chinesenotes-go

README

Chinese Notes Dictionary App and Translation Portal

A Go web application and text processing library for translation from Chinese to English that is adaptable to different dictionaries and corpora, powering these use cases

1. A web site for Chinese-English dictionary lookup and language tools for language learning and reference
2. A password protected translation portal for a team of translators
3. A software library for Chinese text processing

Features include:

1. Chinese-English dictionary word lookup
2. Chinese text tokenization
3. Translation memory (optional)
4. Full text search of a Chinese corpus (optional)
5. Password proection (optional)
6. Integration with a rich JavaScript web client (optional)
7. Integration with a backend SQL databsae (optional)
8. Go module with Go and JSON APIs for interactive Chinese text processing
   • as opposed to batch processing of a corpus for indexing

A screenshot of dictionary lookup of multiple terms with text tokenization is shown below.

The web app drives the https://chinesenotes.com, https://ntireader.org, and https://hbreader.org web site and a private translation portal developed for Fo Guang Shan, working together with the Fo Guang Shan Institute of Humanistic Buddhism and Nan Tien Institute.

The items above marked as optional require some special set-up, as described below.

Setup

Install Go.

Quickstart

In a terminal, run the commands to get the command line app and download the dicitonary

PATH=$PATH:$HOME/go/bin
go install github.com/alexamies/cnreader@latest
cnreader -download_dict

Set an environment variable to let the app know where its home is

export CNWEB_HOME=.

Install the app

go install github.com/alexamies/chinesenotes-go@latest

Run the app

chinesenotes-go

When you see the output

...
Starting http server at http://localhost:8080

navigate to http://localhost:8080 with your browser. Enter some Chinese words and see the English equivalents. With no environment settings the dictionary will be loaded from the Net.

Basic Usage

The repo contains a number of file that allow for customization of the app. To get them, clone this repo:

git clone https://github.com/alexamies/chinesenotes-go.git

Then cd into the directory cloned:

cd chinesenotes-go

Build the app

go build

Run the web server

./chinesenotes-go

Navigate to http://localhost:8080

You can change styles by changing the CSS settings in the file web/styles.css. The webconfig.yaml file and HTML tempates in /templates allow some additional customization. The HTML interface is very basic, just enough for minimal testing. See the web-resources directory for customization of template files. Make sure that the TemplateDir parameter is set in the webconfig.yaml file for this to take effect. Another approach is to use AJAX communicate with the web app with complete customization of the web user interface, as in https://github.com/alexamies/chinesenotes.com .

Development testing

In another terminal

curl http://localhost:8080/find/?query=邃古

You should see JSON encoded data sent back.

Features

Chinese-English dictionary word lookup

The data/testdict.tsv file gives an example file that illustrates the structure of the dictionary. The dictionary is indexed by both simplified and traditional Chinese. Lookup by Chinese word is supported in file mode. You will need to install and setup the database to do lookup by English word Hanyu pinyin.

Chinese text tokenization

Given a string of Chinese text, the web app will segment it into words or multiword expressions contained in the dictionary. This saves a lot of time for readers who look up many words or discover how the words in a phrase are grouped, since there are no spaces in Chinese sentences. The presence of the dictionary files is needed for this. They can be loaded either from the file system or from the database by the web app.

Digital Library

The web app can support a library of Chinese texts. The texts in the library will be scanned with the Chinese terms enabled for mouse-over and clicking to find the English equivalents. The titles of the documents are also indexed to enable title search. This requires setting up the library files and list of texts with a certain structure, which is explained below.

Translation memory

Translation memory search find the closest matching term based on multiple criteria, including how many characters match, similarity of the character order, Pinyin match, and inclusion of the query in the notes. This depends on compilation of the translation memory index and loading it into the database.

