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9b2cded | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | """Inference API for SQL error classification."""
from __future__ import annotations
import argparse
import json
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import List, Optional
from src.categories import id_to_name, load_categories
from src.model import DEFAULT_MODEL_PATH, combine_features, load_model
from src.cross_encoder_model import (
CrossEncoderClassifier,
FineTunedCrossEncoderClassifier,
)
from src.multi_tower_model import MultiTowerClassifier, QueryContext
CONTEXT_MODELS = (
CrossEncoderClassifier,
FineTunedCrossEncoderClassifier,
MultiTowerClassifier,
)
@dataclass
class Prediction:
label_id: int
label_name: str
confidence: float
top_k: List[dict]
similarities: Optional[dict] = None
pair_scores: Optional[dict] = None
class SQLErrorClassifier:
"""Classifier wrapper for playground integration."""
def __init__(self, model_path: Path = DEFAULT_MODEL_PATH):
self.model = load_model(model_path)
self.label_map = id_to_name(load_categories())
def predict(
self,
query: str,
error_message: Optional[str] = None,
schema: Optional[str] = None,
question: Optional[str] = None,
correct_query: Optional[str] = None,
top_k: int = 3,
) -> Prediction:
if isinstance(self.model, CONTEXT_MODELS):
if not all([schema, question, correct_query]):
raise ValueError(
"context models require schema, question, and correct_query"
)
ctx = QueryContext(
question=question,
schema=schema,
correct_query=correct_query,
student_query=query,
error_message=error_message,
)
proba = self.model.predict_proba([ctx])[0]
similarities = (
self.model.explain_similarities(ctx)
if isinstance(self.model, MultiTowerClassifier)
else None
)
pair_scores = (
self.model.explain_pair_scores(ctx)
if isinstance(self.model, CrossEncoderClassifier)
else None
)
else:
pair_scores = None
similarities = None
text = combine_features(
queries=[query],
error_messages=[error_message] if error_message else None,
schemas=[schema] if schema else None,
questions=[question] if question else None,
)[0]
proba = self.model.predict_proba([text])[0]
similarities = None
classes = self.model.classes_
ranked = sorted(zip(classes, proba), key=lambda x: x[1], reverse=True)
best_id = int(ranked[0][0])
return Prediction(
label_id=best_id,
label_name=self.label_map[best_id],
confidence=float(ranked[0][1]),
top_k=[
{
"label_id": int(cls),
"label_name": self.label_map[int(cls)],
"confidence": float(p),
}
for cls, p in ranked[:top_k]
],
similarities=similarities,
pair_scores=pair_scores,
)
def main() -> None:
parser = argparse.ArgumentParser(description="Classify SQL error type")
parser.add_argument("--query", type=str, required=True)
parser.add_argument("--correct-query", type=str, default=None)
parser.add_argument("--error-message", type=str, default=None)
parser.add_argument("--schema", type=str, default=None)
parser.add_argument("--question", type=str, default=None)
parser.add_argument("--model", type=Path, default=DEFAULT_MODEL_PATH)
parser.add_argument("--top-k", type=int, default=3)
args = parser.parse_args()
clf = SQLErrorClassifier(args.model)
result = clf.predict(
args.query,
args.error_message,
args.schema,
args.question,
args.correct_query,
top_k=args.top_k,
)
print(json.dumps(asdict(result), indent=2))
if __name__ == "__main__":
main()
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