fix app.py

app.py

@@ -75,19 +75,19 @@ class Chatbot:
     def load_data(self, path: str = DATA_PATH):
         self.df = pd.read_excel(path, sheet_name='Default')
         self.df['Context'] = pd.read_excel(path, sheet_name='mdeberta')['Context']
-        print('Load data done')
+        # print('Load data done')
 
     def load_model(self, model_name: str = DEFAULT_MODEL):
         self.model = AutoModelForQuestionAnswering.from_pretrained(MODEL_DICT[model_name])
         self.tokenizer = AutoTokenizer.from_pretrained(MODEL_DICT[model_name])
         self.model_name = model_name
-        print('Load model done')
+        # print('Load model done')
 
     def load_embedding_model(self, model_name: str = DEFAULT_SENTENCE_EMBEDDING_MODEL):
         if torch.cuda.is_available():  # Check if GPU is available
             self.embedding_model = SentenceTransformer(model_name, device='cpu')
         else: self.embedding_model = SentenceTransformer(model_name)
-        print('Load sentence embedding model done')
+        # print('Load sentence embedding model done')
 
     def set_vectors(self):
         self.vectors = self.prepare_sentences_vector(self.load_embeddings(EMBEDDINGS_PATH))
@@ -149,8 +149,9 @@ class Chatbot:
         similar_questions, similar_contexts, distances, indices = self.faiss_search(question_vector)
         Answers = [self.df['Answer'][i] for i in indices[0]]
         Answer = Answers[0]
-        
+
         return Answer
+    
 
     # Function to predict using BERT embedding
     def predict_bert_embedding(self,message):
@@ -181,19 +182,6 @@ class Chatbot:
         Answer = self.model_pipeline(message, context)
         return Answer
 
-    
-    def predict_semantic_search(self, message):
-        message = message.strip()
-        query_embedding = self.embedding_model.encode([message], convert_to_tensor=True)[0]  # Fix here
-        query_embedding = query_embedding.to('cpu')
-        corpus_embeddings = self.embedding_model.encode(self.df['Question'].tolist(), convert_to_tensor=True)  # Fix here
-        hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=1)
-        hit = hits[0][0]
-        context = self.df['Context'][hit['corpus_id']]
-        score = "{:.4f})".format(hit['score'])
-        Answer = self.model_pipeline(message, context)
-        return Answer
-
     def predict_without_faiss(self,message):
         MostSimilarContext = ""
         min_distance = 1000