Full text search of the Digital Library

Full text search of a Chinese corpus allows users to search a monolingual Chinese corpus. First, you need

1. Have a corpus following the layout conventions of Chinese Notes.
2. Compile the index, which computes word and bigram frequencies for each document with the cnreader command
3. Load the index files into the database
4. Load the corpus files into Google Cloud Storage

For the corpus structure see https://github.com/alexamies/chinesenotes.com/tree/master/data/corpus

Integration with a rich JavaScript web client (optional)

For web resources to give a higher quality user experience than the basic Go HTML templates using Material Design Web, see (Material Design Web Resources)[web_resources/README.md].

For an implementation where the communication with the backend uses AJAX, see the web files at

https://github.com/alexamies/chinesenotes.com/tree/master/web-resources

FAQ

Q: Why would I use a dictionary and translation memory to translate Chinese text instead of machine translation?

A: This project is based around the idea of using linguistic assets, including a dictionary, named entity database, and translation memory, to aid in translation. To translate literature, especially classical literature and Buddhist texts, and to prepare for publishing you will need to thoroughly understand what you are the source Chinese text.

Q: Can I use the Chinese Notes Translation Portal software for my own project?

A: Yes, please do that. It is also adaptable to your own dictionary, glossary, and corpus of source text.

Architecture

This project is designed to be easy to setup with minimal dependencies but also run in a production architecture like shown below.

Various flavors are possible depending on how it is configured. It can interoperate with the other components in the Chinese Notes family

1. cnreader - Command line utility for generating indexes and HTML files for the reader
2. chinesedict-js - JavaScript package for browser module for the dictionary and text parser
3. chinesenotes-python - Pyhton utilities for Chinese text analysis
4. chinesenotes.com - Chinese-English dictionary and corpus of historic Chinese literature
5. buddhist-dictionary - Buddhist dictionary extensions to the Chinese-English dictionary and structure to present the Taisho Tripitaka as a digital library for ntireader.org

Digital Library Setup

The setup of the library requires the structure explained in <a href='https://github.com/alexamies/cnreader'

https://github.com/alexamies/cnreader. There is a small set of sample files also included in this project to facilitate getting started and testing. Set the environment variable CNWEB_HOME to the location of the library and export it. It does not have to be the home directory of the chinesenotes-go project but it can be to get started.

export CNWEB_HOME=.

To generate the static HTML files for the library and to enable mouseover and clicking of Chinese terms to find English equivalents, run the command

go run github.com/alexamies/cnreader

This generates the HTML files in the web directory.

To index the titles run

go run github.com/alexamies/cnreader -titleindex

This generates the file documents.tsv, which is used for title search. It is a flat structure transformed from their hierarchical, human-edited structure with the Chinese and English parts of the titles separated.

After running the commands above, restart the application

go run github.com/alexamies/chinesenotes-go

Go to the Library menu and search on a document title. You should see something like shown in the screenshot below.

You should be able to click on the title to follow a link to the document.

Database Setup

The prepared statements in the Go code assuming a MySQL driver. Maria is compatible with MySQL. The local development instructions assume a Mariadb database running in Docker. For full details about the database see the Mariadb Documentation.

Install Docker if you have not already.

Translation memory files

The command to download the dictionary files was given above. Generate the translation memory index files with the command

go run github.com/alexamies/cnreader -tmindex

The index files are saved in the index directory.

Mariadb Docker Image

See the documentation at Mariadb Image Documentation and Installing and using MariaDB via Docker.

To start a Docker container with Mariadb and connect to it from a MySQL command line client execute the command below. First, set environment variable MYSQL_ROOT_PASSWORD. Also, create a directory outside the container to use as a permanent Docker volume for the database files. In addition, mount volumes for the tabe separated data to be loaded into Mariadb. See Manage data in Docker and Use volumes for details on volume and mount management with Docker. In another terminal, create a Maria DB database with the commands

cd ..
mkdir mariadb
cd mariadb
MYSQL_ROOT_PASSWORD=[your password]
CNREADER_HOME=../chinesenotes-go
mkdir mariadb-data
docker run --name mariadb -p 3306:3306 \
  -e MYSQL_ROOT_PASSWORD=$MYSQL_ROOT_PASSWORD -d \
  -v "$(pwd)"/mariadb-data:/var/lib/mysql \
  --mount type=bind,source="$CNREADER_HOME"/data,target=/cndata \
  --mount type=bind,source="$CNREADER_HOME"/index,target=/cnindex \
  mariadb:10

The data in the database is persistent even if the container is deleted. To restart the database use the command

docker restart  mariadb

To load data from other sources connect to the database container or start up a mysql-client

docker exec -it mariadb bash

In the container command line

mysql --local-infile=1 -h localhost -u root -p

Load dictionary data into the database

The first time you run this execute the commands in data/first_time_setup.sql.

