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import json |
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import time |
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from http import HTTPStatus |
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from typing import Dict |
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from aws_lambda_powertools.event_handler import content_types |
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from aws_lambda_powertools.utilities.typing import LambdaContext |
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from src import app_logger |
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from src.io.coordinates_pixel_conversion import get_point_latlng_to_pixel_coordinates, get_latlng_to_pixel_coordinates |
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from src.prediction_api.predictors import samexporter_predict |
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from src.utilities.constants import CUSTOM_RESPONSE_MESSAGES |
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from src.utilities.utilities import base64_decode |
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def get_response(status: int, start_time: float, request_id: str, response_body: Dict = None) -> str: |
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""" |
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Return a response for frontend clients. |
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Args: |
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status: status response |
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start_time: request start time (float) |
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request_id: str |
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response_body: dict we embed into our response |
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Returns: |
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str: json response |
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""" |
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app_logger.debug(f"response_body:{response_body}.") |
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response_body["duration_run"] = time.time() - start_time |
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response_body["message"] = CUSTOM_RESPONSE_MESSAGES[status] |
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response_body["request_id"] = request_id |
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response = { |
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"statusCode": status, |
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"header": {"Content-Type": content_types.APPLICATION_JSON}, |
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"body": json.dumps(response_body), |
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"isBase64Encoded": False |
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} |
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app_logger.info(f"response type:{type(response)} => {response}.") |
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return json.dumps(response) |
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def get_parsed_bbox_points(request_input: Dict) -> Dict: |
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app_logger.info(f"try to parsing input request {request_input}...") |
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ne = request_input["ne"] |
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sw = request_input["sw"] |
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ne_latlng = [float(ne["lat"]), float(ne["lng"])] |
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sw_latlng = [float(sw["lat"]), float(sw["lng"])] |
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bbox = [ne_latlng, sw_latlng] |
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zoom = int(request_input["zoom"]) |
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for prompt in request_input["prompt"]: |
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app_logger.info(f"current prompt: {type(prompt)}, value:{prompt}.") |
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data = prompt["data"] |
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app_logger.info(f"current data point: {type(data)}, value:{data}.") |
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diff_pixel_coordinates_ne = get_latlng_to_pixel_coordinates(ne, data, zoom) |
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app_logger.info(f'current data by current prompt["data"]: {type(data)}, {data} => {diff_pixel_coordinates_ne}.') |
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prompt["data"] = [diff_pixel_coordinates_ne["x"], diff_pixel_coordinates_ne["y"]] |
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app_logger.debug(f"bbox {bbox}.") |
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app_logger.debug(f'request_input["prompt"]:{request_input["prompt"]}.') |
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app_logger.info(f"unpacking elaborated {request_input}...") |
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return { |
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"bbox": bbox, |
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"prompt": request_input["prompt"], |
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"zoom": zoom |
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} |
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def lambda_handler(event: dict, context: LambdaContext): |
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app_logger.info(f"start with aws_request_id:{context.aws_request_id}.") |
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start_time = time.time() |
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if "version" in event: |
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app_logger.info(f"event version: {event['version']}.") |
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try: |
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app_logger.debug(f"event:{json.dumps(event)}...") |
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app_logger.debug(f"context:{context}...") |
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try: |
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body = event["body"] |
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except Exception as e_constants1: |
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app_logger.error(f"e_constants1:{e_constants1}.") |
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body = event |
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app_logger.debug(f"body, #1: {type(body)}, {body}...") |
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if isinstance(body, str): |
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body_decoded_str = base64_decode(body) |
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app_logger.debug(f"body_decoded_str: {type(body_decoded_str)}, {body_decoded_str}...") |
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body = json.loads(body_decoded_str) |
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app_logger.info(f"body, #2: {type(body)}, {body}...") |
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try: |
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body_request = get_parsed_bbox_points(body) |
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body_response = samexporter_predict(body_request["bbox"], body_request["prompt"], body_request["zoom"]) |
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app_logger.info(f"output body_response:{body_response}.") |
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response = get_response(HTTPStatus.OK.value, start_time, context.aws_request_id, body_response) |
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except Exception as ex2: |
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app_logger.error(f"exception2:{ex2}.") |
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response = get_response(HTTPStatus.UNPROCESSABLE_ENTITY.value, start_time, context.aws_request_id, {}) |
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except Exception as ex1: |
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app_logger.error(f"exception1:{ex1}.") |
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response = get_response(HTTPStatus.INTERNAL_SERVER_ERROR.value, start_time, context.aws_request_id, {}) |
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app_logger.info(f"response_dumped:{response}...") |
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return response |
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