https://github.com/deeppavlov/easyintentcacther

Administrative tool for managing datasets of IntentCacther
https://github.com/deeppavlov/easyintentcacther

Last synced: about 1 month ago
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Administrative tool for managing datasets of IntentCacther

• Host: GitHub
• URL: https://github.com/deeppavlov/easyintentcacther
• Owner: deeppavlov
• Created: 2020-10-29T12:58:35.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-01-21T12:07:07.000Z (over 5 years ago)
• Last Synced: 2025-02-23T23:28:10.556Z (over 1 year ago)
• Language: Python
• Size: 1.69 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# EasyIntentCatcher

Administrative tool for managing datasets of IntentCacther

# Features:

- IntentCatcher dataset management: view, edit, create new training samples from WEB UI

- Train IntentCacther models from UI

- Test and Analyze predictions of the trained model

- Export trained models to remote SSH Server 

- Automatically prepares model for updated dataset

TODO add screenshots:

  - intents list intent details

  - predictions log

  - prediction api

  

# Requirements:

Assure your server fit following requirements:

 - python 3.7.9

   - conda -N new_env python=3.7

 - redis-server

   -   sudo apt-get install redis-server

# Set Up

How to setup:

```

git clone https://github.com/deepmipt/EasyIntentCacther

cd EasyIntentCacther

pip install -r requirements.txt

python manage.py migrate

```

Now you need to launch worker for training tasks and web server for managing dataset and training system.

# Run worker for training task execution:

```

celery  -A EasyIntentCatcher worker -l INFO

```

# Run server for web-administration tool

```

python manage.py runserver 0.0.0.0:8000

```

Now you can browse your intents, train a model, export to SSH server and use it for predictions!

# Useful commands for work with IntentCatcher on lowel level interface

Generate IntentCather JSON dataset specification from Database contents:

```

python ic_dataset/from_db_2_icjson.py

```

Train model from IntentCather JSON dataset specification with specification of target path for model:

```

python data/create_data_and_train_model.py --intent_phrases_path data/intent_phrases_export.json --model_path data/models/my_model

```

# Screenshots

List of intents:

![210121_easyintentcatcher_intents_list](https://user-images.githubusercontent.com/2207706/105342371-55395a00-5bf1-11eb-83b8-5d2662453999.png)

Intent editing: 

![210122_easyintentcatcher_intents_edit](https://user-images.githubusercontent.com/2207706/105342390-5c606800-5bf1-11eb-8e7b-2707041d381c.png)

History of predictions:

![210123_easyintentcatcher_prediction_cases_log](https://user-images.githubusercontent.com/2207706/105342407-608c8580-5bf1-11eb-9b11-881b57d9199a.png)

History of trained models:

![210124_easyintentcatcher_trained_model](https://user-images.githubusercontent.com/2207706/105342426-65e9d000-5bf1-11eb-8ee9-eb9964e7be72.png)

Prediction API:

![210125_easyintentcatcher_prediction_API](https://user-images.githubusercontent.com/2207706/105342443-6a15ed80-5bf1-11eb-937f-097a1c997f69.png)

API Response example:

![210126_easyintentcatcher_prediction_API_response_example](https://user-images.githubusercontent.com/2207706/105342461-70a46500-5bf1-11eb-9c4b-1867671efacb.png)