Anno Plot

Demo

TryDemoVideo.mp4

This is a monorepo combining the repositories

• Backend: https://github.com/bruehldev/Code-Graph-Backend
• Frontend: https://github.com/jsp1999/Code-Graph-Frontend

Anno-Plot-Backend

This is a FastAPI-based API that provides topic modeling and sentence embedding functionalities using BERT models. The API supports NER dataset like few_nerd.

Requirements

• Conda for package management
• Ensure you have an NVIDIA GPU with the required drivers:

  nvidia-smi
  

• Install the build-essential packages:

  sudo apt update
  sudo apt install build-essential
  

• PostgreSQL as database.
  • Follow the official PostgreSQL download instructions here.
  • Use the following command to check if PostgreSQL is installed:

    which psql
    

• Setting Up PostgreSQL Database:
  • Access the PostgreSQL prompt:

    sudo -u postgres psql
    

    Create a new user (replace password):

    CREATE USER admin WITH PASSWORD 'password';
    

    Create Database:

    CREATE DATABASE annoplot OWNER admin;
    

    Grant Privileges:

    GRANT ALL PRIVILEGES ON DATABASE annoplot TO admin;
    

    Exit Prompt:

    \q
    

• Adapt environment (env.json) if necessary

Usage

1. Install the required packages by running the following command:

• Create:

  conda env create --name AnnoPlot --file environment.yml
  

• Activate:

  conda activate AnnoPlot
  

2. Start the server using the following command:

cd src
uvicorn main:app --reload

3. Once the server is running, you can access the API at http://localhost:8000.

4. API Endpoints: To access the API documentation use:

http://localhost:8000/docs

Datasets

Datasets used by us can be found in the datasets folder:

• movie datasets:
  • from: https://github.com/juand-r/entity-recognition-datasets/tree/master/data/MITMovieCorpus
  • normal dataset: movie_ner.txt
  • movie dataset german labels (demo): movie_ner_renamed_german.txt
  • movie dataset english labels (demo): movie_ner_renamed.txt
  • huggingface model which worked well for us: dbmdz/bert-large-cased-finetuned-conll03-english
• few-ner datasets:
  • from: https://www.kaggle.com/datasets/nbroad/fewnerd
  • small: few_ner_small.txt
  • medium: dev.txt
  • huggingface model which worked well for us: dbmdz/bert-large-cased-finetuned-conll03-english
• bio-ner datasets:
  • from: https://github.com/juand-r/entity-recognition-datasets/tree/master/data/AnEM
  • normal: bio_ner.txt
  • huggingface model which worked well for us: d4data/biomedical-ner-all
• german datasets:
  • from: https://github.com/davidsbatista/NER-datasets/tree/master/GermEval2014
  • test: NER_de_test.txt
  • train: NER_de_train.txt
  • huggingface model which worked well for us: mschiesser/ner-bert-german

Contributing

Contributions are welcome! If you have any suggestions, improvements, or bug fixes, please submit a pull request.

License

This project is licensed under the Apache License, Version 2.0.

Folder Structure

The project follows the following folder structure:
project/
├─ src/
│ ├─ main.py
│ ├─ module
│ │ └─ routes.py
│ │ └─ service.py
│ │ └─ schema.py
│ ├─ ...
├─ exported/ 
├─ .gitignore
├─ environment.yml
├─ ...

• exported/: .Used to store generated data
• .gitignore: Specifies files and folders to exclude from version control.
• main.py: The main entry point of the application.

Anno-Plot-Frontend

This is a Next.js-based web application used to display annotations and their clusters.

Requirements

Ensure that a version of Node.js is installed:

npm -v

otherwise it can be downloaded here.

Getting Started

First, ensure that you are in the frontend directory:

cd frontend

Then, install packages:

npm install

Then, run the development server:

npm run dev

Or, run build and run the production server:

npm run build

npm run start

Open http://localhost:3000 with your browser to see the result.

For full functionality ensure that the backend is running.

License

This project is licensed under the Apache License, Version 2.0.