Create the table definitions

source cndata/chinesenotes.ddl

are in the index directory.

Load the data into the database

use cnotest_test;
LOAD DATA LOCAL INFILE 'cndata/grammar.txt' INTO TABLE grammar CHARACTER SET utf8mb4 LINES TERMINATED BY '\n';
LOAD DATA LOCAL INFILE 'cndata/topics.txt' INTO TABLE topics CHARACTER SET utf8mb4 LINES TERMINATED BY '\n';
LOAD DATA LOCAL INFILE 'cndata/words.txt' INTO TABLE words CHARACTER SET utf8mb4 LINES TERMINATED BY '\n' IGNORE 1 LINES;
LOAD DATA LOCAL INFILE 'cnindex/tmindex_uni_domain.tsv' INTO TABLE tmindex_uni_domain CHARACTER SET utf8mb4 LINES TERMINATED BY '\n';
LOAD DATA LOCAL INFILE 'cnindex/tmindex_unigram.tsv' INTO TABLE tmindex_unigram CHARACTER SET utf8mb4 LINES TERMINATED BY '\n';

Quit from the Maria DB client session

quit

Run against a databsae

Restart the web application server.

export DBUSER=app_user
export DBPASSWORD="[your password]"
export DATABASE=cnotest_test
export DBHOST=localhost
./chinesenotes-go

From the command line in a new shell you should be able to do a query like

curl http://localhost:8080/find/?query=antiquity

You should see JSON returned.

Updating the dictionary

If you add more words to the dictionary, you can update it with the SQ commands:

use cnotest_test;
DELETE FROM words;

Restart the web application.

JavaScript Integration

The web-resources directory has sufficient JavaScript for the meu system and display of vocabulary dialog in HTML pages. Building the JavaScript bundle requires installation of Node.js. The bundle is compiled from TypeScript.

To build it

cd web-resources
npm install
npm run-script build
cp dist/cnotes-compiled.* ../web/
cd ..

The JavaScript code depends on a JSON bundle of the dictionary. This requies Python 3 to build. It can be created and saved to the right location with the command

python3 bin/words2json.py "data/words.txt" web/words.json

Try starting the app, navigating to the library and clicking on a term. The JavaScript enables a popover dialog like shown below.

Deploy to Cloud Run with a Cloud SQL databsae

The steps here describe how to deploy and run on Google Cloud with Cloud Run, Cloud SQL, and Cloud Storage. This assumes that you have a Google Cloud project.

Set up a Cloud SQL Database

New: Replacing management of the Mariadb database in a Kubernetes cluster Follow instructions in Cloud SQL Quickstart using the Cloud Console.

Create a Cloud SQL instance with the Cloud Console user interface. Then log into it in the Cloud Shell with the command

INSTANCE=[your instance]
gcloud sql connect $INSTANCE --user=root

In the MySQL client create a database and add an app user with the commands in data/firt_time_setup.sql. Create the tables with the commands in chinesenotes.ddl, and add test data as needed. You can clone this Git project to get the same data files.

Cloud Storage

If you are using full text search, create a GCS bucket and copy your corpus files to it. You the environment variable TEXT_BUCKET inform the web app of the bucket name.

