2022_1013

requirements.txt

@@ -0,0 +1,84 @@
+aiofiles==0.8.0
+altair==4.2.0
+attrs==22.1.0
+backports.zoneinfo==0.2.1
+beautifulsoup4==4.11.1
+blinker==1.5
+cachetools==5.2.0
+certifi==2022.5.18.1
+charset-normalizer==2.0.12
+ckip-transformers==0.3.2
+click==8.1.3
+commonmark==0.9.1
+conda==4.3.16
+CwnGraph==0.4.0
+CwnSenseTagger==0.1.6
+cycler==0.11.0
+decorator==5.1.1
+DistilTag==0.2.2
+entrypoints==0.4
+filelock==3.8.0
+fonttools==4.33.3
+gdown==4.5.1
+gitdb==4.0.9
+GitPython==3.1.27
+huggingface-hub==0.10.0
+idna==3.3
+importlib-metadata==4.12.0
+importlib-resources==5.9.0
+jieba==0.42.1
+Jinja2==3.1.2
+joblib==1.2.0
+jsonschema==4.16.0
+kiwisolver==1.4.2
+MarkupSafe==2.1.1
+matplotlib==3.5.2
+nltk==3.7
+numpy==1.22.4
+packaging==21.3
+pandas==1.5.0
+pdfminer==20191125
+pdfminer3k==1.3.4
+Pillow==9.1.0
+pkgutil-resolve-name==1.3.10
+ply==3.11
+protobuf==3.20.2
+pyarrow==9.0.0
+pycosat==0.6.3
+pycryptodome==3.14.1
+pydeck==0.8.0b3
+Pygments==2.13.0
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.18.1
+PySocks==1.7.1
+python-dateutil==2.8.2
+python-dotenv==0.20.0
+pytz==2022.2.1
+pytz-deprecation-shim==0.1.0.post0
+PyYAML==6.0
+regex==2022.9.13
+requests==2.27.1
+rich==12.5.1
+ruamel.yaml==0.17.21
+ruamel.yaml.clib==0.2.6
+semver==2.13.0
+six==1.16.0
+smmap==5.0.0
+snownlp==0.12.3
+soupsieve==2.3.2.post1
+streamlit==1.12.2
+tokenizers==0.12.1
+toml==0.10.2
+toolz==0.12.0
+torch==1.12.1
+tornado==6.2
+tqdm==4.64.1
+transformers==4.22.2
+typing-extensions==4.3.0
+tzdata==2022.4
+tzlocal==4.2
+urllib3==1.26.9
+validators==0.20.0
+wordcloud==1.8.1
+zipp==3.8.1

twNLP-app/.dockerignore

@@ -0,0 +1,101 @@
+.git/
+.gitignore
+
+# Docker
+docker-compose.yml
+.docker
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*/__pycache__/
+*/*/__pycache__/
+*/*/*/__pycache__/
+*.py[cod]
+*/*.py[cod]
+*/*/*.py[cod]
+*/*/*/*.py[cod]
+**/*.egg-info
+**/*.egg/
+**/*.pyc
+**/*.swp
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Virtual environment
+.env/
+.venv/
+venv/
+
+# PyCharm
+.idea
+
+# Python mode for VIM
+.ropeproject
+*/.ropeproject
+*/*/.ropeproject
+*/*/*/.ropeproject
+
+# Vim swap files
+*.swp
+*/*.swp
+*/*/*.swp
+*/*/*/*.swp
+
+# readme
+README.md
+
+# tempCodeRunnerFile
+tempCodeRunnerFile.py
+
+# pickle files
+*/*/*.pickle

twNLP-app/.gitignore

@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+*.ipynb
+
+# sqlite3 database
+*.db
+
+# tempCodeRunnerFile
+tempCodeRunnerFile.py
+
+# pickle files
+*.pickle

twNLP-app/.streamlit/config.toml

@@ -0,0 +1,8 @@
+[logger]
+level = "info"
+messageFormat = "%(asctime)s  %(message)s"
+
+[theme]
+# Primary accent for interactive elements
+primaryColor = '#228be6'
+backgroundColor = 'rgb(255, 255, 255)'

twNLP-app/Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.7-slim
+
+WORKDIR /app
+
+COPY ./Pipfile* ./
+
+RUN pip install pipenv && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends gcc python3-dev libssl-dev && \
+    pipenv install --deploy --system && \
+    apt-get remove -y gcc python3-dev libssl-dev && \
+    apt-get autoremove -y && \
+    pip uninstall pipenv -y \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY ./.streamlit ./.streamlit
+
+COPY ./src ./src
+
+EXPOSE 8501

twNLP-app/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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twNLP-app/Pipfile

