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2f74c56 0fe28e9 2f74c56 0fe28e9 4213665 0fe28e9 ada7257 0fe28e9 4213665 ada7257 0fe28e9 af0cf81 2f74c56 ada7257 2f74c56 0fe28e9 4213665 0fe28e9 4213665 0fe28e9 af0cf81 2f74c56 ada7257 0fe28e9 ada7257 2f74c56 | 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 | # Portions of this file were developed with assistance from OpenAI ChatGPT/Codex and reviewed/modified by the author.
"""Inference helpers for the Campus Triage application.
Portions of this file were developed with assistance from OpenAI ChatGPT/Codex and reviewed/modified by the author.
"""
from __future__ import annotations
from pathlib import Path
from typing import Any
from campus_triage.config import CATEGORY_LABELS, ROUTING_RECOMMENDATIONS, URGENCY_LABELS
from campus_triage.features import keyword_explanation
from campus_triage.models import load_dual_classifier
EXAMPLE_MESSAGES = [
"My FAFSA documents still say incomplete and tuition is due tomorrow. Can someone help?",
"I cannot register for BIO 101 because there is a hold on my account.",
"The portal keeps showing an error when I try to submit my housing form.",
"I am worried about a student who said they might hurt themselves. Please call me ASAP.",
]
ROOT_DIR = Path(__file__).resolve().parents[2]
MODEL_PATH = ROOT_DIR / "models" / "tfidf_logistic_regression.joblib"
def resolve_deployed_model_path(model_path: Path = MODEL_PATH) -> Path | None:
"""Return the trained model path from the repository root."""
if model_path.exists():
return model_path
return None
def model_available(model_path: Path = MODEL_PATH) -> bool:
"""Return whether the deployed model artifact exists."""
return resolve_deployed_model_path(model_path) is not None
def model_search_diagnostics(model_path: Path = MODEL_PATH) -> str:
"""Return the absolute model path checked during deployment."""
return str(model_path)
def load_deployed_model(model_path: Path = MODEL_PATH) -> Any:
"""Load the deployed TF-IDF Logistic Regression model."""
resolved_model_path = resolve_deployed_model_path(model_path)
if resolved_model_path is None:
raise FileNotFoundError(f"No deployed model artifact found at {model_path}")
return load_dual_classifier(str(resolved_model_path))
def predict_message(message_text: str, model: Any | None = None) -> dict[str, Any]:
"""Predict triage fields and attach routing guidance."""
deployed_model = model or load_deployed_model()
prediction = deployed_model.predict_one(message_text)
prediction["routing_recommendation"] = ROUTING_RECOMMENDATIONS[prediction["category"]]
prediction["explanation"] = keyword_explanation(
message_text,
prediction["category"],
prediction["category_confidence"],
)
prediction["category_scores"] = {label: prediction["category_scores"].get(label, 0.0) for label in CATEGORY_LABELS}
prediction["urgency_scores"] = {label: prediction["urgency_scores"].get(label, 0.0) for label in URGENCY_LABELS}
return prediction
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