from transformers import pipeline


class Model:
    def __init__(self, **kwargs):
        self._model = None

    def load(self):
        self._model = pipeline("text-classification")

    def predict(self, model_input):
        return self._model(model_input)
When you’re happy with your Truss, it’s time to publish it to production. This re-builds the model server on production-ready infrastructure. Before publishing your Truss, you can turn off truss watch as it only patches models under development, not published models.

​
Publish your Truss

To publish your Truss, run: 
truss push --publish
Re-building your model server takes more time than patching it; it’ll be a moment until the new server is ready to be called.

​
Call the published model

Once the new model server is live, call it with truss predict: Invocation 
truss predict --published -d '"Truss is awesome!"'
Response 
[
  {
    "label": "POSITIVE",
    "score": 0.999873161315918
  }
]

​
Review your learning

In this tutorial, you learned how to:
  1. Create a Truss
  2. Connect your local development environment to a model server
  3. Deploy a basic text classification model
  4. Publish your model to production
For more step-by-step instructions, move on to the Truss 201 tutorial. Or, to find an example that matches your use case, see the Truss examples docs. 
from transformers import pipeline


class Model:
    def __init__(self, **kwargs):
        self._model = None

    def load(self):
        self._model = pipeline("text-classification")

    def predict(self, model_input):
        return self._model(model_input)