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Imrpove examples

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Andras Schmelczer 2022-05-26 13:33:27 +02:00
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21
examples/simple/predict_domain.py
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@ -1,15 +1,10 @@
from great_ai import configure, create_service, log_argument, log_metric, use_model
configure(development_mode_override=True)
import re
from typing import Dict, Iterable, List
from preprocess import preprocess
from great_ai import GreatAI, use_model
from pydantic import BaseModel
from sklearn.pipeline import Pipeline
from great_ai.utilities.clean import clean
from helpers import lemmatize, preprocess
class DomainPrediction(BaseModel):
@ -18,26 +13,22 @@ class DomainPrediction(BaseModel):
explanation: List[str]
@create_service
@GreatAI.deploy()
@use_model("small-domain-prediction-v2", version="latest")
@log_argument("text", validator=lambda t: len(t) > 0)
def predict_domain(
text: str, model: Pipeline, cut_off_probability: float = 0.2
) -> List[DomainPrediction]:
assert 0 <= cut_off_probability <= 1
"""
Predict the scientific domain of the input text.
Return labels until their sum likelihood is larger than cut_off_probability.
"""
log_metric("text_length", len(text))
assert 0 <= cut_off_probability <= 1
cleaned = clean(text, convert_to_ascii=True)
text = re.sub(r"[^a-zA-Z0-9]", " ", cleaned)
text = preprocess(text)
feature_names = model.named_steps["vectorizer"].get_feature_names_out()
token_mapping = {preprocess(original): original for original in text.split(" ")}
token_mapping = {lemmatize(original): original for original in text.split(" ")}
features = model.named_steps["vectorizer"].transform(
[" ".join(token_mapping.keys())]