updated for rasa core 11

Author: Justina Petraityte

Full Code [Latest release of Rasa NLU and Rasa Core]/README.md

@@ -1,14 +1,50 @@
 # Weatherbot Tutorial (using the latest release of Rasa NLU and Rasa Core)
 
 Rasa NLU and Rasa Core devs are doing an amazing job improving both of these libraries which results in code changes for one method or another. In fact, since I recorded a Wetherbot tutorial,
-there were quite a few changes which were introduced to Rasa NLU and Rasa Core. For this reason I created this directory which will contain Weatherbot code, compatible with the latest
-releases of Rasa NLU and Rasa Core. I will do my best to keep it up-to-date, but if you encounter any issues with using the code, please raise an issue or drop me a message :)
+there were quite a few changes which were introduced to Rasa NLU and Rasa Core. On 30th of August, Rasa v.0.11 was released with a lot of changes under the hood. This repo contains the updated weatherbot code compatible with the latest releases of Rasa Core and Rasa NLU.
 
-Latest code update: 15/08/2018
+## How to use this repo
 
-Latest compatible Rasa NLU version: 0.13.1
+The code of this repo differs quite significantly from the original video. This is how to use it:
 
-Latest compatible Rasa Core version: 0.10.4
+### Training the NLU model
+
+Training of the NLU model didn't change much from the way it was shown in the video. To train and test the model run:  
+
+``` python nlu_model.py ```
+
+### Training the Rasa Core model
+
+The biggest change in how Rasa Core model works is that custom action 'action_weather' now needs to run on a separate server. That server has to be configured in a 'endpoints.yml' file.  This is how to train and run the dialogue management model:  
+1. Start the custom action server by running:  
+``` python -m rasa_core_sdk.endpoint --actions actions '''
+2. Open a new terminal and train the Rasa Core model by running:  
+``` python dialogue_management_model.py```  
+3. Talk to the chatbot once it's loaded.
+
+### Starting the online training session:
+
+The process of running the online session is very similar to training the Rasa Core model:
+1. Make sure the custom actions server is running:
+``` python -m rasa_core_sdk.endpoint --actions actions '''   
+2. Start the online training session by running:
+``` python train_online.py ```
+
+### Connecting a chatbot to Slack:
+1. Configure the slack app as shown in the video
+2. Make sure custom actions server is running
+3. Start the agent by running run_app.py file (don't forget to provide the slack_token)
+4. Start the ngrok on the port 5004
+5. Provide the url: https://your_ngrok_url/webhooks/slack/webhook to 'Event Subscriptions' page of the slack configuration.
+6. Talk to you bot.
+
+I will do my best to keep it up-to-date, but if you encounter any issues with using the code, please raise an issue or drop me a message :)
+
+Latest code update: 08/09/2018
+
+Latest compatible Rasa NLU version: 0.13.2
+
+Latest compatible Rasa Core version: 0.11.3
 
 
 

Full Code [Latest release of Rasa NLU and Rasa Core]/__pycache__/actions.cpython-36.pyc

@@ -2,8 +2,8 @@
 from __future__ import division
 from __future__ import unicode_literals
 
-from rasa_core.actions.action import Action
-from rasa_core.events import SlotSet
+from rasa_core_sdk import Action
+from rasa_core_sdk.events import SlotSet
 
 class ActionWeather(Action):
 	def name(self):

Full Code [Latest release of Rasa NLU and Rasa Core]/actions.py

@@ -4,13 +4,13 @@
 from __future__ import unicode_literals
 
 import logging
-
+import rasa_core
 from rasa_core.agent import Agent
-from rasa_core.channels.console import ConsoleInputChannel
-from rasa_core.interpreter import RegexInterpreter
 from rasa_core.policies.keras_policy import KerasPolicy
 from rasa_core.policies.memoization import MemoizationPolicy
 from rasa_core.interpreter import RasaNLUInterpreter
+from rasa_core.utils import EndpointConfig
+from rasa_core.run import serve_application
 
 logger = logging.getLogger(__name__)
 
@@ -19,9 +19,9 @@ def train_dialogue(domain_file = 'weather_domain.yml',
 					training_data_file = './data/stories.md'):
 
 	agent = Agent(domain_file, policies = [MemoizationPolicy(), KerasPolicy()])
-	
+	data = agent.load_data(training_data_file)	
 	agent.train(
-				training_data_file,
+				data,
 				epochs = 300,
 				batch_size = 50,
 				validation_split = 0.2)
@@ -31,10 +31,9 @@ def train_dialogue(domain_file = 'weather_domain.yml',
 
 def run_weather_bot(serve_forever=True):
 	interpreter = RasaNLUInterpreter('./models/nlu/default/weathernlu')
-	agent = Agent.load('./models/dialogue', interpreter = interpreter)
-	
-	if serve_forever:
-		agent.handle_channel(ConsoleInputChannel())
+	action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
+	agent = Agent.load('./models/dialogue', interpreter=interpreter, action_endpoint=action_endpoint)
+	rasa_core.run.serve_application(agent ,channel='cmdline')
 
 	return agent
 

Full Code [Latest release of Rasa NLU and Rasa Core]/dialogue_management_model.py

@@ -0,0 +1,2 @@
+action_endpoint:
+  url: "http://localhost:5055/webhook/"

Full Code [Latest release of Rasa NLU and Rasa Core]/endpoints.yml

@@ -1,16 +1,10 @@
 %YAML 1.1
 ---
-action_factory: null
-action_names:
-- utter_greet
-- utter_goodbye
-- utter_ask_location
-- action_weather
 actions:
 - utter_greet
 - utter_goodbye
 - utter_ask_location
-- actions.ActionWeather
+- action_weather
 config:
   store_entities_as_slots: true
 entities:

Full Code [Latest release of Rasa NLU and Rasa Core]/models/dialogue/domain.yml

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+    "eJyLrs7MK0nNK4lPz89PSapMtVIw1DPQUSgoSi2LT0wuyczPiy9PTSzJSC2CyhTn5JfE5+QnJ4LlDMCitToKhI3JySwGqiDblNKSktQiNAlCphSlppZQ6hJ0M6DuQBLGakIsAOGndoI=": 0
   }
 }

Full Code [Latest release of Rasa NLU and Rasa Core]/models/dialogue/policy_0_MemoizationPolicy/memorized_turns.json

@@ -1,5 +1,4 @@
 {
-  "arch": "keras_arch.json",
-  "weights": "keras_weights.h5",
+  "model": "keras_model.h5",
   "epochs": 300
 }