Update app.py

app.py

@@ -108,8 +108,6 @@ def BM25_retrieval(query, seg_question_corpus, top_BM25):
 def SimCSE_retrieval(query, SimCSE_set, top_Sim):
   from sentence_transformers import CrossEncoder
   query = my_pre_processing(query)
-  # Sim_CSE_model_question = SentenceTransformer('VoVanPhuc/sup-SimCSE-VietNamese-phobert-base')
-  # Sim_CSE_word_ques_embeddings = torch.load('/content/drive/MyDrive/Study/Năm 3/CS336-IR/model/word_ques_embeddings.pth')
   Sim_CSE_model_question = SimCSE_set[0]
   Sim_CSE_word_ques_embeddings = SimCSE_set[1]
 
@@ -123,8 +121,6 @@ def Para_retriveval(query, para_set, top_para):
   query = my_pre_processing(query)
   from sentence_transformers import SentenceTransformer, CrossEncoder
   import torch
-  # retri_model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-  # para_question_embeddings = torch.load('/content/drive/MyDrive/Study/Năm 3/CS336-IR/model/para_embeddings.pth')
   retri_model = para_set[0]
   para_question_embeddings = para_set[1]
 
@@ -198,7 +194,7 @@ def retrieval(query, question_corpus, seg_question_corpus, models, top_n = 15, t
 
 client = OpenAI(
     # defaults to os.environ.get("OPENAI_API_KEY")
-    api_key= <API_key>,)
+    api_key= <API_key>,) # điền API key ở đây
 
 def chat_gpt(prompt):
     response = client.chat.completions.create(
@@ -241,7 +237,7 @@ def RAG(query):
   answer['reference'] = f"https://dichvucong.gov.vn/p/home/dvc-tthc-thu-tuc-hanh-chinh-chi-tiet.html?ma_thu_tuc={info['ID']}"
   return answer
 
-#print(RAG("tôi muốn biết cách làm biển sổ xe lần lần đầu"))
+
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()
     msg = gr.Textbox()