TEXT_BUCKET=[Your GCS bucket name]
gsutil mb gs://$TEXT_BUCKET
gsutil -m rsync -d -r corpus gs://$TEXT_BUCKET

Cloud Run

Use Cloud Build to build the Docker image and upload it to the Google Container Registry:

export PROJECT_ID=[Your project]
BUILD_ID=[your build id]
nohup gcloud builds submit --config cloudbuild.yaml . \
  --substitutions=_IMAGE_TAG="$BUILD_ID" &

Then deploy to Cloud Run with the command

IMAGE=gcr.io/${PROJECT_ID}/cn-portal-image:${BUILD_ID}
SERVICE=cn-portal
REGION=us-central1
INSTANCE_CONNECTION_NAME=[Your connection]
DBUSER=[Your database user]
DBPASSWORD=[Your database password]
DATABASE=[Your database name]
MEMORY=400Mi
TEXT_BUCKET=[Your GCS bucket name for text files]
CNWEB_HOME=.
gcloud run deploy --platform=managed $SERVICE \
--image $IMAGE \
--region=$REGION \
--memory="$MEMORY" \
--add-cloudsql-instances $INSTANCE_CONNECTION_NAME \
--set-env-vars INSTANCE_CONNECTION_NAME="$INSTANCE_CONNECTION_NAME" \
--set-env-vars DBUSER="$DBUSER" \
--set-env-vars DBPASSWORD="$DBPASSWORD" \
--set-env-vars DATABASE="$DATABASE" \
--set-env-vars TEXT_BUCKET="$TEXT_BUCKET" \
--set-env-vars CNREADER_HOME="/"

If the traffic is not migrated to the latest version run the command

gcloud run services update-traffic --platform=managed $SERVICE \
  --region=$REGION \
  --to-revisions=LATEST=100

Custom Domain and SSL

These instructions described how to setup a custom domain and SSL on Google Cloud with a Google Cloud Load Balancer and Cloud Run as a backend Network Endpoint Group (NEG). It is assumed that you already have the app deployed on Cloud Run using the instructions above.

Configuring the load balancer

Create a serverless NEG for the Cloud Run deployment

NEG=[name of NEG]
gcloud compute network-endpoint-groups create $NEG \
    --region=$REGION \
    --network-endpoint-type=serverless \
    --cloud-run-service=$SERVICE

Create a load balancer backend service

LB_SERVICE=[name of service]
gcloud compute backend-services create $LB_SERVICE --global

Add the NEG to the backend service

gcloud beta compute backend-services add-backend $LB_SERVICE \
    --global \
    --network-endpoint-group=$NEG \
    --network-endpoint-group-region=$REGION

Create a URL map as below, except if you are setting up a private portal with login required, see the instructions under password protecting the site below.

URL_MAP=[your url map name]
gcloud compute url-maps create $URL_MAP \
    --default-backend-bucket $BACKEND_BUCKET

Create a matcher for backend bucket holding cacheable content

BACKEND_CBUCKET=[your matcher name]
CBUCKET=[your bucket name]
gcloud compute backend-buckets create $BACKEND_CBUCKET \
    --gcs-bucket-name=$CBUCKET \
    --enable-cdn \
    --cache-mode=CACHE_ALL_STATIC

Create a matcher for the NEG backend service

MATCHER_NAME=[your matcher name]
gcloud compute url-maps add-path-matcher $URL_MAP \
    --default-backend-bucket $BACKEND_BUCKET \
    --path-matcher-name $MATCHER_NAME \
    --path-rules="/find/*=$LB_SERVICE,/findadvanced/*=$LB_SERVICE,/findmedia/*=$LB_SERVICE,/findsubstring=$LB_SERVICE,/findtm=$LB_SERVICE,/cached/*=$BACKEND_CBUCKET"

Enable compression for cached content:

gcloud beta compute backend-buckets update $BACKEND_CBUCKET \
    --compression-mode=AUTOMATIC

Configure the target proxy

TARGET_PROXY=[your target proxy name]
gcloud compute target-http-proxies create $TARGET_PROXY \
    --url-map $URL_MAP

STATIC_IP=[your static ip]
gcloud compute addresses create $STATIC_IP --global

FORWARDING_RULE=[your forwarding rule name]
gcloud compute forwarding-rules create $FORWARDING_RULE \
    --address $STATIC_IP \
    --global \
    --target-http-proxy $TARGET_PROXY \
    --ports 80

Check the name of the forwarding-rule and url-map

gcloud compute forwarding-rules list
gcloud compute url-maps list
gcloud compute url-maps describe $URL_MAP

At this point you should be able to access the site via the static IP.

