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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | """Hugging Face Hub integration for the SQL error classifier."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any, Dict, Optional, Union
import joblib
from src.categories import load_categories
from src.cross_encoder_model import CrossEncoderClassifier
from src.model import DEFAULT_ENCODER, load_model
from src.multi_tower_model import MultiTowerClassifier, QueryContext
PROJECT_ROOT = Path(__file__).resolve().parent.parent
CONFIG_NAME = "config.json"
CLASSIFIER_NAME = "classifier.joblib"
CATEGORIES_NAME = "categories.json"
SUPPORTED_CONTEXT_MODELS = (CrossEncoderClassifier, MultiTowerClassifier)
class SQLLErrorClassifierHF:
"""
Hugging Face–compatible wrapper for SQL error classifiers.
Usage:
clf = SQLLErrorClassifierHF.from_pretrained("username/sql-error-classifier")
result = clf.predict(
question="...", schema="...", correct_query="...", student_query="..."
)
"""
def __init__(self, model, label_map: Dict[int, str]):
self.model = model
self.label_map = label_map
def predict(
self,
question: str,
schema: str,
correct_query: str,
student_query: str,
error_message: Optional[str] = None,
top_k: int = 3,
) -> Dict[str, Any]:
ctx = QueryContext(
question=question,
schema=schema,
correct_query=correct_query,
student_query=student_query,
error_message=error_message,
)
proba = self.model.predict_proba([ctx])[0]
classes = self.model.classes_
ranked = sorted(zip(classes, proba), key=lambda x: x[1], reverse=True)
best_id = int(ranked[0][0])
diagnostics: Dict[str, Any] = {}
if isinstance(self.model, CrossEncoderClassifier):
diagnostics["pair_scores"] = self.model.explain_pair_scores(ctx)
elif isinstance(self.model, MultiTowerClassifier):
diagnostics["similarities"] = self.model.explain_similarities(ctx)
return {
"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]
],
**diagnostics,
}
def save_pretrained(self, save_directory: Union[str, Path]) -> Path:
"""Save model artifacts in Hugging Face Hub layout."""
save_dir = Path(save_directory)
save_dir.mkdir(parents=True, exist_ok=True)
if isinstance(self.model, CrossEncoderClassifier):
payload = {
"model_type": "cross_encoder",
"cross_encoder_name": self.model.cross_encoder_name,
"batch_size": self.model.batch_size,
"max_length": self.model.max_length,
"scaler": self.model.scaler,
"classifier": self.model.clf,
"classes_": self.model.classes_,
}
config = {
"model_type": "cross_encoder",
"architecture": "cross-encoder-pairwise",
"cross_encoder_name": self.model.cross_encoder_name,
"batch_size": self.model.batch_size,
"num_labels": len(self.label_map),
"task": "sql-error-classification",
}
elif isinstance(self.model, MultiTowerClassifier):
payload = {
"model_type": "multi_tower",
"encoder_name": self.model.encoder_name,
"batch_size": self.model.batch_size,
"scaler": self.model.scaler,
"classifier": self.model.clf,
"classes_": self.model.classes_,
}
config = {
"model_type": "multi_tower",
"architecture": "multi-tower-semantic-comparison",
"encoder_name": self.model.encoder_name,
"batch_size": self.model.batch_size,
"num_labels": len(self.label_map),
"task": "sql-error-classification",
}
else:
raise ValueError("Only cross_encoder and multi_tower models can be published")
joblib.dump(payload, save_dir / CLASSIFIER_NAME)
with open(save_dir / CONFIG_NAME, "w") as f:
json.dump(config, f, indent=2)
categories = load_categories()
cat_data = [
{"id": c.id, "name": c.name, "description": c.description}
for c in categories
]
with open(save_dir / CATEGORIES_NAME, "w") as f:
json.dump(cat_data, f, indent=2)
return save_dir
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, Path],
*,
token: Optional[str] = None,
) -> "SQLLErrorClassifierHF":
"""Load from a local directory or Hugging Face Hub repo."""
path = _resolve_model_path(pretrained_model_name_or_path, token=token)
with open(path / CONFIG_NAME) as f:
config = json.load(f)
with open(path / CATEGORIES_NAME) as f:
categories = json.load(f)
label_map = {c["id"]: c["name"] for c in categories}
obj = joblib.load(path / CLASSIFIER_NAME)
model_type = config.get("model_type", obj.get("model_type"))
if model_type == "cross_encoder":
model = CrossEncoderClassifier(
cross_encoder_name=obj.get(
"cross_encoder_name",
config.get("cross_encoder_name", "cross-encoder/ms-marco-MiniLM-L6-v2"),
),
batch_size=obj.get("batch_size", 32),
max_length=obj.get("max_length", 512),
)
model.scaler = obj["scaler"]
model.clf = obj["classifier"]
model.classes_ = obj.get("classes_", obj["classifier"].classes_)
else:
model = MultiTowerClassifier(
encoder_name=obj.get("encoder_name", config.get("encoder_name", DEFAULT_ENCODER)),
batch_size=obj.get("batch_size", 256),
)
model.scaler = obj["scaler"]
model.clf = obj["classifier"]
model.classes_ = obj.get("classes_", obj["classifier"].classes_)
return cls(model=model, label_map=label_map)
def _resolve_model_path(
pretrained_model_name_or_path: Union[str, Path],
token: Optional[str] = None,
) -> Path:
local = Path(pretrained_model_name_or_path)
if local.exists() and (local / CONFIG_NAME).exists():
return local
from huggingface_hub import snapshot_download
return Path(
snapshot_download(
repo_id=str(pretrained_model_name_or_path),
token=token,
allow_patterns=[CONFIG_NAME, CLASSIFIER_NAME, CATEGORIES_NAME],
)
)
def package_for_hub(model_path: Path, output_dir: Path) -> Path:
"""Convert a local joblib model into HF Hub layout."""
sklearn_model = load_model(model_path)
if not isinstance(sklearn_model, SUPPORTED_CONTEXT_MODELS):
raise ValueError(
"Only cross_encoder and multi_tower models can be published to Hugging Face Hub"
)
label_map = {c.id: c.name for c in load_categories()}
wrapper = SQLLErrorClassifierHF(model=sklearn_model, label_map=label_map)
return wrapper.save_pretrained(output_dir)
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