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change python code in Full Code Latest to use environment token/keys … #90

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chnage python code in Full Code Latest to use environment token/keys …
May 20, 2019
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update doc/Rasa-Slack-integration-weather-bot.png
Jul 16, 2019
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20 changes: 20 additions & 0 deletions .gitignore
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**/__pycache__/**
**/dialogue/**
**/apixu/**
**/logs/**

**/.idea/**

test*

**/envrionment.env

# ignore keys, tokens files
credentials.yml
export_rasa_config.sh

# local backup of PIP requirements
requirements-rasa-weatherbot.txt

# local scripts
run-all-services.sh
5 changes: 5 additions & 0 deletions .idea/codeStyles/codeStyleConfig.xml
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270 changes: 219 additions & 51 deletions Full Code [Latest release of Rasa NLU and Rasa Core]/README.md
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# 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. 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.

## How to use this repo

The code of this repo differs quite significantly from the original video. This is how to use it:

### 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 interactive training session:

# 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. 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.

## How to use this repo

The code of this repo differs quite significantly from the original video.
The following explains the overall steps/flows:

* Step-1: Training the NLU model
* Step-2: (Initial) Training the Rasa Core model
* Step-3: Optional -- (Interactive / Online) Training Session for the Rasa Core model
* Step-4: Deploy Rasa Chat Bot (NLU + Core/Agent + Action) to work with Slack Chat.

### Step-1: 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
```

### Step-2: (Initial) 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:

```
APIXU_KEY=<APIXU_token> python -m rasa_core_sdk.endpoint --actions actions
```
or, if you already export Key and Token in each terminal above:

```
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 to provide training with `intended / corrected behaviors` for the Rasa Core to learn from your correct answers that you provide. (To exit the interative/online training session, CTRL-C twice!)

### Step-3: Optional -- (Interactive / Online) Training Session for the Rasa Core model

The process of running the interactive 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 interactive training session by running:

``` python train_interactive.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.
1. Make sure the custom actions server is running:

```
python -m rasa_core_sdk.endpoint --actions actions
```

2. Start the interactive training session by running:

```
python train_interactive.py
```

### Step-4: Deploy Rasa Chat Bot (NLU + Core/Agent + Action) to work with Slack Chat.

1. Configure the slack app as shown in the video

* Study the Video: [From zero to hero: Creating a chatbot with Rasa NLU and Rasa Core](https://player.vimeo.com/video/254777331)
* See also the screenshots in ./doc folder
* Go to [apixu web site](https://www.apixu.com/) to
create API key `APIXU_KEY=<nnnn...>` .
* Go to [SlackAPI](https://api.slack.com/) to
create "weatherbot" to get `Bot_User_OAuth_Access_Token=<xoxb-...>` for `run_app.py` to consume
* Go to [ngrok - secure introspectable tunnels to localhost](https://ngrok.com/) to
down and install ngrok.
* Open 3 extra Terminals for `Action, run_app.py, ngrok`
* Export `Key (obatined from Weather APIXU Key)` and
`Token (obtained from Slack Bot User)` in each terminal of those three terminals

Test Weather API (APIXU) with your `APIXU token` before you proceed:

```
Current Weather
HTTP: http://api.apixu.com/v1/current.json?key=<apixu_key>&q=Paris
HTTPS: https://api.apixu.com/v1/current.json?key=<apixu_key>&q=Paris

Forecast Weather
HTTP: http://api.apixu.com/v1/forecast.json?key=<apixu_key>&q=Paris
HTTPS: https://api.apixu.com/v1/forecast.json?key=<apixu_key>&q=Paris
```

2. Run Dialogue (rasa-core) test

```
(Try the following and kill it when done testing)
APIXU_KEY=<nnnn...> python dialogue_management_model.py
```

or, if you already export Key and Token in each terminal above:

```
python dialogue_management_model.py
```

And, you will enter test conversation such as

```
Processed Story Blocks: 100%|██████████████████████████████████████| 10/10 [00:00<00:00, 1201.39it/s, # trackers=3]
Processed Story Blocks: 100%|██████████████████████████████████████| 10/10 [00:00<00:00, 566.00it/s, # trackers=6]
Processed Story Blocks: 100%|██████████████████████████████████████| 10/10 [00:00<00:00, 515.51it/s, # trackers=6]
Processed actions: 39it [00:00, 11900.90it/s, # examples=39]
2019-05-17 10:58:37.061541: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: FMA
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking (Masking) (None, 3, 16) 0
_________________________________________________________________
lstm (LSTM) (None, 32) 6272
_________________________________________________________________
dense (Dense) (None, 11) 363
_________________________________________________________________
activation (Activation) (None, 11) 0
=================================================================
Total params: 6,635
Trainable params: 6,635
Non-trainable params: 0
_________________________________________________________________

... (ignored screen dump before)

