100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
import argparse
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import json
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import pika
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from pyinfra.config import CONFIG, parse_disjunction_string
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from pyinfra.storage.storages import get_s3_storage
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--bucket_name", "-b", required=True)
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parser.add_argument(
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"--analysis_container", "-a", choices=["detr", "ner", "image", "conversion", "extraction"], required=True
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)
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args = parser.parse_args()
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return args
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def read_connection_params():
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credentials = pika.PlainCredentials(CONFIG.rabbitmq.user, CONFIG.rabbitmq.password)
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parameters = pika.ConnectionParameters(
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host=CONFIG.rabbitmq.host,
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port=CONFIG.rabbitmq.port,
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heartbeat=CONFIG.rabbitmq.heartbeat,
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credentials=credentials,
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)
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return parameters
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def make_channel(connection) -> pika.adapters.blocking_connection.BlockingChannel:
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channel = connection.channel()
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channel.basic_qos(prefetch_count=1)
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return channel
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def declare_queue(channel, queue: str):
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args = {"x-dead-letter-exchange": "", "x-dead-letter-routing-key": CONFIG.rabbitmq.queues.dead_letter}
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return channel.queue_declare(queue=queue, auto_delete=False, durable=True, arguments=args)
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def make_connection() -> pika.BlockingConnection:
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parameters = read_connection_params()
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connection = pika.BlockingConnection(parameters)
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return connection
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def build_message_bodies(analyse_container_type, bucket_name):
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def update_message(message_dict):
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if analyse_container_type == "detr" or analyse_container_type == "image":
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message_dict.update({"targetFileExtension": "ORIGIN.pdf.gz", "responseFileExtension": "IMAGE_INFO.json.gz"})
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if analyse_container_type == "conversion":
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message_dict.update(
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{
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"targetFileExtension": "ORIGIN.pdf.gz",
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"responseFileExtension": "json.gz",
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"operation": "conversion",
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"pages": [1, 2, 3],
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}
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)
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if analyse_container_type == "extraction":
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message_dict.update(
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{"targetFileExtension": "ORIGIN.pdf.gz", "responseFileExtension": "json.gz", "operation": "extraction"}
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)
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if analyse_container_type == "ner":
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message_dict.update(
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{"targetFileExtension": "TEXT.json.gz", "responseFileExtension": "NER_ENTITIES.json.gz"}
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)
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return message_dict
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storage = get_s3_storage()
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for pdf_name in storage.get_all_object_names(bucket_name):
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if "pdf" not in pdf_name:
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continue
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file_id = pdf_name.split(".")[0]
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dossier_id, file_id = file_id.split("/")
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message_dict = {"dossierId": dossier_id, "fileId": file_id}
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update_message(message_dict)
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yield json.dumps(message_dict).encode()
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def main(args):
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connection = make_connection()
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channel = make_channel(connection)
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declare_queue(channel, CONFIG.rabbitmq.queues.input)
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declare_queue(channel, CONFIG.rabbitmq.queues.output)
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for body in build_message_bodies(args.analysis_container, args.bucket_name):
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channel.basic_publish("", CONFIG.rabbitmq.queues.input, body)
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print(f"Put {body} on {CONFIG.rabbitmq.queues.input}")
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for method_frame, _, body in channel.consume(queue=CONFIG.rabbitmq.queues.output):
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print(f"Received {json.loads(body)}")
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channel.basic_ack(method_frame.delivery_tag)
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channel.close()
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if __name__ == "__main__":
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main(parse_args())
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