How to connect to and handle Wit.ai actions and intents from local machine?
i.e sending query to wit.ai using python api's and performing actions on local machine depend on json response.
I cannot find any proper documentation regarding this.
Almost all the tutorials are using node.js
Thanks in advance.
In fact, there is a Python SDK on the Wit.ai site.
There are also few examples that should help you get up to speed.
You may first want to check the quickstart.py example, that corresponds to the Build your first app tutorial on the Wit.ai site.
The documentation of the wit.ai contains the Github repository for python Library pywit.
You can check those examples for better understanding of the python code needed to develop chatbot.
Since you asked for documentations and other useful materials, you can check these blogs and github repos
How to make a basic ChatBot (Wit.ai) Part-1
Fb Bot tutorial along with integration
Wit.ai API provides many kind of NLP services including Speech Recognition. Follow this link for more information.
Related
I'm building a kivy app and almost got firebase cloud messaging working using the pyfcm
package from pypi.
What i'm stuck on is how to get the "registration token" that the firebase SDK creates on app install so that i have a registration token to send push notifications to.
There seems to be lots of info scattered around the net in regards to doing it using some other language other than "python", but python is all i know so i'm trying to work it out for that language.Any one have tips on how i go about that please ?
I can't find a python package that has the FirebaseMessaging.getToken() in it which is what the firebase documentation keeps referring to, so i'm lost and my brain is going mushy now due to researching this for hours and hours so far :)
any help appreciated
To answer my own question.
For those wondering the same question as i had. I came across this package https://github.com/Fox520/pushyy
In that package the guy has incorporated the ability to get the device token and also update the token when it changes. It takes a little manipulating to get the package to work properly with the latest gradle version but it is do able.
There doesnt seem to be a direct "python" method as such to get the token so using this package makes use of the required java and python code to achieve the task. Using this and using pyfcm from pypi to send messages, i'm now able to send firebase push notifications from my kivy app :)
I'm currently trying to develop an application to use ms-graph API using python.
I'm trying to use SSO authentication but I cant find any resources or documentation related.
I found this documentation https://learn.microsoft.com/en-us/office/dev/add-ins/develop/sso-in-office-add-ins
but it is only for javascript.
I wrote a script using Flask and another script using O365 python package.
It doesnt seem like its a Single sign on application that I try to achieve...
I'm looking for any documentation that could help.
You are referring to the Office web add-ins where you could also use SSO. But it seems you are developing a standalone application where Graph API is used. In that case you may find the Configure SAML-based single sign-on for your application using the Microsoft Graph API tutorial helpful.
I am new to using APIs and was trying to build a trading bot in python using WazirX exchange's REST API. I am unable to understand how to make API calls using their documentation (Also, is this an example of a good API documentation or of a bad one?). Can anyone point to any python wrapper libraries through which I can execute trades using an API key and secret? I tried searching a lot but couldn't find any Github repository using their API to execute trades (I have seen some using the public endpoints only).
Any help would be appreciated.
Wazirx itself has provided Ruby and Python connectors
There are also 3rd party libraries available which can be used.
https://docs.wazirx.com/#api-third-party-integrations
I am trying to run through the following tutorial on Bluemix:
https://www.ibm.com/watson/developercloud/doc/retrieve-rank/get_start.shtml
However, I am not able to install Python locally onto my system due to security policies. Is there a way I can run this tutorial through hosting my code in IBM DevOps Services using the Python runtime on Bluemix?
I am not sure if the Bluemix Python runtime can be leveraged like a natively installed Python and accept the command line instructions from:
Stage 4: Create and train the ranker.
Any feedback would be greatly appreciated!
An alternative to the manual Python and curl commands in that tutorial is to use the web interface.
I've written about it in some detail on my blog which also includes a video walkthrough of how the web tool works. (You can also find the official documentation on ibm.com).
But to sum up, it'd mean you could do everything through a web browser and not have to install or run anything locally - including training a ranker.
There is a small wrinkle in this plan right now, unfortunately. The Solr schema used in the Python/curl tutorial you've followed isn't compatible with the web tool, but we're working on that. This means that if you use the web tool, you'll need to start again with a new cluster and collection. But this means that you could start again using your own documents, content and training questions, instead of having to use the cranfield test data - so hopefully this is a good thing!
I am attempting to work with Google Checkout on Google App Engine. Currently, I am writing everything in Python and have the checkout process working. I am having some difficulty getting the notifications of processed orders functioning. I've been searching for Python examples, but thus far have been unsuccessful. Programming directly off of the documentation provided by Google, I have not been able to get my notifications working either. Would anyone happen to have a framework/demo Google Checkout Notification example in Python?
The chippyshop source has a nice example of a python Google Checkout implementation that should get you moving in the right direction.
UPDATE: I actually ended up needing to do this myself so I extended the example linked above and turned it into a reusable RequestHandler for use on GAE.
You can find the source and examples on github which should serve as a good starting point for any others following this path.