A simple package for interfacing with Dialogflow
Affability allows for an easy utilization of Google's DialogFlow for natural language understanding. It allows for calling a single function and returning the result from Dialogflow as a class containing all the pertinent data such as detected intent.
This can be utilized to understand commands and then perform the relevant tasks based from the detected intent. Affability is ultimately designed to have the ability to assist with a quadruped robot project in the future.
Installing using the requirements.txt file:
pip install -r requirements.txt
pip install SpeechRecognition pip install dialogflow
pip install Affability
Using the understand function:
The understand function contains 5 parameters: text, credentials, projectID, languageCode, and sessionID. Text is text to be analyzed, credentials is the file path of the authentication key, projectID is the project ID, languageCode is the language, and sessionID is the session ID. All parameters are strings.
understand('textToBeAnalyzed', 'filepath', 'projectIDname', 'en-US', 'me')
The understand function returns the results as an organizer class. This class contains detectedIntent, confidence, reply, action, requiredParamsPresent, and replyParams.
class organizer: def __init__(self, detectedIntent, confidence, reply, action, requiredParamsPresent, replyParams): self.detectedIntent = detectedIntent self.confidence = confidence self.reply = reply self.action = action self.requiredParamsPresent = requiredParamsPresent self.replyParams = replyParams
For example, to extract and print detected intent:
reply = understand('textToBeAnalyzed', 'filepath', 'projectIDname', 'en-US', 'me') print(reply.detectedIntent)
The example_usage.py file demonstrates the ease of communicating with Dialogflow through Affability.
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