Update app.py

app.py

@@ -68,97 +68,8 @@ with st.sidebar:
 if "messages" not in st.session_state:
     st.session_state.chain = init_chain()
     st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}]
-
-# Function for generating response using the last three conversations
-# def generate_response(prompt_input):
-#     # Initialize result
-#     result = ''
-    
-#     # Prepare conversation history: get the last 3 user and assistant messages
-#     conversation_history = ""
-#     recent_messages = st.session_state.messages[-3:]  # Last 3 user and assistant exchanges (each exchange is 2 messages)
-
-#     for message in recent_messages:
-#         conversation_history += f"{message['role']}: {message['content']}\n"
-    
-#     # Append the current user prompt to the conversation history
-#     conversation_history += f"user: {prompt_input}\n"
-
-#     # Invoke chain with the truncated conversation history
-#     res = st.session_state.chain.invoke(conversation_history)
-    
-#     # Process response (as in the original code)
-#     if res['result'].startswith('According to the provided context, '):
-#         res['result'] = res['result'][35:]
-#         res['result'] = res['result'][0].upper() + res['result'][1:]
-#     elif res['result'].startswith('Based on the provided context, '):
-#         res['result'] = res['result'][31:]
-#         res['result'] = res['result'][0].upper() + res['result'][1:]
-#     elif res['result'].startswith('According to the provided text, '):
-#         res['result'] = res['result'][34:]
-#         res['result'] = res['result'][0].upper() + res['result'][1:]
-#     elif res['result'].startswith('According to the context, '):
-#         res['result'] = res['result'][26:]
-#         res['result'] = res['result'][0].upper() + res['result'][1:]
-        
-    
-    # result += res['result']
-    
-    # # Process sources
-    # result += '\n\nSources: '
-    # sources = []
-    # for source in res["source_documents"]:
-    #     sources.append(source.metadata['source'][122:-4])  # Adjust as per your source format
-
-    # sources = list(set(sources))  # Remove duplicates
-    # source_list = ", ".join(sources)
-    
-    # result += source_list
-
-    # return result, res['result'], source_list
-    # return result, res['result']
-    # def generate_response(prompt_input):
-    # # Prepare conversation history: get the last 3 user and assistant messages
-    # conversation_history = ""
-    # recent_messages = st.session_state.messages[-3:]  # Last 3 user and assistant exchanges
-
-    # for message in recent_messages:
-    #     conversation_history += f"{message['role']}: {message['content']}\n"
-    
-    # # Append the current user prompt to the conversation history
-    # conversation_history += f"user: {prompt_input}\n"
-
-    # # Invoke chain with the truncated conversation history
-    # res = st.session_state.chain.invoke(conversation_history)
-    
-    # # Process response
-    # result_text = res['result']
-    # if result_text.startswith('According to the provided context, '):
-    #     result_text = result_text[35:].capitalize()
-    # elif result_text.startswith('Based on the provided context, '):
-    #     result_text = result_text[31:].capitalize()
-    # elif result_text.startswith('According to the provided text, '):
-    #     result_text = result_text[34:].capitalize()
-    # elif result_text.startswith('According to the context, '):
-    #     result_text = result_text[26:].capitalize()
-
-    # # Extract and format sources
-    # sources = []
-    # for source in res.get("source_documents", []):  # Safeguard with .get() in case sources are missing
-    #     source_path = source.metadata.get('source', '')
-    #     formatted_source = source_path[122:-4] if source_path else "Unknown source"
-    #     sources.append(formatted_source)
-    
-    # # Remove duplicates and combine into a single string
-    # unique_sources = list(set(sources))
-    # source_list = ", ".join(unique_sources)
-    
-    # # Combine response text with sources
-    # result_text += f"\n\n**Sources:** {source_list}" if source_list else "\n\n**Sources:** None"
-    
-    # return result_text
-
-    # return res['result']
+    st.session_state.query_counter = 0  # Track the number of user queries
+    st.session_state.conversation_history = ""  # Keep track of history for the LLM
 
 def generate_response(prompt_input):
     try:
@@ -166,19 +77,11 @@ def generate_response(prompt_input):
         retriever = st.session_state.chain.retriever
         relevant_context = retriever.get_relevant_documents(prompt_input)  # Retrieve context only for the current prompt
         
-        # Prepare full conversation history for the LLM
-        conversation_history = ""
-        for message in st.session_state.messages:
-            conversation_history += f"{message['role']}: {message['content']}\n"
-        
-        # Append the current user prompt to the conversation history
-        conversation_history += f"user: {prompt_input}\n"
-    
         # Format the input for the chain with the retrieved context
         formatted_input = (
             f"Context:\n"
             f"{' '.join([doc.page_content for doc in relevant_context])}\n\n"
-            f"Conversation:\n{conversation_history}"
+            f"Conversation:\n{st.session_state.conversation_history}user: {prompt_input}\n"
         )
     
         # Invoke the RetrievalQA chain directly with the formatted input
@@ -214,6 +117,9 @@ def generate_response(prompt_input):
         # # Combine response text with sources
         # result_text += f"\n\n**Sources:** {source_list}" if source_list else "\n\n**Sources:** None"
         
+        # Update conversation history
+        st.session_state.conversation_history += f"user: {prompt_input}\nassistant: {result_text}\n"
+    
         return result_text
 
     except Exception as e:
@@ -223,6 +129,7 @@ def generate_response(prompt_input):
         else:
             return f"❌ An error occurred: {str(e)}"
 
+
 # Display chat messages
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
@@ -230,6 +137,8 @@ for message in st.session_state.messages:
 
 # User-provided prompt for input box
 if prompt := st.chat_input(placeholder="Ask a question..."):
+    # Increment query counter
+    st.session_state.query_counter += 1
     # Append user query to session state
     st.session_state.messages.append({"role": "user", "content": prompt})
     with st.chat_message("user"):
@@ -244,6 +153,12 @@ if prompt := st.chat_input(placeholder="Ask a question..."):
         message_placeholder.markdown(response)  # Replace placeholder with actual response
         st.session_state.messages.append({"role": "assistant", "content": response})
 
+    # Check if query counter has reached the limit
+    if st.session_state.query_counter >= 10:
+        st.sidebar.warning("Conversation context has been reset after 10 queries.")
+        st.session_state.query_counter = 0  # Reset the counter
+        st.session_state.conversation_history = ""  # Clear conversation history for the LLM
+
 # Clear chat history function
 def clear_chat_history():
     # Clear chat messages (reset the assistant greeting)
@@ -252,8 +167,8 @@ def clear_chat_history():
     # Reinitialize the chain to clear any stored history (ensures it forgets previous user inputs)
     st.session_state.chain = init_chain()
 
-    # Clear any additional session state that might be remembering user inquiries
-    if "recent_user_messages" in st.session_state:
-        del st.session_state["recent_user_messages"]  # Clear remembered user inputs
+    # Clear the query counter and conversation history
+    st.session_state.query_counter = 0
+    st.session_state.conversation_history = ""
 
 st.sidebar.button('Clear Chat History', on_click=clear_chat_history)