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5_2_read_and_sentimental_analysis_solution.py
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5_2_read_and_sentimental_analysis_solution.py
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# MIT License
#
# Copyright (c) 2019 Jaehyeuk Oh, Hyperconnect
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import absolute_import
import logging
import json
from sentimental import Sentimental
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.transforms.core import DoFn
def run(argv=None):
pipeline_options = PipelineOptions(["--runner=DirectRunner", "--streaming"])
p = beam.Pipeline(options=pipeline_options)
# read
topic_path = "projects/qwiklabs-gcp-34125c5e4e40e9e3/topics/pycon30-tweet" # replace topic with yours
lines = p | 'read' >> beam.io.ReadFromPubSub(topic=topic_path)
# format message
def format_message(message, timestamp=beam.DoFn.TimestampParam):
message = json.loads(message)
formatted_message = {
'text': message.get('text'),
'created_at': message.get('created_at'),
'timestamp': float(timestamp)
}
return formatted_message
formatted = lines | beam.Map(format_message)
class CalculateSentimentFn(DoFn):
senti = None
def start_bundle(self):
logging.info('model loading in start_bundle: start')
if (not self.senti):
self.senti = Sentimental.load('model.pickle')
logging.info('model loading in start_bundle: done')
def setup(self):
logging.info('model loading in setup: start')
if (not self.senti):
self.senti = Sentimental.load('model.pickle')
logging.info('model loading in setup: done')
def process(self, element):
key, value = element
res = self.senti.sentiment(value.get('text'))
yield (value, res.get('positive'), res.get('neutral'), res.get('negative'))
sentimented = (formatted
| 'convert to KV' >> beam.Map(lambda x: ('common key', x))
| 'calc sentiment' >> (beam.ParDo(CalculateSentimentFn())))
sentimented | 'out' >> beam.Map(lambda x: logging.info(x))
result = p.run()
result.wait_until_finish()
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
run()