Update metrics.py

metrics.py

@@ -1,6 +1,5 @@
 # metrics.py
 from model_loader import metrics_models
-from classifier import classify_toxic_comment
 import re
 
 def compute_semantic_similarity(original, paraphrased):
@@ -66,40 +65,4 @@ def compute_hallucination_score(original, paraphrased):
         return round(score, 2)
     except Exception as e:
         print(f"Error computing hallucination score: {str(e)}")
-        return None
-
-def compute_reward_scores(original, paraphrased):
-    """
-    Compute all reward scores for a paraphrase.
-    Returns a dictionary with empathy, toxicity, bias, hallucination, and overall reward.
-    """
-    try:
-        # Get toxicity from classifier
-        _, _, _, toxicity_score, bias_score, _, _, _, _, paraphrased_toxicity_score, paraphrased_bias_score, _, _ = classify_toxic_comment(paraphrased)
-        toxicity = paraphrased_toxicity_score if paraphrased_toxicity_score is not None else 0.5
-
-        # Compute other metrics
-        empathy = compute_empathy_score(paraphrased) or 0.5
-        bias = compute_bias_score(paraphrased) or 0.5
-        hallucination = compute_hallucination_score(original, paraphrased) or 0.5
-
-        # Overall reward: Weighted combination (adjust weights as needed)
-        reward = (0.4 * empathy) - (0.2 * toxicity) - (0.2 * bias) - (0.2 * hallucination)
-        reward = max(0.0, min(1.0, round(reward, 2)))
-
-        return {
-            "empathy": empathy,
-            "toxicity": toxicity,
-            "bias": bias,
-            "hallucination": hallucination,
-            "reward": reward
-        }
-    except Exception as e:
-        print(f"Error computing reward scores: {str(e)}")
-        return {
-            "empathy": 0.5,
-            "toxicity": 0.5,
-            "bias": 0.5,
-            "hallucination": 0.5,
-            "reward": 0.5
-        }
+        return None