Update app.py

app.py

@@ -45,30 +45,6 @@ if "pdf_files" not in st.session_state:
 if "documents" not in st.session_state:
     st.session_state.documents = None
 
-if "embed_model" not in st.session_state:
-    st.session_state.embed_model = None
-
-if "llm_predictor" not in st.session_state:
-    st.session_state.llm_predictor = None
-
-if "service_context" not in st.session_state:
-    st.session_state.service_context = None    
-
-if "new_index" not in st.session_state:
-    st.session_state.new_index = None  
-
-if "storage_context" not in st.session_state:
-    st.session_state.storage_context = None  
-
-if "loadedindex" not in st.session_state:
-    st.session_state.loadedindex = None   
-
-if "query_engine" not in st.session_state:
-    st.session_state.query_engine = None      
-
-if "user_question " not in st.session_state:
-    st.session_state.user_question  = ""
-
 with st.sidebar:
     st.subheader("Upload your Documents Here: ")
     #if "pdf_files" not in st.session_state:
@@ -103,18 +79,22 @@ with st.sidebar:
     
 start_2 = timeit.default_timer() # Start timer
 st.write(f"向量模型加载开始：{start_2}")
-st.session_state.embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2'))
+if "embed_model" not in st.session_state:
+    st.session_state.embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2'))
 end_2 = timeit.default_timer() # Start timer
 st.write(f"向量模型加载加载结束：{end_2}")
 st.write(f"向量模型加载耗时：{end_2 - start_2}")
 
-st.session_state.llm_predictor = LLMPredictor(HuggingFaceHub(repo_id="HuggingFaceH4/starchat-beta", model_kwargs={"min_length":100, "max_new_tokens":1024, "do_sample":True, "temperature":0.2,"top_k":50, "top_p":0.95, "eos_token_id":49155}))
+if "llm_predictor" not in st.session_state:
+    st.session_state.llm_predictor = LLMPredictor(HuggingFaceHub(repo_id="HuggingFaceH4/starchat-beta", model_kwargs={"min_length":100, "max_new_tokens":1024, "do_sample":True, "temperature":0.2,"top_k":50, "top_p":0.95, "eos_token_id":49155}))
 
-st.session_state.service_context = ServiceContext.from_defaults(llm_predictor=st.session_state.llm_predictor, embed_model=st.session_state.embed_model)
+if "service_context" not in st.session_state:
+    st.session_state.service_context = ServiceContext.from_defaults(llm_predictor=st.session_state.llm_predictor, embed_model=st.session_state.embed_model)
 
 start_3 = timeit.default_timer() # Start timer
 st.write(f"向量库构建开始：{start_3}")
-st.session_state.new_index = VectorStoreIndex.from_documents(
+if "new_index" not in st.session_state:
+    st.session_state.new_index = VectorStoreIndex.from_documents(
     st.session_state.documents,
     service_context=st.session_state.service_context,
 )
@@ -124,18 +104,22 @@ st.write(f"向量库构建耗时：{end_3 - start_3}")
 
 st.session_state.new_index.storage_context.persist("st.session_state.directory_path")
 
-st.session_state.storage_context = StorageContext.from_defaults(persist_dir="st.session_state.directory_path")
+if "storage_context" not in st.session_state:
+    st.session_state.storage_context = StorageContext.from_defaults(persist_dir="st.session_state.directory_path")
 
 start_4 = timeit.default_timer() # Start timer
 st.write(f"向量库装载开始：{start_4}")
-st.session_state.loadedindex = load_index_from_storage(storage_context=st.session_state.storage_context, service_context=st.session_state.service_context)
+if "loadedindex" not in st.session_state:
+    st.session_state.loadedindex = load_index_from_storage(storage_context=st.session_state.storage_context, service_context=st.session_state.service_context)
 end_4 = timeit.default_timer() # Start timer
 st.write(f"向量库装载结束：{end_4}")
 st.write(f"向量库装载耗时：{end_4 - start_4}")
 
-st.session_state.query_engine = st.session_state.loadedindex.as_query_engine()
+if "query_engine" not in st.session_state:
+    st.session_state.query_engine = st.session_state.loadedindex.as_query_engine()
     
-st.session_state.user_question=st.text_input("Enter your query:")
+if "user_question " not in st.session_state:
+    st.session_state.user_question = st.text_input("Enter your query:")
 if st.session_state.user_question !="" and not st.session_state.user_question.strip().isspace() and not st.session_state.user_question == "" and not st.session_state.user_question.strip() == "" and not st.session_state.user_question.isspace():
     print("user question: "+st.session_state.user_question)
     with st.spinner("AI Thinking...Please wait a while to Cheers!"):