@@ -0,0 +1,18 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+streamlit = "*"
+ckip-transformers = "*"
+cwngraph = "*"
+distiltag = "*"
+cwnsensetagger = "*"
+aiofiles = "*"
+python-dotenv = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.7"

twNLP-app/Pipfile.lock

@@ -0,0 +1,953 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "c3ccdc36761e5f63f4df701a2b449aae3db40ccf5b055b6fc06ba39e61001309"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.7"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "aiofiles": {
+            "hashes": [
+                "sha256:7a973fc22b29e9962d0897805ace5856e6a566ab1f0c8e5c91ff6c866519c937",
+                "sha256:8334f23235248a3b2e83b2c3a78a22674f39969b96397126cc93664d9a901e59"
+            ],
+            "index": "pypi",
+            "version": "==0.8.0"
+        },
+        "altair": {
+            "hashes": [
+                "sha256:0c724848ae53410c13fa28be2b3b9a9dcb7b5caa1a70f7f217bd663bb419935a",
+                "sha256:d87d9372e63b48cd96b2a6415f0cf9457f50162ab79dc7a31cd7e024dd840026"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.2.0"
+        },
+        "attrs": {
+            "hashes": [
+                "sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6",
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+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==22.1.0"
+        },
+        "backports.zoneinfo": {
+            "hashes": [
+                "sha256:17746bd546106fa389c51dbea67c8b7c8f0d14b5526a579ca6ccf5ed72c526cf",
+                "sha256:1b13e654a55cd45672cb54ed12148cd33628f672548f373963b0bff67b217328",
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+                "sha256:e81b76cace8eda1fca50e345242ba977f9be6ae3945af8d46326d776b4cf78d1",
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+            ],
+            "markers": "python_version < '3.9'",
+            "version": "==0.2.1"
+        },
+        "beautifulsoup4": {
+            "hashes": [
+                "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30",
+                "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==4.11.1"
+        },
+        "blinker": {
+            "hashes": [
+                "sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36",
+                "sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+            "version": "==1.5"
+        },
+        "cachetools": {
+            "hashes": [
+                "sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757",
+                "sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db"
+            ],
+            "markers": "python_version ~= '3.7'",
+            "version": "==5.2.0"
+        },
+        "certifi": {
+            "hashes": [
+                "sha256:84c85a9078b11105f04f3036a9482ae10e4621616db313fe045dd24743a0820d",
+                "sha256:fe86415d55e84719d75f8b69414f6438ac3547d2078ab91b67e779ef69378412"
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+            "markers": "python_version >= '3.5'",
+            "version": "==0.12.0"
+        },
+        "torch": {
+            "hashes": [
+                "sha256:03e31c37711db2cd201e02de5826de875529e45a55631d317aadce2f1ed45aa8",
+                "sha256:0b44601ec56f7dd44ad8afc00846051162ef9c26a8579dda0a02194327f2d55e",
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+                "sha256:69fe2cae7c39ccadd65a123793d30e0db881f1c1927945519c5c17323131437e",
+                "sha256:6cf6f54b43c0c30335428195589bd00e764a6d27f3b9ba637aaa8c11aaf93073",
+                "sha256:743784ccea0dc8f2a3fe6a536bec8c4763bd82c1352f314937cb4008d4805de1",
+                "sha256:8a34a2fbbaa07c921e1b203f59d3d6e00ed379f2b384445773bd14e328a5b6c8",
+                "sha256:976c3f997cea38ee91a0dd3c3a42322785414748d1761ef926b789dfa97c6134",
+                "sha256:9b356aea223772cd754edb4d9ecf2a025909b8615a7668ac7d5130f86e7ec421",
+                "sha256:9c038662db894a23e49e385df13d47b2a777ffd56d9bcd5b832593fab0a7e286",
+                "sha256:a8320ba9ad87e80ca5a6a016e46ada4d1ba0c54626e135d99b2129a4541c509d",
+                "sha256:b5dbcca369800ce99ba7ae6dee3466607a66958afca3b740690d88168752abcf",
+                "sha256:bfec2843daa654f04fda23ba823af03e7b6f7650a873cdb726752d0e3718dada",
+                "sha256:cd26d8c5640c3a28c526d41ccdca14cf1cbca0d0f2e14e8263a7ac17194ab1d2",
+                "sha256:e9c8f4a311ac29fc7e8e955cfb7733deb5dbe1bdaabf5d4af2765695824b7e0d",
+                "sha256:f00c721f489089dc6364a01fd84906348fe02243d0af737f944fddb36003400d",
+                "sha256:f3b52a634e62821e747e872084ab32fbcb01b7fa7dbb7471b6218279f02a178a"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==1.12.1"
+        },
+        "tornado": {
+            "hashes": [
+                "sha256:1d54d13ab8414ed44de07efecb97d4ef7c39f7438cf5e976ccd356bebb1b5fca",
+                "sha256:20f638fd8cc85f3cbae3c732326e96addff0a15e22d80f049e00121651e82e72",
+                "sha256:5c87076709343557ef8032934ce5f637dbb552efa7b21d08e89ae7619ed0eb23",
+                "sha256:5f8c52d219d4995388119af7ccaa0bcec289535747620116a58d830e7c25d8a8",
+                "sha256:6fdfabffd8dfcb6cf887428849d30cf19a3ea34c2c248461e1f7d718ad30b66b",
+                "sha256:87dcafae3e884462f90c90ecc200defe5e580a7fbbb4365eda7c7c1eb809ebc9",
+                "sha256:9b630419bde84ec666bfd7ea0a4cb2a8a651c2d5cccdbdd1972a0c859dfc3c13",
+                "sha256:b8150f721c101abdef99073bf66d3903e292d851bee51910839831caba341a75",
+                "sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac",
+                "sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e",
+                "sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==6.2"
+        },
+        "tqdm": {
+            "hashes": [
+                "sha256:40be55d30e200777a307a7585aee69e4eabb46b4ec6a4b4a5f2d9f11e7d5408d",
+                "sha256:74a2cdefe14d11442cedf3ba4e21a3b84ff9a2dbdc6cfae2c34addb2a14a5ea6"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==4.64.0"
+        },
+        "transformers": {
+            "hashes": [
+                "sha256:07f3df80144f7f032ad5d367507445980d4aa25855a5d658cfa47ac5fff32aca",
+                "sha256:c03c150857e2d8f18f6dcb51e3061207522425ad57a0a6dc6cde407226a45e3e"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.21.2"
+        },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:25642c956049920a5aa49edcdd6ab1e06d7e5d467fc00e0506c44ac86fbfca02",
+                "sha256:e6d2677a32f47fc7eb2795db1dd15c1f34eff616bcaf2cfb5e997f854fa1c4a6"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.3.0"
+        },
+        "tzdata": {
+            "hashes": [
+                "sha256:21f4f0d7241572efa7f7a4fdabb052e61b55dc48274e6842697ccdf5253e5451",
+                "sha256:c3119520447d68ef3eb8187a55a4f44fa455f30eb1b4238fa5691ba094f2b05b"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==2022.2"
+        },
+        "tzlocal": {
+            "hashes": [
+                "sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745",
+                "sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==4.2"
+        },
+        "urllib3": {
+            "hashes": [
+                "sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e",
+                "sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' and python_version < '4'",
+            "version": "==1.26.12"
+        },
+        "validators": {
+            "hashes": [
+                "sha256:24148ce4e64100a2d5e267233e23e7afeb55316b47d30faae7eb6e7292bc226a"
+            ],
+            "markers": "python_version >= '3.4'",
+            "version": "==0.20.0"
+        },
+        "zipp": {
+            "hashes": [
+                "sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2",
+                "sha256:47c40d7fe183a6f21403a199b3e4192cca5774656965b0a4988ad2f8feb5f009"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==3.8.1"
+        }
+    },
+    "develop": {}
+}

