import json def input_handler(data, context): """ Pre-process request input before it is sent to TensorFlow Serving REST API Args: data (obj): the request data, in format of dict or string context (Context): an object containing request and configuration details Returns: (dict): a JSON-serializable dict that contains request body and headers """ if context.request_content_type == 'application/json': # pass through json (assumes it's correctly formed) d = data.read().decode('utf-8') print("------------") print(d) print(type(d)) print("------------") return d if len(d) else '' if context.request_content_type == 'text/csv': # very simple csv handler return json.dumps({ 'instances': [float(x) for x in data.read().decode('utf-8').split(',')] }) raise ValueError('{{"error": "unsupported content type {}"}}'.format( context.request_content_type or "unknown")) def output_handler(data, context): """Post-process TensorFlow Serving output before it is returned to the client. Args: data (obj): the TensorFlow serving response context (Context): an object containing request and configuration details Returns: (bytes, string): data to return to client, response content type """ if data.status_code != 200: raise ValueError(data.content.decode('utf-8')) response_content_type = context.accept_header print("-------") print(data) print(type(data)) print("-----") prediction = data.content return prediction, response_content_type