IP=[output from command above]
curl http://${IP}

To update the load balancer

gcloud compute url-maps edit $URL_MAP

Custom domain

Register your domain name. Create an A record pointing to the static IP created above. It may take time for the DNS record to propagate.

At this point you should be able to access the site via the custom domain.

SITEDOMAIN=[output from command above]
curl http://${SITEDOMAIN}

HTTPS Setup

Create an SSL cert

SSL_CERTIFICATE_NAME=[your cert name]
gcloud compute ssl-certificates create $SSL_CERTIFICATE_NAME \
  --domains=$SITEDOMAIN \
  --global

It may take some time to provision the cert.

Create a target proxy

SSL_TARGET_PROXY=[your proxy]
gcloud compute target-https-proxies create $SSL_TARGET_PROXY \
    --url-map=$URL_MAP \
    --ssl-certificates=$SSL_CERTIFICATE_NAME

Create a forwarding rule

SSL_FORWARDING_RULE=[your forwarding rule]
gcloud compute forwarding-rules create $SSL_FORWARDING_RULE \
    --address $STATIC_IP \
    --ports=443 \
    --global \
    --target-https-proxy $SSL_TARGET_PROXY

It may take a few minutes to update the load balancer.

At this point you should be able to access the site via the custom domain with HTTPS.

curl https://${SITEDOMAIN}

Password protecting the web app

To set up the translation portal with password protection, a database and HTTPS are required. Cryptographically hashed passwords are stored in the database. Cookies are used to maintain a session.

First configure the database:

docker exec -it mariadb bash

In the container command line

mysql --local-infile=1 -h localhost -u root -p

In the mysql client give the app runtime permission to update and add an admin user

USE mysql;
GRANT SELECT, INSERT, UPDATE ON cnotest_test.* TO 'app_user'@'%';

use cnotest_test;

INSERT INTO 
  user (UserID, UserName, Email, FullName, Role, PasswordNeedsReset, Organization, Position, Location) 
VALUES (1, 'admin', "admin@email.com", "Privileged User", "admin", 0, "Test", "Developer", "Home");

INSERT INTO passwd (UserID, Password) 
VALUES (1, '[your hashed password]');

Note that the password needs to be SHA-256 hashed before inserting. If the user forgets their password, there is a password recovery function, which requires setup of a SendGrid account and generation of an API, which is stored in the env variable SENDGRID_API_KEY.

Set the environment variable PROTECTED, the SITEDOMAIN variable for cookies:

export PROTECTED=true
export SITEDOMAIN=localhost
./chinesenotes-go

Note that you need to have HTTPS enabled for cookies to be sent with most browsers. You can create a self-signed certificate, run locally without one, or deploy to a managed service like Cloud Run.

For deployment to Cloud Run use the command

PROTECTED=true
SITEDOMAIN=[your domain]
IMAGE=gcr.io/${PROJECT_ID}/cn-portal-image:${BUILD_ID}
SERVICE=cn-portal
REGION=us-central1
INSTANCE_CONNECTION_NAME=[Your connection]
DBUSER=[Your database user]
DBPASSWORD=[Your database password]
DATABASE=[Your database name]
MEMORY=400Mi
TEXT_BUCKET=[Your GCS bucket name for text files]
CNWEB_HOME=.
SENDGRID_API_KEY=[Your API key]
gcloud run deploy --platform=managed $SERVICE \
--image $IMAGE \
--region=$REGION \
--memory="$MEMORY" \
--allow-unauthenticated \
--add-cloudsql-instances $INSTANCE_CONNECTION_NAME \
--set-env-vars INSTANCE_CONNECTION_NAME="$INSTANCE_CONNECTION_NAME" \
--set-env-vars DBUSER="$DBUSER" \
--set-env-vars DBPASSWORD="$DBPASSWORD" \
--set-env-vars DATABASE="$DATABASE" \
--set-env-vars TEXT_BUCKET="$TEXT_BUCKET" \
--set-env-vars CNWEB_HOME="/" \
--set-env-vars CNREADER_HOME="/" \
--set-env-vars PROTECTED="$PROTECTED" \
--set-env-vars SITEDOMAIN="$SITEDOMAIN" \
--set-env-vars SENDGRID_API_KEY="$SENDGRID_API_KEY"\
--set-env-vars DEEPL_AUTH_KEY="$DEEPL_AUTH_KEY" \
--set-env-vars TRANSLATION_GLOSSARY="$TRANSLATION_GLOSSARY" \
--set-env-vars PROJECT_ID="$PROJECT_ID"