twNLP-app/README.md

@@ -0,0 +1,45 @@
+# **ckip-cwn-app**
+中文很適合拿來探索自然語言處理管線的標記語言。這個探索過程也可反應生成語言架構多年來牽制語言理論，以及它對於自然語言處理與理解的想像影響。
+
+## **Documentation**
+### 1. Installation
+
+1. Python version
+   * `python == 3.7.5`
+
+2. Clone repository
+
+    ```bash
+    git clone [email protected]:lopentu/nlp_web.git
+    ```
+
+3. Install Requirement
+    ```bash
+    cd nlp_web/assignments/twNLP-app && pip install -r requirements.txt      
+    ```
+
+
+### 2. Start the app
+There are two main ways to run the app:
+
+- run with Python 
+
+  First make sure you are in the `twNLP-app` folder, and then simply run:
+
+  ```bash 
+  streamlit run src/app.py
+  ```
+
+- run with Docker
+  
+  Install Docker, and user the following command to run:
+  
+  ```bash
+  docker-compose up
+  ```
+
+  Then acces `http://localhost` in the browser.
+
+
+## Contact
+If you have any suggestion or question, please do not hesitate to email us at [email protected] or [email protected]

twNLP-app/deployment/nginx.conf

@@ -0,0 +1,30 @@
+worker_processes auto;
+
+events {
+    worker_connections 1024;
+    multi_accept on;
+}
+
+http {
+    map $http_upgrade $connection_upgrade {
+        default upgrade;
+        '' close;
+    }
+
+    upstream app {
+        server app:8501;
+    }
+
+    server {
+        listen 80;
+        charset utf-8;
+
+        location / {
+            proxy_pass http://app;
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection $connection_upgrade;
+            auth_basic off;
+        }
+    }
+}

twNLP-app/docker-compose.yml

@@ -0,0 +1,20 @@
+version: "3"
+
+services:
+  app:
+    build: ./
+    ports:
+      - 8501:8501
+    command: "streamlit run ./src/app.py"
+
+  nginx:
+    image: "nginx:stable"
+    depends_on:
+      - app
+    links:
+      - app
+    restart: always
+    ports:
+      - 80:80
+    volumes:
+      - "./deployment/nginx.conf:/etc/nginx/nginx.conf"

twNLP-app/requirements.txt

@@ -0,0 +1,7 @@
+aiofiles==0.8.0
+cwngraph==0.4.0
+distiltag==0.2.2
+streamlit==1.12.2
+python-dotenv==0.20.0
+cwnsensetagger==0.1.6
+ckip-transformers==0.3.2

