Predict using a saved regression model
Use sklearn.preprocessing.OneHotEncoder for example and transfer the one-hot encoding to your web-service ( i'm guessing that's how you're using the model for inference ) via sklearn.pipeline.Pipeline. The pipeline will save the state of your fit on your training data and apply the same function on your production data.

Example :

pipeline1 = Pipeline([
                ('OneHotEncoder', OneHotEncoder())
            ])
pipeline1.fit(trainingdata.column1.values.reshape(-1,1))


This is how you create a pipeline containing the onehotencoder , fit your data on the pipeline. All is left is dumping your pipeline in a file, loading it later in your production environment, and call the transform method on your loaded pipeline :

joblib.dump(pipeline1,"pipeline1.joblib")
# Production environment
pipeline1 = joblib.load('pipeline1.joblib')
momo = pipeline1.transform(productiondata.column1.values.reshape(-1,1)).toarray()


And here , the variable momo contains your production data with the pipeline ( containing the one-hot encoding operation ) applied to it.