Update model_loader.py

model_loader.py

@@ -1,7 +1,6 @@
 # model_loader.py
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM
 from sentence_transformers import SentenceTransformer
-from transformers import pipeline
 
 # Classifier Model (XLM-RoBERTa for toxicity classification)
 class ClassifierModel:
@@ -39,28 +38,16 @@ class ParaphraserModel:
         except Exception as e:
             raise Exception(f"Error loading paraphrase model or tokenizer: {str(e)}")
 
-# Metrics Models (Sentence-BERT, Emotion Classifier, NLI)
+# Metrics Models (Sentence-BERT only)
 class MetricsModels:
     def __init__(self):
         self.sentence_bert_model = None
-        self.emotion_classifier = None
-        self.nli_classifier = None
 
     def load_sentence_bert(self):
         if self.sentence_bert_model is None:
             self.sentence_bert_model = SentenceTransformer('all-MiniLM-L6-v2')
         return self.sentence_bert_model
 
-    def load_emotion_classifier(self):
-        if self.emotion_classifier is None:
-            self.emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=None)
-        return self.emotion_classifier
-
-    def load_nli_classifier(self):
-        if self.nli_classifier is None:
-            self.nli_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-        return self.nli_classifier
-
 # Singleton instances
 classifier_model = ClassifierModel()
 paraphraser_model = ParaphraserModel()