/**
* Predict using a file and return predictions as a CSV.
*
* @param modelId Unique id of the model
* @param dataFormat Data format of the file (CSV or TSV)
* @param columnHeader Whether the file contains the column header as the first row (YES or NO)
* @param inputStream Input stream generated from the file used for predictions
* @return A file as a {@link javax.ws.rs.core.StreamingOutput}
*/
@POST
@Path("/predictionStreams")
@Produces(MediaType.APPLICATION_OCTET_STREAM)
@Consumes(MediaType.MULTIPART_FORM_DATA)
public Response streamingPredict(
@Multipart("modelId") long modelId,
@Multipart("dataFormat") String dataFormat,
@Multipart("columnHeader") String columnHeader,
@Multipart("file") InputStream inputStream) {
PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext();
int tenantId = carbonContext.getTenantId();
String userName = carbonContext.getUsername();
try {
// validate input parameters
// if it is a file upload, check whether the file is sent
if (inputStream == null || inputStream.available() == 0) {
String msg =
String.format(
"Error occurred while reading the file for model [id] %s of tenant [id] %s and [user] %s .",
modelId, tenantId, userName);
logger.error(msg);
return Response.status(Response.Status.BAD_REQUEST).entity(new MLErrorBean(msg)).build();
}
final String predictions =
mlModelHandler.streamingPredict(
tenantId, userName, modelId, dataFormat, columnHeader, inputStream);
StreamingOutput stream =
new StreamingOutput() {
@Override
public void write(OutputStream outputStream) throws IOException {
Writer writer =
new BufferedWriter(new OutputStreamWriter(outputStream, StandardCharsets.UTF_8));
writer.write(predictions);
writer.flush();
writer.close();
}
};
return Response.ok(stream)
.header(
"Content-disposition",
"attachment; filename=Predictions_"
+ modelId
+ "_"
+ MLUtils.getDate()
+ MLConstants.CSV)
.build();
} catch (IOException e) {
String msg =
MLUtils.getErrorMsg(
String.format(
"Error occurred while reading the file for model [id] %s of tenant [id] %s and [user] %s.",
modelId, tenantId, userName),
e);
logger.error(msg, e);
return Response.status(Response.Status.BAD_REQUEST)
.entity(new MLErrorBean(e.getMessage()))
.build();
} catch (MLModelHandlerException e) {
String msg =
MLUtils.getErrorMsg(
String.format(
"Error occurred while predicting from model [id] %s of tenant [id] %s and [user] %s.",
modelId, tenantId, userName),
e);
logger.error(msg, e);
return Response.status(Response.Status.INTERNAL_SERVER_ERROR)
.entity(new MLErrorBean(e.getMessage()))
.build();
}
}
