def test_a_full_analysis_request_is_executed(self):
"""
...then verify that whole flow works as expected
"""
response_json = {
"success": True,
"data": {
"content": "Facebook is looking at buying U.S. startup for $6 million",
"language": "en",
"version": "sensei: 3.1.0; disambiguator: 15.0-QNTX-2016",
"knowledge": [
{
"label": "organization.company",
"properties": [
{"type": "WikiDataId", "value": "Q380"}
],
"syncon": 288110,
}
],
"phrases": [
{"tokens": [0], "type": "PP", "start": 54, "end": 65},
],
"tokens": [
{
"syncon": 62653,
"start": 74,
"end": 83,
"type": "NOU",
"lemma": "long time",
"pos": "NOUN",
"dependency": {"id": 11, "head": 7, "label": "nmod"},
"morphology": "Number=Sing",
"paragraph": 0,
"sentence": 0,
"phrase": 4,
"atoms": [
{
"start": 74,
"end": 78,
"type": "ADJ",
"lemma": "long",
},
],
}
],
"mainSentences": [],
"mainPhrases": [],
"mainLemmas": [],
"mainSyncons": [],
"entities": [],
"topics": [],
"sentences": [{"phrases": [0], "start": 0, "end": 100}],
"paragraphs": [],
},
}
response = MagicMock(text="e@i")
response.status_code = 200
response.json.return_value = response_json
self.patched_post.return_value = response
client = ExpertAiClient()
request_body = {"document": {"text": "text"}}
data_model = client.full_analysis(
body=request_body, params={"language": "es"}
)
# two POST requests are made, one for the token and one for analysis
self.assertEqual(self.patched_post.call_count, 2)
self.assertEqual(data_model.sentences[0].phrases[0].type_, "PP")
from expertai.nlapi.cloud.client import ExpertAiClient
client = ExpertAiClient()
text = "Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half.'"
language = 'en'
output = client.full_analysis(body={"document": {
"text": text
}},
params={'language': language})
# Output arrays size
print("Output arrays size:")
print("knowledge: ", len(output.knowledge))
print("paragraphs: ", len(output.paragraphs))
print("sentences: ", len(output.sentences))
print("phrases: ", len(output.phrases))
print("tokens: ", len(output.tokens))
print("mainSentences: ", len(output.main_sentences))
print("mainPhrases: ", len(output.main_phrases))
print("mainLemmas: ", len(output.main_lemmas))
print("mainSyncons: ", len(output.main_syncons))
print("topics: ", len(output.topics))
print("entities: ", len(output.entities))
print("entities: ", len(output.relations))
print("sentiment.items: ", len(output.sentiment.items))
