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"""lambda helper functions""" |
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import json |
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import logging |
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import time |
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from typing import Dict, List |
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from aws_lambda_powertools.event_handler import content_types |
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from src import app_logger |
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from src.io.coordinates_pixel_conversion import get_latlng_to_pixel_coordinates |
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from src.utilities.constants import CUSTOM_RESPONSE_MESSAGES |
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from src.utilities.type_hints import RawRequestInput |
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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.debug(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: RawRequestInput) -> Dict: |
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""" |
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Format the bbox and prompt request input |
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Args: |
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request_input: input dict |
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Returns: |
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Dict: |
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""" |
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app_logger.info(f"try to parsing input request {request_input}...") |
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bbox = request_input.bbox |
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app_logger.debug(f"request bbox: {type(bbox)}, value:{bbox}.") |
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ne = bbox.ne |
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sw = bbox.sw |
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app_logger.debug(f"request ne: {type(ne)}, value:{ne}.") |
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app_logger.debug(f"request sw: {type(sw)}, value:{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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new_zoom = int(request_input.zoom) |
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new_prompt_list = [] |
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for prompt in request_input.prompt: |
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app_logger.debug(f"current prompt: {type(prompt)}, value:{prompt}.") |
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current_point = get_latlng_to_pixel_coordinates(ne, sw, prompt.data, new_zoom, prompt.type) |
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app_logger.debug(f"current prompt: {type(current_point)}, value:{current_point}.") |
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new_prompt_data = [current_point['x'], current_point['y']] |
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app_logger.debug(f"new_prompt_data: {type(new_prompt_data)}, value:{new_prompt_data}.") |
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new_prompt = { |
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"type": prompt.type, |
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"data": new_prompt_data |
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} |
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if prompt.label is not None: |
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new_prompt["label"] = prompt.label |
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new_prompt_list.append(new_prompt) |
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app_logger.debug(f"bbox => {bbox}.") |
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app_logger.debug(f'request_input-prompt updated => {new_prompt_list}.') |
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app_logger.info(f"unpacking elaborated request...") |
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return { |
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"bbox": [ne_latlng, sw_latlng], |
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"prompt": new_prompt_list, |
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"zoom": new_zoom |
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} |
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def get_parsed_request_body(event: Dict): |
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""" |
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Parse the input request lambda event. |
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Args: |
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event: input dict |
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Returns: |
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RawRequestInput: parsed request input |
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""" |
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app_logger.info(f"event:{json.dumps(event)}...") |
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try: |
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raw_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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raw_body = event |
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app_logger.debug(f"raw_body, #1: {type(raw_body)}, {raw_body}...") |
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if isinstance(raw_body, str): |
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body_decoded_str = base64_decode(raw_body) |
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app_logger.debug(f"body_decoded_str: {type(body_decoded_str)}, {body_decoded_str}...") |
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raw_body = json.loads(body_decoded_str) |
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app_logger.info(f"body, #2: {type(raw_body)}, {raw_body}...") |
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parsed_body = RawRequestInput.model_validate(raw_body) |
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log_level = "DEBUG" if parsed_body.debug else "INFO" |
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app_logger.setLevel(log_level) |
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app_logger.warning(f"set log level to {logging.getLevelName(app_logger.log_level)}.") |
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return parsed_body |
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