You will need to add the users manually using SQL statements. There is no user interface to add users yet.

For additional customization you can change the title of the portal with the Title variable in the webconfig.yaml file, edit the styles.css sheet, or edit the templates under the /templates directory. For the even more customization, just use the portal server JSON API to driver a JavaScript client.

For a custom domain, follow the instructions above, except there is no backend bucket. Instead all the static content is included in the container. So when seting up the load balancer, create the URL map as

gcloud compute url-maps create $URL_MAP \
  --default-service $LB_SERVICE

Create a matcher for the NEG backend service with the command below

MATCHER_NAME=[your matcher name]
gcloud compute url-maps add-path-matcher $URL_MAP \
    --default-service $LB_SERVICE \
    --path-matcher-name $MATCHER_NAME \
    --path-rules="/find/*=$LB_SERVICE,/findadvanced/*=$LB_SERVICE,/findmedia/*=$LB_SERVICE,/findsubstring=$LB_SERVICE,/findtm=$LB_SERVICE"

Corpus content and full text search

Full text search requires an forward and reverse indexes: The forward index is like a table of contents describing the titles and files of the text documents. There is a three level structure:

corpus
-- collection
  -- document

This gives a way of organizing a corpus of documents in a book library like structure with, say, one book per collection, one chapter per document. The structure also provides a way to construct links for users to navigate search results.

The reverse index is based on unigrams and bigrams with a BM25 formula to give the most relevant documents for a given text search. To generate the reverse index install the cnreader command line tool https://github.com/alexamies/chinesenotes.com/tree/master/go/src/cnreader

Run the command with no flags

mkdir -p web/example_collection/
mkdir -p web/analysis/example_collection
export CNREADER_HOME=.
go run github.com/alexamies/cnreader

This will write the full text index files into the index directory and marked up HTML files in the example_collection directory. Add a link in the library.html template to the page /web/texts.html. After starting the web server again you should be able to navigate to the list of texts and see Chinese words marked up with English equivalents on mouseover. You can change the config.yaml VocabFormat variable to create other markup options, including hyperlinks to dictionary pages and dialog boxes.

For full text search, you will need to load the document indexes into the database. There are sample forward index files in the data/corpus directory which can be used for testing. Load them all into the databsae with the SQL commands

use [your database];
LOAD DATA LOCAL INFILE 'cndata/corpus/collections.csv' INTO TABLE collection CHARACTER SET utf8mb4 LINES TERMINATED BY '\n' IGNORE 1 LINES;
LOAD DATA LOCAL INFILE 'cndata/corpus/example_collection.tsv' INTO TABLE document CHARACTER SET utf8mb4 LINES TERMINATED BY '\n'  IGNORE 1 LINES;;
LOAD DATA LOCAL INFILE 'cnindex/word_freq_doc.txt' INTO TABLE word_freq_doc CHARACTER SET utf8mb4 LINES TERMINATED BY '\n';
LOAD DATA LOCAL INFILE 'cnindex/bigram_freq_doc.txt' INTO TABLE bigram_freq_doc CHARACTER SET utf8mb4 LINES TERMINATED BY '\n';

If the site is password protected then copy the generated HTML files to the translation_portal directory. If the site is open then copy the HTML files to GCS with a load balancer in front, as per the instructions at https://github.com/alexamies/chinesenotes.com

Copy the plain text files to the GCS text directory with environment variable TEXT_BUCKET.

