TjBot | Name | Bot library
kandi X-RAY | TjBot Summary
kandi X-RAY | TjBot Summary
The intention of this bot is to make moderation easier and provide useful commands to manage the server.
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Top functions reviewed by kandi - BETA
- Entry point for the last sent message
- Joins a list of parts
- Format a duration to English equivalent
- Formats the value if it is not zero
- Retrieve user
- Parses a user by id
- Overrides superclasses to customize the request context
- Parses a user by its id
- Fetch a path from messages
- Translates a string
- Retrieves the value for a given root map
- Main entry point
- Gets the bot token from the config
- Loads the configuration from the given input stream
- Searches for a command
- Returns a success parser for the given prefixes
- Returns all the prefixes
- Checks if the given message is in help channel
- Returns the config for the free command
- Searches for a resource in the database
- Called when a message is received
- Converts a user id into a User object
- Translates key - value pair
- Handles incoming message
- Executes the embed
- Returns true if the given kex exists
TjBot Key Features
TjBot Examples and Code Snippets
Community Discussions
Trending Discussions on TjBot
QUESTION
I have the following code, straight from the documentation:
...ANSWER
Answered 2018-Dec-06 at 07:34The below code works for me and generating an audio.wav file using the Text-to-Speech API Key
QUESTION
I'm trying to run an electron application on raspberry pi. When I use
var TJBot = require('tjbot') in my code to import a node package, the following error occurs:
ANSWER
Answered 2017-Jul-21 at 18:51Fibers is built to native binary code, it is not pure javascript. Rasperry Pi runs on ARM, which isn't included in the prebuilt binaries. The prebuilt binaries support 32 and 64 bit architectures for OSX (darwin), linux, and windows.
This means you need to manually build Fibers for your system, not install it via NPM. The README for Fibers has clear instructions on how to do so. I've included them here for ease of access. Please follow these instructions to try and get it running on Raspberry Pi / ARM.
Installing from sourcegit clone git://github.com/laverdet/node-fibers.gitcd node-fibersnpm install
Note: node-fibers uses node-gyp for
building. To manually invoke the build process, you can use node-gyp rebuild.
This will put the compiled extension in build/Release/fibers.node. However,
when you do require('fibers'), it will expect the module to be in, for
example, bin/linux-x64-v8-3.11/fibers.node. You can manually put the module
here every time you build, or you can use the included build script. Either
npm install or node build -f will do this for you. If you are going to be
hacking on node-fibers, it may be worthwhile to first do node-gyp configure
and then for subsequent rebuilds you can just do node-gyp build which will
be faster than a full npm install or node-gyp rebuild.
QUESTION
I’m working on a TJBot visual recognition application.
I have some particular questions about retraining.
My case is similar to this:
I have a model trained for cats, dogs and a negative category (not cats or dogs).
The visual recognizer sometimes gets the wrong answer.
What I’d like to do is ‘automatically’ retrain the model, programmatically using an API whenever it gets the wrong answer.
For example after making the wrong determination of a dog when it was actually a cat, I’d like to turn around and call the visual recognition retraining api and indicate: this picture is a cat. This picture is not a dog.
Questions: 1) Is there an API for ‘partial retraining’ using just one image? I don’t want to have to upload hundreds of photos again to completely retrain the model if I don’t have to.
2) In this case I’d like to say this is a positive example of a cat, and a negative example of a dog. As I understand it there’s no way to indicate ‘negative example of a dog’. I can only say ‘negative example of cat and dog’. Is there a way to indicate ‘negative example of just one of the trained classes?
3) If there’s no API, can I accomplish #1 or #2 using the ‘studio’ website? How?
Thanks, Andy Citron (IBM retired)
...ANSWER
Answered 2018-Sep-11 at 02:46Thanks for your question.
Yes, you can send a single image as the data for retraining a classifier. However, 1 image is unlikely to affect your classifier much. We suggest sending at least 10 new images in each retraining request.
If sent as a "negative_example" the image should be neither a cat or a dog. The classes in a classifier should be mutually exclusive. A photo of a cat and dog together does not work well for training a system which is trying to tell the difference between the two types. To the classifier, the types of answers it can give are defined by the classes, or if negative_examples are used "none of the above". So in your example, every image in the world gets classified by this model as either cat, dog, or neither.
The API is documented here: https://www.ibm.com/watson/developercloud/visual-recognition/api/v3/curl.html?curl#update-classifier
QUESTION
Found in service examples, a working conversation script. Thanks again to @Taj!
I feel like I am very very close to get it to work. I have done the samething on Raspberry Pi with TJBot, so I have all the accounts, and I linked all the credentials correctly including the workplace ID from Conversation tooling. I am using Unity 3D 5.5.1f1 and the latest SDK, the one that was updated 13 days ago.
I copied and pasted the sample code for conversation on SDK's github page into a brand new C# file:
...ANSWER
Answered 2017-Feb-09 at 17:00The response strings are an array in the resp object.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install TjBot
You can use TjBot like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the TjBot component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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