Epoch 298/300
25/25 [==============================] - 0s 213us/step - loss: 0.3034 - acc: 0.9600
Epoch 299/300
25/25 [==============================] - 0s 319us/step - loss: 0.2905 - acc: 1.0000
Epoch 300/300
25/25 [==============================] - 0s 235us/step - loss: 0.2877 - acc: 0.9200
Bot loaded. Type a message and press enter (use '/stop' to exit):
Your input -> hello
Hello! How can I help?
127.0.0.1 - - [2019-05-17 10:58:52] "POST /webhooks/rest/webhook?stream=true&token= HTTP/1.1" 200 222 0.248822
Your input -> tell me the weather
In what location?
127.0.0.1 - - [2019-05-17 10:58:58] "POST /webhooks/rest/webhook?stream=true&token= HTTP/1.1" 200 217 0.044651
Your input -> in London
It is currently Overcast in London at the moment. The temperature is 13.0 degrees, the humidity is 77% and the wind speed is 9.4 mph.
127.0.0.1 - - [2019-05-17 10:59:02] "POST /webhooks/rest/webhook?stream=true&token= HTTP/1.1" 200 333 0.329568
Your input ->

(CTRL+C twice to quit)
```

3. Make sure custom actions server is running

```
APIXU_KEY=<nnnn...> python -m rasa_core_sdk.endpoint --actions actions
```

or, if you already export Key and Token in each terminal:

```
python -m rasa_core_sdk.endpoint --actions actions
```

4. Start the agent by running run_app.py file (don't forget to provide the slack_token)

```
Bot_User_OAuth_Access_Token=<xoxb-...> python run_app.py
```

or, if you already export Key and Token in each terminal:

```
python run_app.py
```

5. Start the ngrok on the port 5004

```
ngrok http 5004
```

And, you will see like below:

```
ngrok by @inconshreveable (Ctrl+C to quit)
Session Status online Account Jim Kowalski (Plan: Free) Update update available (version 2.3.28, Ctrl-U to update) Version 2.3.27 Region United States (us) Web Interface http://127.0.0.1:4040 Forwarding http://92239ac7.ngrok.io -> http://localhost:5004 Forwarding https://92239ac7.ngrok.io -> http://localhost:5004
Connections ttl opn rt1 rt5 p50 p90
0 0 0.00 0.00 0.00 0.00
```

Then, open ngrok monitor URL using Web browser, Firefox or Chrome or any:

```
http://127.0.0.1:4040
```

6. Provide the ngrok url to 'Event Subscriptions' page of the slack configuration.

```
https://<your_ngrok_url>/webhooks/slack/webhook`
```

And, test the connection for ngrok:

```
http://localhost:5004/webhooks/slack/webhook
http://<ngrok_URL>/webhooks/slack/webhook
```

Now, it's ready for end-to-end test of the Rasa Chat Bot + Slack Chat integration with Weather API called by Rasa SDK Action.

7. Talk to your Slack bot using Slack Chat, e.g.

```
https://<your-Slack-Channel>.slack.com/messages/
e.g.
https://openkbsorg.slack.com/messages/
```

I will do my best to keep this repo 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: 01/03/2019

Latest compatible Rasa NLU version: 0.14.4
Latest compatible Rasa Core version: 0.13.2




Latest code update: 05/17/2019

Latest compatible Rasa NLU version: 0.14.6
Latest compatible Rasa Core version: 0.13.7
58 changes: 37 additions & 21 deletions Full Code [Latest release of Rasa NLU and Rasa Core]/actions.py
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from rasa_core_sdk import Action
from rasa_core_sdk.events import SlotSet

import os


class ActionWeather(Action):
def name(self):
return 'action_weather'

def run(self, dispatcher, tracker, domain):
from apixu.client import ApixuClient
api_key = '...' #your apixu key
client = ApixuClient(api_key)

loc = tracker.get_slot('location')
current = client.getcurrent(q=loc)

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']
def name(self):
return 'action_weather'

def run(self, dispatcher, tracker, domain):
from apixu.client import ApixuClient

# You need to provide APIXUKEY in file export_APIXU_KEY.sh since it won't be saved in GIT
#
# http://api.apixu.com/v1/current.json?key=<apixu_key...>&q=paris
try:
#print(os.environ)
print("APIXU_KEY=" + os.environ["APIXU_KEY"])
api_key = os.environ['APIXU_KEY']
# api_key = 'xxxx' #your apixu key
client = ApixuClient(api_key)
except KeyError:
print("Please set the environment variable APIXU_KEY")
os.sys.exit(1)

loc = tracker.get_slot('location')
current = client.current(q=loc)

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. The temperature is {} degrees, the humidity is {}% and the wind speed is {} mph.""".format(condition, city, temperature_c, humidity, wind_mph)

dispatcher.utter_message(response)
return [SlotSet('location',loc)]
response = """It is currently {} in {} at the moment.
The temperature is {} degrees, the humidity is {}% and the wind speed is {} mph."""\
.format(condition, city, temperature_c, humidity, wind_mph)

dispatcher.utter_message(response)
return [SlotSet('location',loc)]

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