Add exception logging

examples/simple/predict_domain.py

@@ -1,77 +0,0 @@
-from typing import Dict, Iterable, List
-
-from great_ai import GreatAI, use_model
-from pydantic import BaseModel
-from sklearn.pipeline import Pipeline
-
-from helpers import lemmatize, preprocess
-
-
-class DomainPrediction(BaseModel):
-    domain: str
-    probability: float
-    explanation: List[str]
-
-
-@GreatAI.deploy()
-@use_model("small-domain-prediction-v2", version="latest")
-def predict_domain(
-    text: str, model: Pipeline, cut_off_probability: float = 0.2
-) -> List[DomainPrediction]:
-    """
-    Predict the scientific domain of the input text.
-    Return labels until their sum likelihood is larger than cut_off_probability.
-    """
-    assert 0 <= cut_off_probability <= 1
-
-    text = preprocess(text)
-
-    feature_names = model.named_steps["vectorizer"].get_feature_names_out()
-
-    token_mapping = {lemmatize(original): original for original in text.split(" ")}
-
-    features = model.named_steps["vectorizer"].transform(
-        [" ".join(token_mapping.keys())]
-    )
-    prediction = model.named_steps["classifier"].predict_proba(features)[0]
-    best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True)
-
-    results: List[DomainPrediction] = []
-    for class_index, probability in best_classes:
-        weights = model.named_steps["classifier"].feature_log_prob_[class_index]
-        domain = model.named_steps["classifier"].classes_[class_index]
-
-        results.append(
-            DomainPrediction(
-                domain=domain,
-                probability=round(probability * 100),
-                explanation=_get_explanation(
-                    feature_names=feature_names,
-                    features=features.A[0],
-                    weights=weights,
-                    token_mapping=token_mapping,
-                ),
-            )
-        )
-
-        if sum(r.probability for r in results) >= cut_off_probability * 100:
-            break
-
-    return results
-
-
-def _get_explanation(
-    feature_names: Iterable[str],
-    features: Iterable[float],
-    weights: Iterable[float],
-    token_mapping: Dict[str, str],
-) -> List[str]:
-    influential = [
-        (weight, name)
-        for weight, value, name in zip(weights, features, feature_names)
-        if value
-    ]
-
-    most_influential = sorted(influential, reverse=True)[:5]
-
-    return [token_mapping[name] for _, name in most_influential]