Update app.py

app.py

@@ -143,6 +143,34 @@ from langchain.chains import ConversationalRetrievalChain
 #         )
 
     
+#     # Set up memory for conversation history
+#     memory = ConversationBufferMemory(
+#         memory_key='chat_history', return_messages=True
+#     )
+    
+#     # Create conversational retrieval chain
+#     conversation_chain = ConversationalRetrievalChain.from_llm(
+#         llm=llm,
+#         retriever=vectorstore.as_retriever(),
+#         memory=memory
+#     )
+    
+#     return conversation_chain
+
+# def get_conversation_chain(vectorstore, tokenH):
+#     if not tokenH:
+#         raise ValueError("API token is required to initialize the HuggingFaceHub model")
+
+#     # Initialize the HuggingFaceHub model with the correct token
+#     try:
+#         llm = HuggingFaceHub(
+#             repo_id="Qwen/Qwen2.5-7B-Instruct", 
+#             token=tokenH,
+#             use_auth_token=True
+#         )
+#     except Exception as e:
+#         raise ValueError(f"Error initializing HuggingFaceHub model: {str(e)}")
+
 #     # Set up memory for conversation history
 #     memory = ConversationBufferMemory(
 #         memory_key='chat_history', return_messages=True
@@ -185,6 +213,7 @@ def get_conversation_chain(vectorstore, tokenH):
     
     return conversation_chain
     
+    
 def handle_userinput(user_question):
     # 대화 체인을 사용하여 사용자 질문에 대한 응답을 생성합니다.
     response = st.session_state.conversation({'question': user_question})
@@ -215,15 +244,19 @@ def main():
     st.header("Chat with multiple Files :",)
     st.header("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21")
     tokenH = st.text_input("Paste your TOKEN key (sk-...)", key="token_input_1")
+    
+    # Validate if the token is empty
+    if not tokenH:
+        st.warning("Please enter a valid HuggingFace API token.")
+        return
+
     user_question = st.text_input("Ask a question about your documents:")
     if user_question:
         handle_userinput(user_question)
 
     with st.sidebar:
         tokenH_sidebar = st.text_input("Paste your TOKEN key (sk-...)", key="token_input_2")
-        tokenH=tokenH_sidebar
-        # if openai_key:
-        #     os.environ["OPENAI_API_KEY"] = openai_key
+        tokenH = tokenH_sidebar or tokenH  # fallback to main input if sidebar is empty
 
         st.subheader("Your documents")
         docs = st.file_uploader(
@@ -257,7 +290,5 @@ def main():
                 # create conversation chain
                 st.session_state.conversation = get_conversation_chain(vectorstore, tokenH)
 
-
-
 if __name__ == '__main__':
     main()