Merge pull request #27 from mertbozkir/grace/dev

Final PR for hackathon! 🧪

handlers/userinput.py

@@ -1,28 +1,37 @@
 import streamlit as st
+import ast
 from public import tpl_bot, tpl_user
 
 from .audio import handle_text_2_speech
 
 
 def handle_userinput(user_question):
     response = st.session_state.conversation({'question': user_question})
-    st.session_state.chat_history = response['chat_history']
+    chat_history_list = response['chat_history']
 
-    chat_history = st.session_state.chat_history
+    # Extract content from each message object
+    for i, message in enumerate(chat_history_list):
+        st.session_state.chat_history.insert(i, message.content)
+    # only keep the latest 12 chat history
+    if len(st.session_state.chat_history) >= 12:
+        st.session_state.chat_history = st.session_state.chat_history[:-2]
 
+    chat_history = st.session_state.chat_history
+    print(f"length of chat_history is {len(chat_history)}")
     for i, message in enumerate(chat_history):
-        if i % 2 == 0:  # User's message #FIXME
+        if i % 2 == 0:  # User's message 
+            print(f'User question is {message}')
             st.write(
                 tpl_user.replace(
-                    '{{MSG}}', message.content,
+                    '{{MSG}}', message,
                 ), unsafe_allow_html=True,
             )
         else:  # AI message
             st.write(
                 tpl_bot.replace(
-                    '{{MSG}}', message.content,
+                    '{{MSG}}', message,
                 ), unsafe_allow_html=True,
             )
 
-    if len(chat_history) > 0:
-        handle_text_2_speech(chat_history[-1].content)
+#    if len(chat_history) > 0:
+#        handle_text_2_speech(chat_history[-1].content)

utils/ai/open_ai.py

@@ -54,11 +54,12 @@ def upsert(data) -> Pinecone:
     return vectorstore
 
 
-def create_or_get_conversation_chain(vectorstore) -> BaseConversationalRetrievalChain:
-    template = """/
-        Can you give us the results as markdown code, in a funny way?
+def create_or_get_conversation_chain(vectorstore):
+    template = """
+        Return results as markdown code?
     """
-    llm = ChatOpenAI(model=OPENAI_CHAT_MODEL)
+    #llm = ChatOpenAI(model=OPENAI_CHAT_MODEL)
+    llm = ChatOpenAI()
     memory = ConversationBufferMemory(
         memory_key='chat_history', return_messages=True,
     )
@@ -70,5 +71,5 @@ def create_or_get_conversation_chain(vectorstore) -> BaseConversationalRetrieval
         memory=memory,
         condense_question_prompt=prompt_template,
     )
-    ic(f'conversation_chain is {conversation_chain}')
+    # ic(f'conversation_chain is {conversation_chain}')
     return conversation_chain

views/home.py

@@ -16,7 +16,7 @@ def home():
     if 'conversation' not in st.session_state:
         st.session_state.conversation = None
     if 'chat_history' not in st.session_state:
-        st.session_state.chat_history = None
+        st.session_state.chat_history = []
 
     st.header('Chat based on open-source documentation! :globe_with_meridians:')
     user_question = st.text_input('Ask a question about your dvc pipeline:')