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90 lines
2.5 KiB
90 lines
2.5 KiB
#!/usr/bin/env python
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import os
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import requests
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import pandas as pd
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import numpy as np
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from iso639 import languages
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from asyncio_pool import AioPool
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import asyncio as aio
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API_URL = "http://localhost:8000"
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def test_get_supported_languages():
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response = requests.get(API_URL + "/api/language/predict")
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assert response.status_code == 200
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resp_body = response.json()
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assert isinstance(resp_body["supported_languages"], list)
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assert isinstance(resp_body["supported_languages"][0], str)
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def test_post_language_detection():
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response = requests.post(
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API_URL + "/api/language/predict", json={"text": "Testing this application"}
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)
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assert response.status_code == 200
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resp_body = response.json()
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assert resp_body["lang"] == "English"
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def getSupportedLanguages():
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allLangs = requests.get(API_URL + "/api/language/predict").json()
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allLangs = allLangs["supported_languages"]
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return allLangs
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def getTestData():
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cacheFile = "./test.csv"
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testDataUrl = "https://huggingface.co/datasets/papluca/language-identification/raw/main/test.csv"
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supportedLangs = getSupportedLanguages()
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if os.path.exists(cacheFile):
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data = pd.read_csv(cacheFile)
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else:
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data = pd.read_csv(testDataUrl)
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data.to_csv(cacheFile)
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data.rename(columns={"labels": "language", "text": "Text"}, inplace=True)
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data["language"] = data["language"].apply(lambda code: languages.part1[code].name)
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data = data[data["language"].isin(supportedLangs)]
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return data
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async def task(row):
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row = row[1]
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response = requests.post(
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API_URL + "/api/language/predict", json={"text": row["Text"]}
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)
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resp_body = response.json()
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return [resp_body, row]
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async def evaluateAccuracy():
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data = getTestData()
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stats = {}
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failed = []
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pool = AioPool(10)
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results = await pool.map(task, data.iterrows())
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print('complted %d requests ' % len(data))
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for [resp_body, row] in results:
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lang = row["language"]
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if lang not in stats:
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stats[lang] = {"total": 0, "failed": 0}
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if not resp_body["lang"] == lang:
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stats[lang]["failed"] += 1
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failed.append(row)
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stats[lang]["total"] += 1
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stats = pd.DataFrame(stats).T
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stats["success_percent"] = (1 - (stats["failed"] / stats["total"])) * 100.0
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stats.sort_values("success_percent", ascending=False, inplace=True)
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print(stats)
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return stats
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if __name__ == "__main__":
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loop = aio.new_event_loop()
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loop.run_until_complete(evaluateAccuracy())
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