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#' Predict using a SavedModel
#'
#' Runs a prediction over a saved model file, web API or graph object.
#'
#' @inheritParams predict_savedmodel
#'
#' @param instances A list of prediction instances to be passed as input tensors
#' to the service. Even for single predictions, a list with one entry is expected.
#'
#' @param model The model as a local path, a REST url or graph object.
#'
#' A local path can be exported using \code{export_savedmodel()}, a REST URL
#' can be created using \code{serve_savedmodel()} and a graph object loaded using
#' \code{load_savedmodel()}.
#'
#' A \code{type} parameter can be specified to explicitly choose the type model
#' performing the prediction. Valid values are \code{export}, \code{webapi} and
#' \code{graph}.
#'
#' @param ... See [predict_savedmodel.export_prediction()],
#' [predict_savedmodel.graph_prediction()],
#' [predict_savedmodel.webapi_prediction()] for additional options.
#'
#' #' @section Implementations:
#'
#' - [predict_savedmodel.export_prediction()]
#' - [predict_savedmodel.graph_prediction()]
#' - [predict_savedmodel.webapi_prediction()]]
#'
#' @seealso [export_savedmodel()], [serve_savedmodel()], [load_savedmodel()]
#'
#' @examples
#' \dontrun{
#' # perform prediction based on an existing model
#' tfdeploy::predict_savedmodel(
#' list(rep(9, 784)),
#' system.file("models/tensorflow-mnist", package = "tfdeploy")
#' )
#' }
#'
#' @export
predict_savedmodel <- function(
instances,
model,
...) {
params <- list(...)
if (!is.null(params$type))
type <- params$type
else if (any(grepl("MetaGraphDef", class(model))))
type <- "graph"
else if (grepl("https?://", model))
type <- "webapi"
else
type <- "export"
class(instances) <- paste0(type, "_prediction")
UseMethod("predict_savedmodel", instances)
}
#' @export
print.savedmodel_predictions <- function(x, ...) {
predictions <- x$predictions
for (index in seq_along(predictions)) {
prediction <- predictions[[index]]
if (length(predictions) > 1)
message("Prediction ", index, ":")
print(prediction)
}
}
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