fixed app.py

app.py

@@ -184,15 +184,18 @@ class Chatbot:
     #     Answer = self.model_pipeline(message, context)
     #     return Answer
     def predict_semantic_search(self, message):
+        corpus_embeddings = bot._chatbot.prepare_sentences_vector(bot._chatbot.get_embeddings(bot._chatbot.df['Context']))
         message = message.strip()
-        query_embedding = self.embedding_model.encode([message], convert_to_tensor=True)[0]
-        corpus_embeddings = self.embedding_model.encode(self.df['Question'].tolist(), convert_to_tensor=True)
-        hits = util.semantic_search(query_embedding.unsqueeze(0), corpus_embeddings, top_k=1)
+        query_embedding = self.embedding_model.encode(message, convert_to_tensor=True)
+        query_embedding = query_embedding.to('cuda')
+        hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=1)
         hit = hits[0][0]
         context = self.df['Context'][hit['corpus_id']]
         Answer = self.model_pipeline(message, context)
         return Answer
 
+
+
     def predict_without_faiss(self,message):
         MostSimilarContext = ""
         min_distance = 1000
@@ -212,6 +215,8 @@ class Chatbot:
         return Answer
 
 bot = ChatbotModel()
+corpus_embeddings = bot._chatbot.get_embeddings(bot._chatbot.df['Context'])
+corpus_embeddings = bot._chatbot.prepare_sentences_vector(corpus_embeddings)
 
 """#Gradio"""