twNLP-app/src/app.py

@@ -0,0 +1,111 @@
+#R11142005 紀柔安
+import streamlit as st
+from views.components.spinner import dowload_ckip_package, download_cwn_drivers
+import pandas as pd
+import requests 
+import bs4
+from snownlp import SnowNLP
+
+
+def run_app(ckip_nlp_models, cwn_upgrade) -> None:
+    # need to download first because CWN packages will first check whether
+    # there is .cwn_graph folder in the root directory.
+    download_cwn_drivers(cwn_upgrade)
+    dowload_ckip_package(ckip_nlp_models)
+
+    from views.components.sidebar import visualize_side_bar
+    from views.containers import display_cwn, display_ckip, display_data_form
+
+    st.title("NLP app for PTT")
+    st.write("這是一個針對PTT語料的 情緒分析｜中文NLP管線處理🔎")
+    st.image("/Users/joannechi/nlpWeb/myApp/nlpweb/nlp_assignment_1/img/Mo-PTT-Logo.png", width=200)
+
+    #menu = ["Text","Sentences"]
+    #choice = st.sidebar.selectbox("Menu",menu) 
+
+
+    #spectra = st.file_uploader("upload your file", type={"csv", "txt"})
+    #if spectra is not None:
+    #    spectra_df = pd.read_csv(spectra) #讀取csv
+    #    st.write(spectra_df)
+
+    #~~web crawler~~
+    st.subheader("PTT Crawler 🐛")
+    st.text('目前看板有：HatePolitics｜Gossiping｜Military｜Stock')
+    selected = st.selectbox('請選擇看板：', 
+      ['HatePolitics', 'Gossiping','Military','Stock']) 
+    if selected=='HatePolitics':
+        URL = "https://www.ptt.cc/bbs/HatePolitics/index.html"
+    elif selected=='Gossiping':
+        URL = "https://www.ptt.cc/bbs/Gossiping/index.html"
+    elif selected=='Military':
+        URL = "https://www.ptt.cc/bbs/Military/index.html"
+    else:
+        URL = "https://www.ptt.cc/bbs/Stock/index.html"
+
+    my_headers = {'cookie': 'over18=1;'}
+    response = requests.get(URL, headers = my_headers)
+    soup = bs4.BeautifulSoup(response.text,"html.parser")
+    list_results=[]
+    for t in soup.find_all('div','title'):
+        find_a=t.find('a')
+        find_href="https://www.ptt.cc"+find_a.get("href")
+        title=t.text
+        results={
+            "title":title,
+            "url":find_href
+        }
+        list_results.append(results)        
+    my_df=pd.DataFrame(list_results)
+    print(my_df)
+    st.write(my_df)
+    #~~web crawler~~
+
+    #~~sentiment analysis~~
+    st.subheader("情緒分析")
+    with st.form(key="nlpForm"):
+        raw_text=st.text_area("請輸入句子✏️")
+        submit_button=st.form_submit_button(label="確定")
+        
+    if submit_button:
+        
+        st.info("sentiment")
+        sentiment=SnowNLP(SnowNLP(raw_text).han) #轉簡體
+        sentiment_han=sentiment.sentiments
+        st.write(sentiment_han)
+
+            #emoji
+        if sentiment_han>0:
+            st.markdown("Sentiment:: Positive :smiley: ")
+        elif sentiment_han<0:
+            st.markdown("Sentiment:: Negative :angry: ")
+        else:
+            st.markdown("Sentiment:: Neutral :neutral: ")
+
+
+
+        #with col2:
+            #st.info("category")
+            #category=SnowNLP(SnowNLP(raw_text).han) #轉簡體
+            #category_han=list(category.tags)
+            #st.write(category_han)
+
+    #~~sentiment analysis~~
+
+    st.subheader("中文 NLP 管線處理")
+
+    input_data = display_data_form()
+    model, pipeline, active_visualizers = visualize_side_bar(ckip_nlp_models) 
+                                        #return model_options, pipeline_options, active_visualizers
+
+    display_factories = {"CKIP": display_ckip, "CWN": display_cwn}
+
+    if "input_data" in st.session_state:
+        display_factories[pipeline](
+            model, active_visualizers, st.session_state["input_data"]
+        )
+
+
+if __name__ == "__main__":
+    ckip_nlp_models = ["bert-base", "albert-tiny", "bert-tiny", "albert-base"]
+    run_app(ckip_nlp_models, cwn_upgrade=False)

twNLP-app/src/configs/__init__.py

@@ -0,0 +1,10 @@
+from .ckip import ckip_path, download_ckip_drivers
+from .cwn import cwn_model_path, download_cwn_models
+
+
+__all__ = [
+    "ckip_path",
+    "download_ckip_drivers",
+    "cwn_model_path",
+    "download_cwn_models",
+]

twNLP-app/src/configs/ckip.py

@@ -0,0 +1,36 @@
+import pickle
+import asyncio
+import aiofiles
+from pathlib import Path
+
+
+pkg_path = Path("__file__").resolve().parent / "src"
+ckip_path = pkg_path / "models" / "ckip"
+
+
+async def write_drivers(nlp_model: str) -> None:
+    """The write drivers function writes the ckip drivers to pickle files asynchronously.
+    Args:
+        nlp_model (str): the nlp model name
+    """
+    from ckip_transformers.nlp import (
+        CkipWordSegmenter,
+        CkipPosTagger,
+        CkipNerChunker,
+    )
+
+    drivers = (
+        CkipWordSegmenter(model=nlp_model),
+        CkipPosTagger(model=nlp_model),
+        CkipNerChunker(model=nlp_model),
+    )
+
+    driver_path = ckip_path / f"{nlp_model}_drivers.pickle"
+    async with aiofiles.open(driver_path, mode="wb") as file:
+        result = pickle.dumps(drivers)
+        await file.write(result)
+        print(f"{nlp_model}_drivers.pickle done!")
+
+
+async def download_ckip_drivers(ckip_nlp_models):
+    await asyncio.gather(*list(map(write_drivers, ckip_nlp_models)))

twNLP-app/src/configs/cwn.py

@@ -0,0 +1,14 @@
+from pathlib import Path
+from typing import Optional
+
+
+cwn_model_path = Path.home().resolve() / ".cwn_graph"
+
+
+def download_cwn_models(upgrade: Optional[bool] = False):
+    import CwnSenseTagger, DistilTag
+    from CwnGraph import CwnImage
+
+    DistilTag.download(upgrade=upgrade)
+    CwnSenseTagger.download(upgrade=upgrade)
+    CwnImage.latest()

