wechaty-meme-bot 0.0.1

Wechaty-Meme-Bot is an interactive meme bot who respond interesting picture according to what he sees.

Wechaty-Meme-Bot

Preface

This project is supported by Wechaty Community and Institute of Software of Chinese Academy of Sciences.

PowerPoint Demonstration: https://www.bilibili.com/video/BV1kZ4y1M7F6/

Demo Live Video on bilibili: https://www.bilibili.com/video/BV17f4y197ut/

My community mentor is Huang, contributor of python-wechaty. I won't make such progress without his support.

Introduction

I was required to build a meme bot based on python-wechaty, which should possess at least following functions:

• receive & save meme image from specific contact
• analyse received meme image
• response meme image accordingly based on analysis given above

To achieve such requirements, I came out with a cross-functional diagram below to assist my development(written in Chinese):

Some Concepts

1. Frontend: Run on user end, be in charge of communicating with python-wechaty-puppet and backend, acting as a middleware.
2. Backend: Run on server end equipping a NVIDIA GPU, be in charge of analyzing meme image and choose response meme based on certain strategy.

Directory Layout

$ tree -L 3 -I '__pycache__'
.
├── LICENSE
├── Makefile
├── README.md
├── backend  # backend files
│   ├── chineseocr_lite  # modified OCR module
│   │   ├── Dockerfile
│   │   ├── LICENSE
│   │   ├── __init__.py
│   │   ├── angle_class
│   │   ├── config.py
│   │   ├── crnn
│   │   ├── model.py
│   │   ├── models
│   │   ├── psenet
│   │   └── utils.py
│   ├── config.yaml   # config file in yaml format
│   ├── conversation  # conversation GPT2 model path (~600MB), download from GDrive mentioned before
│   ├── cosine_metric_net.py  # definition of CosineMetricNet
│   ├── cosine_train  # CosineMetricNet Training scripts
│   │   ├── dataset.py
│   │   ├── metric.py
│   │   └── train_and_eval.py
│   ├── dataset.py  # Common training dataset module
│   ├── feature_extract.py  # feature extract module
│   ├── hanlp_wrapper.py  # NLP wrapper
│   ├── logs  # log dir
│   ├── meme  # default dir for meme import
│   │   ├── classified
│   │   ├── others
│   │   └── unclassified
│   ├── meme_importer.py
│   ├── ocr_wrapper.py
│   ├── requirements.txt
│   ├── response
│   │   ├── __init__.py
│   │   ├── conversation.py
│   │   ├── dispatcher.py
│   │   └── feature.py
│   ├── spider  # custom spider dir, any spiders should derive from BaseSpider
│   │   ├── BaseSpider.py
│   │   └── FaBiaoQingSpider.py  # example spider to crawl FaBiaoQing
│   ├── stopwords.txt  # stop words list for NLP tokenizer
│   ├── utils.py  # backend public utils
│   └── web_handler.py  # backend Flask module
├── frontend
│   ├── config.py  # frontend configuration
│   ├── image  # image cache dir
│   ├── logs  # log dir
│   ├── main.py
│   └── meme_bot.py
├── gdrive.sh   # bash to download from GDrive
├── image  # static image files
│   ├── logo.png
│   ├── summer2020.svg
│   └── wechaty-logo.svg
├── orm.py  # orm module
├── test.db   # SQLite database
└── tests  # unittests using pytest
    ├── conftest.py
    ├── test_conversation.py
    ├── test_dataset.py
    └── test_orm.py

Deploy Tutorial

git clone https://github.com/MrZilinXiao/python-wechaty-meme-bot.git

Frontend

Via PyPi (Pending...)

pip3 install wechaty-meme-bot

Manually

1.Correctly configure backend settings in frontend/config.yaml

general:
  image_temp_dir: './image'
  allow_img_extensions: ('.jpg', '.png', '.jpeg', '.gif')

backend:  # change to your backend server
  backend_upload_url: 'http://192.168.10.102:5000/meme/upload'
  backend_static_url: 'http://192.168.10.102:5000/static'

2.Run lines below in your shell:

export WECHATY_PUPPET=wechaty-puppet-hostie
export WECHATY_PUPPET_HOSTIE_TOKEN=your-donut-token   # replace `your-donut-token` with your wechaty donut token
make run-frontend
# if no `make` in your system, try run `pip3 install -r frontend/requirements.txt`, `python3 frontend/main.py`

Backend

Currently we only get backend tested on Ubuntu, while frontend possesses cross-platform feature.

You may refer to Github Action Configuration to learn how we deploy backend when you encounter issues.

Nvidia-docker

Pending...

Manually

pip3 install -r backend/requirements.txt
python backend/web_handler.py  # this will trigger chineseocrlite compiling process

Open-Source Reference

• chineseocr_lite: Powerful Chinese OCR module with accurate results and fast inference.
• HaNLP: Multilingual NLP library for researchers and companies, built on TensorFlow 2.0.
• Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
• GPT2-Chinese: Chinese version of GPT2 training code, using BERT tokenizer.

Academic Citation

# in backend/cosine_metric_net.py
[1]N. Wojke and A. Bewley, “Deep Cosine Metric Learning for Person Re-identification,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, Mar. 2018, pp. 748–756, doi: 10.1109/WACV.2018.00087.
# GPT2 Original Paper
[2]Radford, Alec, et al. "Language models are unsupervised multitask learners." OpenAI Blog 1.8 (2019): 9.