Source code for deeppavlov.models.api_requester.api_requester

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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import asyncio
from typing import Any, List, Dict, AsyncIterable

import requests

from deeppavlov.core.common.registry import register
from deeppavlov.core.models.component import Component


[docs]@register('api_requester') class ApiRequester(Component): """Component for forwarding parameters to APIs Args: url: url of the API. out: count of expected returned values or their names in a chainer. param_names: list of parameter names for API requests. debatchify: if ``True``, single instances will be sent to the API endpoint instead of batches. Attributes: url: url of the API. out: count of expected returned values. param_names: list of parameter names for API requests. debatchify: if True, single instances will be sent to the API endpoint instead of batches. """ def __init__(self, url: str, out: [int, list], param_names: [list, tuple] = (), debatchify: bool = False, *args, **kwargs): self.url = url self.param_names = param_names self.out_count = out if isinstance(out, int) else len(out) self.debatchify = debatchify
[docs] def __call__(self, *args: List[Any], **kwargs: Dict[str, Any]): """ Args: *args: list of parameters sent to the API endpoint. Parameter names are taken from self.param_names. **kwargs: named parameters to send to the API endpoint. If not empty, args are ignored Returns: result of the API request(s) """ data = kwargs or dict(zip(self.param_names, args)) if self.debatchify: batch_size = 0 for v in data.values(): batch_size = len(v) break assert batch_size > 0 async def collect(): return [j async for j in self.get_async_response(data, batch_size)] loop = asyncio.get_event_loop() response = loop.run_until_complete(collect()) else: response = requests.post(self.url, json=data).json() if self.out_count > 1: response = list(zip(*response)) return response
[docs] async def get_async_response(self, data: dict, batch_size: int) -> AsyncIterable: """Helper function for sending requests asynchronously if the API endpoint does not support batching Args: data: data to be passed to the API endpoint batch_size: requests count Yields: requests results parsed as json """ loop = asyncio.get_event_loop() futures = [ loop.run_in_executor( None, requests.post, self.url, None, {k: v[i] for k, v in data.items()} ) for i in range(batch_size) ] for r in await asyncio.gather(*futures): yield r.json()