twNLP-app/src/context/__init__.py

@@ -0,0 +1,5 @@
+from .wsg import use_WSG
+from .actions import WSGKind
+
+
+__all__ = ["use_WSG", "WSGKind"]

twNLP-app/src/context/actions.py

@@ -0,0 +1,30 @@
+from typing import Any
+from enum import Enum, auto
+from types import SimpleNamespace
+from dataclasses import dataclass, FrozenInstanceError
+
+
+class WSGKind(Enum):
+    ADD_WSG = auto()
+    RESET = auto()
+
+
+class FrozenSimpleNamespace(SimpleNamespace):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        raise FrozenInstanceError(f"cannot assign to field '{name}'")
+
+
+@dataclass(frozen=True)
+class Action:
+    """
+    The Action object contains the payload of information.
+    """
+
+    kind: WSGKind
+    payload: dict
+
+    def __post_init__(self):
+        super().__setattr__("payload", FrozenSimpleNamespace(**self.payload))

twNLP-app/src/context/reducer.py

@@ -0,0 +1,13 @@
+from typing import Any, Union
+from .actions import WSGKind, Action
+
+
+def reducer(state, action: Action) -> Union[str, Any]:
+    """The reducer function generates new states."""
+
+    initial_state = (state != None) if state else ""
+
+    if action["kind"] == WSGKind.ADD_WSG:
+        return action["payload"]
+
+    return initial_state

twNLP-app/src/context/wsg.py

@@ -0,0 +1,19 @@
+from .reducer import reducer
+from utils.stores import Store
+from typing import Callable, List, Union
+
+wsg_store = None
+
+
+def return_value():
+    return wsg_store.get_state()
+
+
+wsg_store = Store(reducer)
+
+wsg_store.add_listener(return_value)
+
+
+def use_WSG() -> List[Union[str, Callable]]:
+    """The use_WSG function contains wsg result and and wsg dispatcher."""
+    return [wsg_store.get_state(), wsg_store.dispatch]

twNLP-app/src/controllers/ckip/__init__.py

@@ -0,0 +1,6 @@
+from .ner import handle_create_ner
+from .pos import handle_create_pos
+from .wsg import handle_create_wsg
+
+
+__all__ = ["handle_create_ner", "handle_create_pos", "handle_create_wsg"]

twNLP-app/src/controllers/ckip/ner.py

@@ -0,0 +1,32 @@
+import asyncio
+from typing import List, Union
+from models import connect_ckip_drivers
+from ckip_transformers.nlp.util import NerToken
+from utils.ckip.ner import chunk_multiple_entities
+
+
+def is_list_of_empty_list(ner_token_list: List[Union[NerToken, None]]) -> bool:
+    """The is_list_of_empty_list function checks whether a list is full of empty lists.
+    Args:
+        ner_token_list (list): the result of the ner driver
+    Returns:
+        a bool
+    """
+    return all(map(lambda value: not value, ner_token_list))
+
+
+def handle_create_ner(nlp_model: str, sentence_list: List[str]) -> List[str]:
+    """The handle_create_ner function handles the request that deals with NER.
+    Args:
+        nlp_model (str): the nlp model name
+        sentence_list (list): a list of sentences
+    Returns:
+        a list of strings
+    """
+    ner_driver = connect_ckip_drivers(nlp_model)[2]
+    ner_token_list = ner_driver(sentence_list)
+
+    if is_list_of_empty_list(ner_token_list):
+        return sentence_list
+
+    return asyncio.run(chunk_multiple_entities(zip(sentence_list, ner_token_list)))

twNLP-app/src/controllers/ckip/pos.py

@@ -0,0 +1,18 @@
+import asyncio
+from typing import List
+from context import use_WSG
+from utils.ckip.pos import PosTagging
+from utils.text import add_multiple_textsubscripts
+
+
+def handle_create_pos(nlp_model: str, sentence_list: List[str]):
+    """The handle_create_pos function handles the request that deals with pos-tagging.
+    Args:
+        sentence_list (list): a list of sentences
+    Returns:
+        a list of strings
+    """
+
+    ws_result = use_WSG()[0]
+    segmented_result = PosTagging(nlp_model, ws_result).tag()
+    return asyncio.run(add_multiple_textsubscripts("pos", segmented_result))

twNLP-app/src/controllers/ckip/wsg.py

@@ -0,0 +1,20 @@
+import asyncio
+from typing import List
+from context import use_WSG, WSGKind
+from utils.ckip.wsg import WordSegmentation
+from utils.text import add_multiple_textsubscripts
+
+
+def handle_create_wsg(nlp_model: str, sentence_list: List[str]) -> List[str]:
+    """The handle_create_wsg function handles the request that deals with word
+    segmentation.
+    Args:
+        sentence_list (list): a list of sentences
+    Returns:
+        a list of strings
+    """
+
+    ws_result = WordSegmentation(nlp_model, sentence_list).segment()
+    dispatch = use_WSG()[1]
+    dispatch({"kind": WSGKind.ADD_WSG, "payload": ws_result})
+    return asyncio.run(add_multiple_textsubscripts("ws", ws_result))

twNLP-app/src/controllers/cwn/__init__.py

@@ -0,0 +1,4 @@
+from .cwn_sense_tag import handle_create_cwn_tags
+
+
+__all__ = ["handle_cwn_sense_tag"]

