Delete nli_detector.py

nli_detector.py

@@ -1,63 +0,0 @@
-"""
-Natural Language Inference detector – checks if generated response is consistent with input.
-"""
-import logging
-from typing import Optional
-import torch
-from transformers import pipeline
-
-logger = logging.getLogger(__name__)
-
-class NLIDetector:
-    """
-    Uses an NLI model to detect contradictions/hallucinations.
-    Returns entailment probability (0 to 1) for a given premise‑hypothesis pair.
-    """
-
-    def __init__(self, model_name: str = "microsoft/deberta-base-mnli"):
-        """
-        Args:
-            model_name: Hugging Face model identifier for NLI.
-                       Default is a public model that does not require authentication.
-        """
-        try:
-            # Request all scores to obtain probabilities for each class.
-            # The pipeline returns a list of lists of dicts: each dict has 'label' and 'score'.
-            self.pipeline = pipeline(
-                "text-classification",
-                model=model_name,
-                device=0 if torch.cuda.is_available() else -1,
-                return_all_scores=True
-            )
-            logger.info(f"NLI model {model_name} loaded with return_all_scores=True.")
-        except Exception as e:
-            logger.error(f"Failed to load NLI model: {e}")
-            self.pipeline = None
-
-    def check(self, premise: str, hypothesis: str) -> Optional[float]:
-        """
-        Returns probability of entailment (higher means more consistent).
-        Args:
-            premise: The original input/context.
-            hypothesis: The generated response.
-        Returns:
-            Float between 0 and 1, or None if model unavailable.
-        """
-        if self.pipeline is None:
-            return None
-        try:
-            # For a single input, the pipeline returns a list containing one element,
-            # which is itself a list of class-score dicts.
-            result = self.pipeline(f"{premise} </s></s> {hypothesis}")
-            # result[0] is the list of scores for all classes.
-            scores = result[0]
-            # Find the score corresponding to 'ENTAILMENT' (typical label for this model).
-            for item in scores:
-                if item['label'] == 'ENTAILMENT':
-                    return item['score']
-            # If the label is not found (should not happen), fall back to 0.0.
-            logger.warning("ENTAILMENT label not found in NLI output; returning 0.0.")
-            return 0.0
-        except Exception as e:
-            logger.error(f"NLI error: {e}")
-            return None