Day #293 - Date with RASA - Chatbot Learning day :)

Found an interesting workshop on end to end demo with RASA.

Training

Demo

	#https://www.youtube.com/watch?v=xu6D_vLP5vY
	#https://github.com/JustinaPetr/Weatherbot_Tutorial
	Rasa based chatbot
	Step 1 - Pre-Requisites
	========================
	1. Clone Project https://github.com/JustinaPetr/Weatherbot_Tutorial
	2. Install Requirements from FULL Code Directory
	cd E:\Code_Repo\Weatherbot_Tutorial\Full_Code
	pip install -r requirements.txt
	3. Download English Spacy model - To parse and get necessary information
	python -m spacy download en
	4. Install npm with node.js. https://www.npmjs.com/get-npm
	https://nodejs.org/dist/v12.13.0/node-v12.13.0-x64.msi
	5. In New Terminal
	npm i -g rasa-nlu-trainer
	Data Annotation - rasa nlu trainer
	Deconstruct into Intent, Entities
	Intent - What it is about
	Entity - Location, Place, Object in Discussion
	Example Messages, Alongside intents, Entities
	Examples
	Greeting
	GoodBye
	Asking
	Step 2 - Data Annotation
	============================
	Step #1 - File data.json
	{
	"rasa_nlu_data":{
	"common_examples":[
	{
	"text":"Hello"
	"intent":"Greet",
	"entities":[]
	}
	{
	"text":"goodbye"
	"intent":"goodbye",
	"entities":[]
	}
	]
	}
	Step #2 - Launch the trainer in Anaconda console
	1. Goto Location E:\Code_Repo\Weatherbot_Tutorial\Full_Code_Latest>
	2. Run Command rasa-nlu-trainer
	3. Custom adding intent and examples
	4. All additional examples present in git code in updated Data.json file
	Step #3 - Train model
	=======================
	1. Configuration File
	- Provide parameters
	- Pipeline - Feature extractors to fetch messages
	- Model save path
	- Data path for annotated data
	{
	"pipeline":"spacy_sklearn",
	"path":"./models/nlu",
	"data":"./data/data.json"
	}
	config_spacy.json file
	2. nlu_model.py file for script for model training
	#import libraries
	from rasa_nlu.converters import load_data
	#load configuration files
	from rasa_nlu.config import RasaNLUConfig
	#load trainer
	from rasa_nlu.model import Trainer
	def train_nlu(data,config,model_sir):
	training_data = load_data(data)
	trainer = Trainer(RasaNLUConfig(config))
	trainer.train(training_data)
	model_directory = trainer.persist(model_dir,fixed_model_name='weathernlu')
	if __name__=='__main__':
	train_nlu('./data/data.json','config_spacy.json','./models/nlu'
	#Run this to train the model
	#Models created in folder directory
	2. Code to test with additional code in nlu_model.py
	#import libraries
	from rasa_nlu.converters import load_data
	#load configuration files
	from rasa_nlu.config import RasaNLUConfig
	#load trainer
	from rasa_nlu.model import Trainer
	from rasa_nlu.model import Metadata, Interpreter
	def train_nlu(data,config,model_sir):
	training_data = load_data(data)
	trainer = Trainer(RasaNLUConfig(config))
	trainer.train(training_data)
	model_directory = trainer.persist(model_dir,fixed_model_name='weathernlu')
	def run_nlu():
	interpreter = interpreter.load('./models/nlu/default/weathernlu',RasaNLUConfig('config_spacy.json'))
	#load the model
	print(interpreter.parse(u"I am planning my holiday to barcelona, I wounder what is the weather out there"))
	if __name__=='__main__':
	run_nlu()
	3. Changes to run for custom packages (Code will run in these versions)
	pip install rasa_core==0.10.3
	pip install rasa-nlu==0.11.5
	Rerun - nlu_model.py file
	Step #4 - Building the conversation
	======================================
	1. Dialogue management will predict action. Domain file. It is yml file.
	2. Key parts are
	slots - placeholders for context of conversation,
	intents - ,
	entities - ,
	templates - ,
	actions -
	3. All details used for predictions
	slot and entities have same attributes - Observations
	template - text responses for users (multiple answers)
	weather_domain.yml
	slots:
	location:
	type:text
	intents:
	- greet
	- goodbye
	- inform
	entities:
	- location
	templates:
	utter-greet:
	- 'Hello, How can i help?'
	utter-goodbye:
	- 'ttyl'
	utter_ask_location:
	-'In what location?'
	actions:
	- utter_greet
	- utter_goodbye
	- utter_ask_location
	- actions.ActionWeather
	Step #5 - Custom Action creation file
	========================================
	actions.py
	from __future__import absolute_import
	from __future__import division
	from __future__import unicode_literals
	from rasa_core.actions.action import Action
	from rasa_core.events import SlotSet
	class ActionWeather(Action):
	def name(self):
	return 'action_weather'
	def run(self,dispatcher,tracker,domain):
	from apixu.client import ApixuClient
	api_key = ""
	#Authentication
	client = ApixuClient(api_key)
	loc = tracker.get_slot('location')
	current = client.getCurrentWeather(q=loc)
	#parse and extract required details
	country = current['location']['country']
	city = current['location']['name']
	condition = current['current']['condition']['text']
	temperature_c = current['current']['temp_c']
	humidity = current['current']['humidity']
	wind_mph = current['current']['wind_mph']
	response = """It is currently {} in {} at the moment. {} {} and wind {}""".format(condition,city,temperature,humidity,wind_mph)
	dispatcher.utter_message(response)
	#custom action
	return [SlotSet('location',loc)]
	#file updated in actions weather_domain.yml
	Step #6 - Story formation
	==========================
	New file Stories.md markdown file in data folder
	Stories.md
	==========
	#story 01
	* greet
	- utter_greet
	## story 02
	* goodbye
	- utter_goodbye
	##story 03
	* inform
	- utter_ask_location
	##story 04
	* inform
	- action_weather
	Step #7
	========
	Start online session
	using train_init.py and train_online.py
	train_init.py
	- train dialogue management model
	- agent used to train model
	- keras polcies used to train model
	- data file path
	- augmentation factor to add more stories
	- Save model with persist function
	pip install rasa-nlu==0.13.1
	Modified train_init.py
	=======================
	Step #8
	========
	train_online.py
	- import libraries
	- parser to parse extract features
	- load model
	Retrain Model
	python -m rasa_core.train -s data/stories.md -d weather_domain.yml -o models/dialogue --epochs 300
	Step #9
	=======
	Run online Training
	python -m rasa_nlu.train -c nlu_model_config.yml --fixed_model_name current --data ./data/nlu.md --path models/ --project nlu
	Step #10
	=========
	dialogue_management_model.py
	Final code to demo
	Summary
	========
	- Install requirements from FULL Code only
	- Run code nlu_model.py train block
	- Run code train_init.py
	- Run code train_online.py (Actual Conversations with chatbot)
	- Action_Listen (Wait for input)
	- Experimented the greet - ask - response - quit workflow
	- Run demo, dialogue_management_model.py
	Next Reads
	https://towardsdatascience.com/create-chatbot-using-rasa-part-1-67f68e89ddad
	https://medium.com/analytics-vidhya/learn-how-to-build-and-deploy-a-chatbot-in-minutes-using-rasa-5787fe9cce19
	https://forum.rasa.com/t/what-is-the-recommended-setup-for-production-deployemnt/1882

view raw rasabot.txt hosted with ❤ by GitHub

Happy Learning!!!