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fc7db98 | 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 | """
UI-ready inference wrapper for the two-head complexity model.
"""
from __future__ import annotations
import json
import pickle
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any
import numpy as np
import torch
from linguistic_features import extract_linguistic_features
from model_loading import load_model_from_checkpoint
from utils import (
EXPORT_DIR,
HARD_LEVELS,
ID_TO_LEVEL,
ID_TO_REASON,
LEVEL_ORDER,
MODELS,
REASON_ORDER,
difficult_class_probability,
tokenize_lcp_input,
)
@dataclass
class PredictionResult:
complexity_level: str
level_id: int
level_probs: dict[str, float]
difficult_class_prob: float # P(Hard)+P(Very Hard) from 5-class softmax (auxiliary)
reason: str | None
reason_id: int | None
reason_probs: dict[str, float] | None
def to_dict(self) -> dict[str, Any]:
return asdict(self)
class ComplexityPredictor:
"""Load once, call predict() from your UI or API."""
def __init__(
self,
model,
tokenizer,
meta: dict,
max_length: int = 192,
device: str | None = None,
):
self.model = model
self.tokenizer = tokenizer
self.meta = meta
self.model_key = meta["model_key"]
self.encoding = meta.get("encoding", "span_mark")
self.max_length = max_length
self.device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu"))
self.model.to(self.device)
self.model.eval()
@classmethod
def from_checkpoint(cls, checkpoint_path: str | Path, device: str | None = None) -> "ComplexityPredictor":
dev = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu"))
model, tokenizer, meta = load_model_from_checkpoint(checkpoint_path, dev)
return cls(model, tokenizer, meta, device=str(dev))
@classmethod
def load(cls, export_dir: str | Path, device: str | None = None) -> "ComplexityPredictor":
export_dir = Path(export_dir)
with open(export_dir / "config.json") as f:
config = json.load(f)
from transformers import AutoTokenizer
from two_head_model import TwoHeadModel
model_key = config["model_key"]
tokenizer = AutoTokenizer.from_pretrained(export_dir / "tokenizer")
model = TwoHeadModel(
model_name=MODELS[model_key],
pooling_mode=config.get("pooling", "span"),
use_linguistic_features=config.get("use_linguistic_features", False),
)
model.encoder.resize_token_embeddings(len(tokenizer))
weights = torch.load(export_dir / "model_weights.pt", map_location="cpu", weights_only=True)
model.load_state_dict(weights)
model.set_tgt_token_ids(config["tgt_id"], config.get("tgt_end_id"))
meta = {
"model_key": model_key,
"encoding": config.get("encoding", "span_mark"),
"pooling": config.get("pooling", "span"),
"use_linguistic_features": config.get("use_linguistic_features", False),
"tgt_id": config["tgt_id"],
"tgt_end_id": config.get("tgt_end_id"),
"feat_mean": np.array(config["feat_mean"]) if config.get("feat_mean") else None,
"feat_std": np.array(config["feat_std"]) if config.get("feat_std") else None,
}
return cls(model, tokenizer, meta, max_length=config.get("max_length", 192), device=device)
def save(self, export_dir: str | Path) -> Path:
export_dir = Path(export_dir)
export_dir.mkdir(parents=True, exist_ok=True)
config = {
"model_key": self.model_key,
"model_name": MODELS[self.model_key],
"tgt_id": self.meta.get("tgt_id"),
"tgt_end_id": self.meta.get("tgt_end_id"),
"encoding": self.encoding,
"pooling": self.meta.get("pooling", "span"),
"use_linguistic_features": self.meta.get("use_linguistic_features", False),
"feat_mean": self.meta.get("feat_mean").tolist() if self.meta.get("feat_mean") is not None else None,
"feat_std": self.meta.get("feat_std").tolist() if self.meta.get("feat_std") is not None else None,
"max_length": self.max_length,
"level_labels": LEVEL_ORDER,
"reason_labels": REASON_ORDER,
"hard_levels": list(HARD_LEVELS),
"inference_rule": "Reason is returned only when predicted level is Hard or Very Hard.",
"task": "5_class_complexity_prediction",
"outputs": "discrete_level_and_optional_reason_class",
}
with open(export_dir / "config.json", "w") as f:
json.dump(config, f, indent=2)
torch.save(self.model.state_dict(), export_dir / "model_weights.pt")
self.tokenizer.save_pretrained(export_dir / "tokenizer")
return export_dir
def save_pickle(self, pickle_path: str | Path) -> Path:
pickle_path = Path(pickle_path)
pickle_path.parent.mkdir(parents=True, exist_ok=True)
with open(pickle_path, "wb") as f:
pickle.dump(self, f)
return pickle_path
@classmethod
def load_pickle(cls, pickle_path: str | Path, device: str | None = None) -> "ComplexityPredictor":
with open(pickle_path, "rb") as f:
obj = pickle.load(f)
if not isinstance(obj, cls):
raise TypeError(f"Expected ComplexityPredictor, got {type(obj)}")
if device:
obj.device = torch.device(device)
obj.model.to(obj.device)
obj.model.eval()
return obj
@torch.no_grad()
def predict(self, sentence: str, target_word: str, corpus: str | None = None) -> PredictionResult:
enc = tokenize_lcp_input(
self.tokenizer,
sentence,
target_word,
encoding=self.encoding,
max_length=self.max_length,
corpus=corpus,
)
enc = {k: v.to(self.device) for k, v in enc.items()}
kwargs = {"input_ids": enc["input_ids"], "attention_mask": enc["attention_mask"]}
if self.meta.get("use_linguistic_features"):
feats = extract_linguistic_features(sentence, target_word)
if self.meta.get("feat_mean") is not None:
feats = (feats - self.meta["feat_mean"]) / self.meta["feat_std"]
kwargs["linguistic_features"] = torch.tensor(feats, dtype=torch.float32).unsqueeze(0).to(self.device)
out = self.model(**kwargs)
level_probs_t = torch.softmax(out.level_logits, dim=-1)[0].cpu()
reason_probs_t = torch.softmax(out.reason_logits, dim=-1)[0].cpu()
level_id = int(level_probs_t.argmax())
level_label = ID_TO_LEVEL[level_id]
level_probs = {ID_TO_LEVEL[i]: float(level_probs_t[i]) for i in range(5)}
diff_prob = difficult_class_probability(level_probs_t)
reason_label = None
reason_id = None
reason_probs = None
if level_label in HARD_LEVELS:
reason_id = int(reason_probs_t.argmax())
reason_label = ID_TO_REASON[reason_id]
reason_probs = {ID_TO_REASON[i]: float(reason_probs_t[i]) for i in range(3)}
return PredictionResult(
complexity_level=level_label,
level_id=level_id,
level_probs=level_probs,
difficult_class_prob=diff_prob,
reason=reason_label,
reason_id=reason_id,
reason_probs=reason_probs,
)
def predict_batch(self, items: list[dict[str, str]]) -> list[PredictionResult]:
return [self.predict(item["sentence"], item["target_word"], item.get("corpus")) for item in items]
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