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Requests to Zemberek gRPC server

Project description

Zemberek gRPC

Latest version 0.16.0

Zemberek-NLP provides some of its functions via a remote procedure call framework called gRPC. gRPC is a high performance, open-source universal RPC framework. Once Zemberek-NLP gRPC server is started, other applications can access remote services natively via automatically generated client libraries.

Start gRPC server with docker

You can use it from directly in your computer or run with in docker container.

Run with docker

docker run -d --rm -p 6789:6789 --name zemberek-grpc ryts/zemberek-grpc

Check logs

docker logs -f zemberek-grpc

Install library and example

pip install zemberek-grpc

Python Client Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import sys

import grpc

import zemberek_grpc.language_id_pb2 as z_langid
import zemberek_grpc.language_id_pb2_grpc as z_langid_g
import zemberek_grpc.normalization_pb2 as z_normalization
import zemberek_grpc.normalization_pb2_grpc as z_normalization_g
import zemberek_grpc.preprocess_pb2 as z_preprocess
import zemberek_grpc.preprocess_pb2_grpc as z_preprocess_g
import zemberek_grpc.morphology_pb2 as z_morphology
import zemberek_grpc.morphology_pb2_grpc as z_morphology_g

channel = grpc.insecure_channel('localhost:6789')

langid_stub = z_langid_g.LanguageIdServiceStub(channel)
normalization_stub = z_normalization_g.NormalizationServiceStub(channel)
preprocess_stub = z_preprocess_g.PreprocessingServiceStub(channel)
morphology_stub = z_morphology_g.MorphologyServiceStub(channel)

def find_lang_id(i):
    response = langid_stub.Detect(z_langid.LanguageIdRequest(input=i))
    return response.langId

def tokenize(i):
    response = preprocess_stub.Tokenize(z_preprocess.TokenizationRequest(input=i))
    return response.tokens

def normalize(i):
    response = normalization_stub.Normalize(z_normalization.NormalizationRequest(input=i))
    return response

def analyze(i):
    response = morphology_stub.AnalyzeSentence(z_morphology.SentenceAnalysisRequest(input=i))
    return response;

def fix_decode(text):
    """Pass decode."""
    if sys.version_info < (3, 0):
        return text.decode('utf-8')
        return text

def run():
    lang_detect_input = 'merhaba dünya'
    lang_id = find_lang_id(lang_detect_input)
    print("Language of [" + fix_decode(lang_detect_input) + "] is: " + lang_id)

    tokenization_input = 'Merhaba dünya!'
    print('Tokens for input : ' + fix_decode(tokenization_input))
    tokens = tokenize(tokenization_input)
    for t in tokens:
        print(t.token + ':' + t.type)

    normalization_input = 'Mrhaba dnya'
    print('Normalization result for input : ' + fix_decode(normalization_input))
    n_response = normalize(normalization_input)
    if n_response.normalized_input:
        print('Problem normalizing input : ' + n_response.error)

    analysis_input = 'Kavanozun kapağını açamadım.'
    print('Analysis result for input : ' + fix_decode(analysis_input))
    analysis_result = analyze(analysis_input)
    for a in analysis_result.results:
        best =
        lemmas = ""
        for l in best.lemmas:
          lemmas = lemmas + " " + l
        print("Word = " + a.token + ", Lemmas = " + lemmas + ", POS = [" + best.pos + "], Full Analysis = {" + best.analysis + "}")

if __name__ == '__main__':

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