twNLP-app/src/controllers/cwn/cwn_sense_tag.py

@@ -0,0 +1,25 @@
+import asyncio
+from typing import List
+from utils.text import add_multiple_textsubscripts
+from utils.cwn import disambiguate_word_sense, create_cwn_sense_tags
+
+
+async def create_tags(segmented_result: List[str]):
+    """The create_tags function runs two asynchronous operations (i.e.
+    `add_multiple_textsubscripts` and `create_cwn_sense_tags`).
+    Args:
+        segmented_result (list)
+    """
+
+    return await asyncio.gather(
+        *[
+            add_multiple_textsubscripts("cwn", segmented_result),
+            create_cwn_sense_tags(segmented_result),
+        ]
+    )
+
+
+def handle_create_cwn_tags(sentence_list: List[str]) -> List[str]:
+    segmented_result = asyncio.run(disambiguate_word_sense(sentence_list))
+    span_tags, cwn_tags = asyncio.run(create_tags(segmented_result))
+    return span_tags, cwn_tags

twNLP-app/src/models/__init__.py

@@ -0,0 +1,4 @@
+from .ckip import connect_ckip_drivers
+
+
+__all__ = ["connect_ckip_drivers"]

twNLP-app/src/models/ckip/__init__.py

@@ -0,0 +1,4 @@
+from .ckip import connect_ckip_drivers
+
+
+__all__ = ["connect_ckip_drivers"]

twNLP-app/src/models/ckip/ckip.py

@@ -0,0 +1,17 @@
+import pickle
+from configs import ckip_path
+
+
+def connect_ckip_drivers(nlp_model: str) -> tuple:
+    """The connect_ckip_drivers function connects to the ckip drivers.
+
+    Args:
+        nlp_model (str): the nlp model name
+    Returns:
+        a tuple, containing CkipWordSegmenter, CkipPosTagger and CkipNerChunker.
+    """
+
+    driver_path = ckip_path / f"{nlp_model}_drivers.pickle"
+
+    with open(driver_path, "rb") as file:
+        return pickle.load(file)

twNLP-app/src/utils/ckip/ner/__init__.py

@@ -0,0 +1,4 @@
+from .chunker import chunk_entities, chunk_multiple_entities
+
+
+__all__ = ["chunk_entities", "chunk_multiple_entities"]

twNLP-app/src/utils/ckip/ner/chunker.py

@@ -0,0 +1,13 @@
+import asyncio
+from .ner import create_ner
+from typing import List, Tuple, Union
+from ckip_transformers.nlp.util import NerToken
+
+
+async def chunk_entities(zip_value: Tuple[Union[str, NerToken]]) -> str:
+    sentence, ner_token_list = zip_value
+    return create_ner(sentence, ner_token_list)
+
+
+async def chunk_multiple_entities(data: zip) -> List[str]:
+    return await asyncio.gather(*list(map(chunk_entities, data)))

twNLP-app/src/utils/ckip/ner/ner.py

@@ -0,0 +1,67 @@
+from functools import lru_cache
+from ...text import create_entity_color
+from typing import List, Tuple, Optional
+from ckip_transformers.nlp.util import NerToken
+
+
+def add_textsubscript(ner_token_list: List[NerToken]) -> Tuple[Tuple[str]]:
+    """The add_textsubscript function combines the token word and the
+    NER-tag, and specifies the NER-tag to be displayed as subscript.
+    Args:
+        ner_token_list (NerToken): a list of NerToken
+    Returns:
+        a tuple: (
+            ("<span>傅達仁<sub style='margin-right: 0.1rem'>PERSON</sub></span>", (0, 3))
+            ...
+        )
+    """
+
+    combine = lambda value: (
+        f"<span style='color: {create_entity_color(value.ner)};'>{value.word}<sub style='margin-right: 0.6rem'>{value.ner}</sub></span>",
+        value.idx,
+    )
+    return tuple(map(combine, ner_token_list))
+
+
+@lru_cache(maxsize=None)
+def modify_sentence(
+    span_tuple: Tuple[Tuple[str]], sentence: str, increased_len: Optional[int] = 0
+) -> str:
+    if len(list(span_tuple)) == 1:
+        span_list = list(span_tuple)
+        modified_word, index = span_list[0]
+        start_index, end_index = index
+        start_index += increased_len
+        end_index += increased_len
+        return "".join((sentence[:start_index], modified_word, sentence[end_index:]))
+
+    span_list = list(span_tuple)
+    modified_word, index = span_list.pop(0)
+    span_tuple = tuple(span_list)
+
+    start_index, end_index = index
+
+    if increased_len:
+        start_index += increased_len
+        end_index += increased_len
+
+    original_word = sentence[start_index:end_index]
+    modified_sentence = "".join(
+        (sentence[:start_index], modified_word, sentence[end_index:])
+    )
+
+    index_gap = len(modified_word) - len(original_word)
+    return modify_sentence(span_tuple, modified_sentence, increased_len + index_gap)
+
+
+def create_ner(sentence: str, ner_token_list: List[NerToken]) -> str:
+    """The replace_entities function replaces words that are recognized
+    as the token words with opening and closing span tags.
+    Args:
+        sentence (str): the orignal sentence
+        ner_token_list (NerToken): a list of NerToken
+    Returns:
+        a str
+    """
+    modified_ner_token_list = add_textsubscript(ner_token_list)
+    return modify_sentence(modified_ner_token_list, sentence)

