Predictive Vision Service

Basic Setup

First, install this repo into your org.

Create your self-signed certificate and metamind org per https://metamind.readme.io/docs/what-you-need-to-call-api . Stop where it says Scenario.
In Salesforce, go to Custom Settings, to EinsteinVision, and then click Manage.
Create a new setting at the organizational default level
Set the Einstein Username you signed up with, and CertName to be the name of the cert from step 2.
Pick a userId to be the Einstein user (bonus points for having a user named Einstein and setting their chatter picture!).
token expiration time should be 3600
certificate issuer: developer.force.com
Try the standard model: In Salesforce, create a model (you can call it anything) and set the Einstein Trained Model Id to GeneralImageClassifier
Post an image as a file or as a link to the Chatter feed.
Click on the comment box (live feed) or reload the page to see the response

Goal of this project

Access Einstien in Salesforce without writing code

Einstein Vision classifies photos.

Docs are here: https://metamind.readme.io/docs/introduction-to-the-einstein-predictive-vision-service

User creates a model and labels using Salesforce objects

Users can control the probability threshold and whether secondary predictions are shown.
Users can view (Lightning Experience Only) the model's statistics via a Lightning Component
Users can access the auth tokens via the browser console

Users can clean up after themselves

deleting a model in Salesforce deletes it from the PVS

Users get predictions

Invoke the model by posting either a file or an image to the chatter feed for that Label.
Einstein (via another user) will chatter back the predictions according to the model's settings.

Users can create examples via a local .bash script

	~/SomeOther/Place/directoryUploader.sh c7f7d45327504236c6dfd897cbeffd42a40be485 1952 795

where the three parameters are the token, the labelId, and the modelId. Note: you will have to make the script executable, like chmod u+x directoryUploader.sh.

After you create a model and some labels, click on a label to view the label-specific terminal command to run, including a valid token (#onlyInLightning). You're welcome!

This directoryUploader will take every image in the user's current directory and upload it to that indicated label. Notice that the script is in some other place, and the current directory only has the images in it.

Users can train a model

Just hit the train button on a model and the code will handle the rest #onlyInLightning Love those new Lightning Actions!

Custom Settings

Manage your Einstein Credentials
Manage the security cert used when you sign up for an account
Be sure to populate these before trying to use the service

Using Standard Models

For the food model, set the Einstein Trained Model Id to FoodImageClassifier
For the general model, set the Einstein Trained Model Id to GeneralImageClassifier

Fun things to do

be sure to walk around with Salesforce1 attaching photos to a model of anything around.
Have Einstein idenfity dog breeds

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Einstein Language

Invocable method from ProcessBuilder for calling standard or custom language models.

Tools in progress for creating custom language models based on

data in Salesforce
files attached via Chatter

ploughran/einsteinVision