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A simple package for interfacing with Dialogflow

Project description

Affability

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.

Dependencies

Installing using the requirements.txt file:

pip install -r requirements.txt

Installing manually:

pip install SpeechRecognition
pip install dialogflow

Installation

pip install Affability

Usage

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)

Sample usage

The example_usage.py file demonstrates the ease of communicating with Dialogflow through Affability.

Project details


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