twNLP-app/src/utils/ckip/pos/__init__.py

@@ -0,0 +1,4 @@
+from .pos import PosTagging
+
+
+__all__ = ["PosTagging"]

twNLP-app/src/utils/ckip/pos/pos.py

@@ -0,0 +1,38 @@
+from typing import List, Union
+from dataclasses import dataclass
+from models import connect_ckip_drivers
+
+
+@dataclass
+class PosTagging:
+    """
+    The PosTagging object marks a word in `ws_result` as corresponding to a particular part of speech.
+    """
+
+    nlp_model: str
+    ws_result: List[Union[List[None], List[str]]]
+
+    def __post_init__(self) -> None:
+        self.pos_driver = connect_ckip_drivers(self.nlp_model)[1]
+
+    def pack_ws_pos_sentece(self, ws_pos_pair: tuple) -> List[tuple]:
+        """The pack_ws_pos_sentece method packs both words and thier part-of-speech to a pair.
+        Args:
+            ws_pos_pair (tuple): the pair of a word and its corresponding part-of-speech
+        Returns:
+            a list of tuples: [
+                ('我', 'Nh'),
+                ('喜歡', 'VK'),
+                ('程式', 'Na')
+            ]
+        """
+
+        sentence_ws, sentence_pos = ws_pos_pair
+        assert len(sentence_ws) == len(sentence_pos)
+        return list(
+            map(lambda word_pos_pair: word_pos_pair, zip(sentence_ws, sentence_pos))
+        )
+
+    def tag(self):
+        pos_pipeline = self.pos_driver(self.ws_result, use_delim=True)
+        return list(map(self.pack_ws_pos_sentece, zip(self.ws_result, pos_pipeline)))

twNLP-app/src/utils/ckip/wsg/__init__.py

@@ -0,0 +1,4 @@
+from .wsg import WordSegmentation
+
+
+__all__ = ["WordSegmentation"]

twNLP-app/src/utils/ckip/wsg/wsg.py

@@ -0,0 +1,40 @@
+from typing import List, Union
+from dataclasses import dataclass
+from models import connect_ckip_drivers
+
+
+@dataclass
+class WordSegmentation:
+    """
+    The WordSegmentation object divides written text in `sentence_lists` into meaningful units.
+    """
+
+    nlp_model: str
+    sentence_list: List[str]
+
+    def __post_init__(self) -> None:
+        self.ws_driver = connect_ckip_drivers(self.nlp_model)[0]
+
+    def remove_empty_string(self, sentence_list: List[str]) -> List[str]:
+        """The remove_empty_string method removes empty string in `sentence_list`.
+        Args:
+            sentence_list (list)
+        Returns:
+            a list
+        """
+        return list(filter(lambda value: value is not "", sentence_list))
+
+    def segment(self) -> List[Union[List[None], List[List[str]]]]:
+        """The segment method divides written text in `sentence_lists` into meaningful units.
+        Returns:
+            a list of splitting text, an empty list otherwise.
+        """
+        invalid_list = not self.sentence_list or all(
+            [value == "" for value in self.sentence_list]
+        )
+
+        if invalid_list:
+            return self.sentence_list
+
+        filtered_list = self.remove_empty_string(self.sentence_list)
+        return self.ws_driver(filtered_list, use_delim=True)

twNLP-app/src/utils/cwn/__init__.py

@@ -0,0 +1,4 @@
+from .tagger import disambiguate_word_sense, create_cwn_sense_tags
+
+
+__all__ = ["disambiguate_word_sense", "create_cwn_sense_tags"]

twNLP-app/src/utils/cwn/tagger.py

@@ -0,0 +1,39 @@
+import asyncio
+from typing import List, Tuple
+from DistilTag import DistilTag
+from CwnSenseTagger import senseTag
+
+
+# --------------------------------------------------------------------
+# distil tag
+
+tagger = DistilTag()
+
+
+async def tag_values(value):
+    """The tag_values function makes `tagger.tag` function an asynchronous function."""
+    return tagger.tag(value)
+
+
+async def disambiguate_word_sense(sentence_list: List[str]) -> List[Tuple[str]]:
+    """The disambiguate_word_sense function disambiuates the word sense.
+    Args:
+        sentence_list (list): a list of sentences
+    Returns:
+        a list of tuples.
+    """
+
+    return await asyncio.gather(*list(map(tag_values, sentence_list)))
+
+
+# --------------------------------------------------------------------
+# CWN sense tag
+
+
+async def sense_value(value):
+    """The sense_value function makes `senseTag` function an asynchronous function."""
+    return senseTag(value)
+
+
+async def create_cwn_sense_tags(disambiguated_list: List[List[List[tuple]]]):
+    return await asyncio.gather(*list(map(sense_value, disambiguated_list)))

twNLP-app/src/utils/stores/__init__.py

@@ -0,0 +1,4 @@
+from .store import Store
+
+
+__all__ = ["Store"]

