Pranav63/SameOldChatBot

Nah its not the same old chat bot. Its all about implementing RASA stack which is a framework mainly used for NLP/NLU purposes. We ll be making a chatbot that you can deploy on slack as well so that you can fetch match scores at the tip of your hand(oh sorry your keyboard). Follow the below steps for the entire project building.

Build your Chatbot to get your favourite cricket match scores.

Setup

If you haven’t installed Rasa NLU and Rasa Core yet, you can do it by navigating to the project directory and running:

pip install -r requirements.txt

You also need to install a spaCy English language model. You can install it by running:

python -m spacy download en

Rasa NLU model files

• data/nlu_data.md file contents training data for the NLU model.

• nlu_config.yml Config for RASA NLU pipeline

language: "en"

pipeline: spacy_sklearn

Files for Rasa Core model

• data/stories.md training stories which represent the conversations between a user and the assistant.
• domain.yml file describes the domain of the assistant which includes intents, entities, slots, templates and actions for the assistant to understand.
• actions.py file contains the code of a custom action which retrieves results of the latest IPL match by making an external API call.
• endpoints.yml file contains the webhook configuration for custom action.
• policies.yml file contains the configuration of the training policies for Rasa Core model.

How to run locally

Note: If running on Windows, you will either have to install make or copy the following commands from the Makefile

1. You can train the Rasa NLU model by running:
   make train-nlu
   This will train the Rasa NLU model and store it inside the /models/current/nlu folder of your project directory.

2. Train the Rasa Core model by running:
   make train-core
   This will train the Rasa Core model and store it inside the /models/current/dialogue folder of your project directory.

3. In a new terminal start the server for the custom action by running:
   make action-server
   This will start the server for emulating the custom action.

4. Test the assistant by running:
   make cmdline
   This will load the assistant in your terminal for you to chat.

How to deploy to Slack

1. Go to your Slack app's settings page and use the Bot User OAuth Access Token:

And add this in the slack_credentials.yml file:

slack:
  slack_token: "Bot User OAuth Access Token"
  slack_channel: 

2. Start the action server by typing the following command in terminal:

make action-server

3. Setup ngrok for the port that the action server is using by the following command:

ngrok http 5055

4. Copy the highlighted url in the above image into your endpoints.yml file:

action_endpoint: "your_url_here/webhook"
  url: 

5. Start the core server in another terminal window:

python -m rasa_core.run -d models/current/dialogue -u models/current/nlu --port 5002 --connector slack --credentials slack_credentials.yml --endpoints endpoints.yml

This will start the server at port 5002.

6. Now you have to expose this port to the world by using ngrok, open another terminal and type:

ngrok http 5002

7. Take the above url and paste it into the Events Subscription page of your slack app in the following format:

your_url_here/webhooks/slack/webhook