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"""https://zetcode.com/python/concurrent-http-requests/"""
import asyncio
import random
import time
import pandas as pd
import httpx
from os.path import exists
NUMBER_OF_CALLS = 1
headers = {"Content-Type": "application/json; charset=utf-8"}
# base_url = "https://tangibleai-mathtext-fastapi.hf.space/{endpoint}"
base_url = "http://localhost:7860/run/{endpoint}"
data_list_1 = {
"endpoint": "text2int",
"test_data": [
"one hundred forty five",
"twenty thousand nine hundred fifty",
"one hundred forty five",
"nine hundred eighty three",
"five million",
]
}
data_list_2 = {
"endpoint": "text2int-preprocessed",
"test_data": [
"one hundred forty five",
"twenty thousand nine hundred fifty",
"one hundred forty five",
"nine hundred eighty three",
"five million",
]
}
data_list_3 = {
"endpoint": "sentiment-analysis",
"test_data": [
"Totally agree",
"I like it",
"No more",
"I am not sure",
"Never",
]
}
# async call to endpoint
async def call_api(url, data, call_number, number_of_calls):
json = {"data": [data]}
async with httpx.AsyncClient() as client:
start = time.perf_counter() # Used perf_counter for more precise result.
response = await client.post(url=url, headers=headers, json=json, timeout=30)
end = time.perf_counter()
return {
"endpoint": url.split("/")[-1],
"test data": data,
"status code": response.status_code,
"response": response.json().get("data"),
"call number": call_number,
"number of calls": number_of_calls,
"start": start.__round__(4),
"end": end.__round__(4),
"delay": (end - start).__round__(4)
}
data_lists = [data_list_1, data_list_2, data_list_3]
results = []
async def main(number_of_calls):
for data_list in data_lists:
calls = []
for call_number in range(1, number_of_calls + 1):
url = base_url.format(endpoint=data_list["endpoint"])
data = random.choice(data_list["test_data"])
calls.append(call_api(url, data, call_number, number_of_calls))
r = await asyncio.gather(*calls)
results.extend(r)
start = time.perf_counter()
asyncio.run(main(NUMBER_OF_CALLS))
end = time.perf_counter()
print(end-start)
df = pd.DataFrame(results)
if exists("call_history.csv"):
df.to_csv(path_or_buf="call_history.csv", mode="a", header=False, index=False)
else:
df.to_csv(path_or_buf="call_history.csv", mode="w", header=True, index=False)
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