Start up the web app again and view the pages:

./chinesenotes-go

Copy the text files to an object store. The only one currently supported is Google Cloud Storage (GCS):

TEXT_BUCKET={your txt bucket}
# First time
gsutil mb gs://$TEXT_BUCKET
gsutil -m rsync -d -r corpus gs://$TEXT_BUCKET

If you are using Cloud Run then the application automatically has access to GCS. To enable the web application to access the storage system for other platforms, create a service account with a GCS Storage Object Admin role and download the JSON credentials file, as described in Create service account credentials. Assuming that you saved the file in the current working directory as credentials.json, create a local environment variable for local testing

export GOOGLE_APPLICATION_CREDENTIALS=$PWD/credentials.json

Containerize the app and run locally against a databsae

If you are not using Google Cloud, you can follow these instructions to build the Docker image for the Go application and run locally:

sudo docker build -t cn-portal-image .

Run it locally with minimal features (C-E dictionary lookp only) enabled

sudo docker run -it --rm -p 8080:8080 --name cn-portal \
  -e CNREADER_HOME="/" \
  cn-portal-image

Browse to the URL http://localhost:8080 Test basic lookup with curl

curl http://localhost:8080/find/?query=邃古

Set up the database as per the instructions at https://github.com/alexamies/chinesenotes.com Then you will be able to run it locally with all features enabled

DBUSER=app_user
DBPASSWORD="***"
DATABASE=cnotest_test
docker run -itd --rm -p 8080:8080 --name cn-app --link mariadb \
  -e DBHOST=mariadb \
  -e DBUSER=$DBUSER \
  -e DBPASSWORD=$DBPASSWORD \
  -e DATABASE=$DATABASE \
  -e SENDGRID_API_KEY="$SENDGRID_API_KEY" \
  -e GOOGLE_APPLICATION_CREDENTIALS=/cnotes/credentials.json \
  -e TEXT_BUCKET="$TEXT_BUCKET" \
  --mount type=bind,source="$(pwd)",target=/cnotes \
  cn-app-image

Test it

curl http://localhost:8080/find/?query=hello

English queries require a database connection. If everything is working ok, you should see results like 您好, 哈嘍 and other variations of hello in Chinese.

Debug

docker exec -it cn-app bash 

Stop it

docker stop cn-app

Push to Google Container Registry

docker tag cn-app-image gcr.io/$PROJECT/cn-app-image:$BUILD_ID
docker push gcr.io/$PROJECT/cn-app-image:$BUILD_ID

Go module for Chinese text processing

This GitHub project is a Go module. You can install it with the instructions at the Go module reference, which just involves importing the APIs and using them. It tries to fail gracefully if you do not have a database setup and do what it can loading from text files. The API doc is given at

https://pkg.go.dev/mod/github.com/alexamies/chinesenotes-go

Try the example below.

package main

import (
  "context"
  "fmt"
  "github.com/alexamies/chinesenotes-go/dictionary"
  "github.com/alexamies/chinesenotes-go/tokenizer"
)

func main() {
  ctx := context.Background()
  // Works even if you do not have a database
  database, err := dictionary.InitDBCon()
  if err != nil {
    fmt.Printf("unable to connect to database: \n%v\n", err)
  }
  wdict, err := dictionary.LoadDict(ctx, database)
  if err != nil {
    fmt.Printf("unable to load dictionary: \n%v", err)
    return
  }
  tokenizer := tokenizer.DictTokenizer{wdict}
  tokens := tokenizer.Tokenize("戰國時代七雄")
  for _, token := range tokens {
    fmt.Printf("token: %s\n", token.Token)
  }
}

Testing

Run unit tests with the command

go test ./... -coverprofile=coverage.out

View test coverage with the command

go tool cover -html=coverage.out

Documentation

Overview

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Web application for Chinese-English dictionary lookup, translation memory, and finding documents in a corpus. Settings in for the app are controlled through the file config.yaml, located in the project home directory, which is found through the env variable CNREADER_HOME or the present working directory.