as np
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import dill as dpickle
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from keras.models import load_model
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from seq2seq_utils import Seq2Seq_Inference
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class IssueSummarization(object):
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def __init__(self):
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with open('body_pp.dpkl', 'rb') as body_file:
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body_pp = dpickle.load(body_file)
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with open('title_pp.dpkl', 'rb') as title_file:
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title_pp = dpickle.load(title_file)
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self.model = Seq2Seq_Inference(encoder_preprocessor=body_pp,
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decoder_preprocessor=title_pp,
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seq2seq_model=load_model('seq2seq_model_tutorial.h5'))
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def predict(self, input_text, feature_names): # pylint: disable=unused-argument
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return np.asarray([[self.model.generate_issue_title(body[0])[1]] for body in input_text])
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