This is an early alpha version of the DD-IDDE SDK. It is used in combination with the DeepPavlov's DD-IDDE available here.
pip install -r requirements.txt
python -m spacy download en_core_web_sm
# install df_engine
pip install dff
Item | Requirements | Comments |
---|---|---|
OS | Debian-based distribution, e.g., Ubuntu or Windows 10 | This version was tested on Ubuntu 18.04 under WSL2 on Windows 11 and Windows 10. |
Python | v3.9+ | This version was tested on OS with Python 3.9. |
Docker | v20+ | This version was tested with Docker v20.10.7 (64-bit). |
Docker-Compose | v1.29.2 | This version was tested with Docker-Compose v1.29.2. |
- DD-IDDE
- Python
- Docker
- Remote - WSL
If needed, set your WSL-based terminal app as the default one in your VS Code by following these instructions.
- Install the python3.9 package using apt-get
sudo apt-get install python3.9
Add Python3.6 & Python 3.9 to update-alternatives
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2
Update Python 3 to point to Python 3.9:
sudo update-alternatives --config python3
Enter 2 for Python 3.9
Test the version of python:
python3 --version
Python 3.9
Test the version of python used by pip3 command:
pip3 --version
pip 21.3.1 from /home/danielko/.local/lib/python3.9/site-packages/pip (python 3.9)
pip3 install lxml
We use Dialog Flow Engine as the runtime for the open-domain/scenario-driven chatbots.
Follow these instructions to install Dialog Flow Engine:
# install df_engine
pip install dff
Follow these requirements to prepare DD-IDDE SDK to run on your machine:
pip install -r requirements.txt
We use our Speech Functions Classifier & Predictor from our larger DeepPavlov Dream Multiskill AI Assistant Platform.
Follow these instructions to run the Discourse Moves Recommendation System:
docker-compose up -d --build
After that sf predictor is available on localhost:8107/annotation
and sf classifier is availible on localhost:8108/annotation
Go to your local clone of this repo and run:
code .
This will ensure that your VS Code will run from this folder, and will (in case you use WSL) run through WSL.
Create ssh tunnel:
ssh -L 8501:localhost:8501 $HOST
Collect stats for food topic:
python examples/food.py
Collect stats for artificial dialog:
python examples/stats_collection.py
After that run dashboard:
streamlit run examples/stats_dashboard.py
### Discourse Moves Recommendation System
TBD
## Generic Responses
TBD
## Entity Detection
TBD