twNLP-app/src/utils/stores/store.py

@@ -0,0 +1,48 @@
+from dataclasses import dataclass
+from typing import Any, Callable, Dict
+
+
+@dataclass
+class Store:
+    """
+    The Store object keeps tracks of changes and generates new states via a reducer.
+    """
+
+    reducer: Callable
+
+    def __post_init__(self):
+        if callable(self.reducer) != True:
+            raise ValueError("Expecting a callable reducer function")
+
+        self.__states = None
+        self.__listeners = list()
+        self.__reducer = self.reducer
+
+    def dispatch(self, action: Dict[str, Any]):
+        if type(action) != dict:
+            raise ValueError("Expecting action to be of type dictionary")
+
+        has_kind = "kind" in action
+
+        if not has_kind:
+            raise ValueError("Action is expected to have an attribute 'kind'")
+
+        currentStates = None
+
+        if type(self.__states) == dict:
+            currentStates = self.__states.copy()
+
+        self.__states = self.__reducer(currentStates, action)
+        self.__emitListeners()
+        pass
+
+    def __emitListeners(self):
+        for listener in self.__listeners:
+            listener()
+
+    def add_listener(self, callback):
+        if callable(callback):
+            self.__listeners.append(callback)
+
+    def get_state(self):
+        return self.__states

twNLP-app/src/utils/text/__init__.py

@@ -0,0 +1,5 @@
+from .textsubscript import add_multiple_textsubscripts
+from .text_color import create_entity_color, create_pos_color
+
+
+__all__ = ["add_multiple_textsubscripts", "create_entity_color", "create_pos_color"]

twNLP-app/src/utils/text/text_color.py

@@ -0,0 +1,57 @@
+def create_pos_color(pos: str) -> str:
+    """The create_color function creats a text color base on the `pos` type.
+    Args:
+        pos (str): part of speech
+    Returns:
+        a str (e.g. 'rgb(255,0,0)' or '#ff0000')
+    """
+
+    if len(pos) >= 4:
+        return "rgb(102, 102, 102)"
+
+    pos_color_factories = {
+        "A": "rgb(21, 170, 191)",
+        "C": "rgb(231, 41, 138)",
+        "D": "rgb(117, 112, 179)",
+        "I": "rgb(102, 166, 30)",
+        "N": "rgb(27, 158, 119)",
+        "P": "rgb(102, 166, 30)",
+        "S": "rgb(166, 118, 29)",
+        "T": "rgb(102, 166, 30)",
+        "V": "rgb(217, 95, 2)",
+        "F": "rgb(230, 171, 2)",
+    }
+
+    return pos_color_factories[pos[0]]
+
+
+def create_entity_color(entity: str):
+    """The create_color function creats a text color base on the `entity` type.
+    Args:
+        entity (str): an entity type from the result of NER
+    Returns:
+        a str (e.g. 'rgb(255,0,0)' or '#ff0000')
+    """
+
+    entity_color_factories = {
+        "GPE": "rgb(102, 166, 30)",
+        "PERSON": "rgb(166, 118, 29)",
+        "DATE": "rgb(217, 95, 2)",
+        "ORG": "rgb(102, 166, 30)",
+        "CARDINAL": "rgb(27, 158, 119)",
+        "ORDINAL": "rgb(231, 41, 138)",
+        "NORP": "rgb(117, 112, 179)",
+        "LOC": "rgb(27, 158, 119)",
+        "TIME": "rgb(117, 112, 179)",
+        "FAC": "rgb(231, 41, 138)",
+        "MONEY": "rgb(217, 95, 2)",
+        "ORDINAL": "rgb(231, 41, 138)",
+        "EVENT": "rgb(117, 112, 179)",
+        "WORK_OF_ART": "rgb(231, 41, 138)",
+        "QUANTITY": "rgb(217, 95, 2)",
+        "PERCENT": "rgb(230, 171, 2)",
+        "LANGUAGE": "rgb(230, 171, 2)",
+        "PRODUCT": "rgb(27, 158, 119)",
+        "LAW": "rgb(166, 118, 29)",
+    }
+    return entity_color_factories[entity]

twNLP-app/src/utils/text/textsubscript.py

@@ -0,0 +1,39 @@
+import asyncio
+from itertools import chain
+from typing import List, Union
+from .text_color import create_pos_color
+
+
+async def add_pos_textsubscript(data_list: List[str]) -> str:
+
+    create = (
+        lambda value: f"""<span>{value[0]}<sub style='margin-right: 0.7rem; color: {create_pos_color(value[1])};'>({value[1]})</sub></span>"""
+    )
+    return "".join(list(map(create, data_list)))
+
+
+async def add_ws_textsubscript(data_list: List[str]) -> str:
+    create = lambda value: f"<span style='margin-right: 1rem'>{value}</span>"
+    return "".join(list(map(create, data_list)))
+
+
+async def add_cwn_textsubscript(segmented_list) -> str:
+    create = (
+        lambda value: f"<span>{value[0]}<sub style='margin-right: 1rem; color: {create_pos_color(value[1])};'>{value[1]}</sub></span>"
+    )
+    return "".join(list(map(create, chain(*segmented_list))))
+
+
+async def add_multiple_textsubscripts(
+    target: str, list_of_lists: List[List[Union[str, tuple]]]
+) -> List[str]:
+
+    textsubscript_factories = {
+        "ws": add_ws_textsubscript,
+        "pos": add_pos_textsubscript,
+        "cwn": add_cwn_textsubscript,
+    }
+
+    return await asyncio.gather(
+        *list(map(textsubscript_factories[target], list_of_lists))
+    )

twNLP-app/src/views/components/form/__init__.py

@@ -0,0 +1,4 @@
+from .form import form_controller
+
+
+__all__ = ["form_controller"]