Update app.py

app.py

@@ -48,7 +48,7 @@ def handle_query(message, history):
     chat_text_qa_msgs = [
         (
             "user",
-            f"You are now the RedFerns Tech chatbot. Your aim is to provide answers to the user based on the conversation flow only.\n\nQuestion:\n{message['text']}"
+            f"You are now the RedFerns Tech chatbot. Your aim is to provide answers to the user based on the conversation flow only.\n\nQuestion:\n{message}"
         )
     ]
     text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
@@ -59,7 +59,7 @@ def handle_query(message, history):
 
     # Use the Llama index to generate a response
     query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str="")
-    answer = query_engine.query(message['text'])
+    answer = query_engine.query(message)
 
     if hasattr(answer, 'response'):
         response = answer.response
@@ -69,10 +69,11 @@ def handle_query(message, history):
         response = "Sorry, I couldn't find an answer."
 
     # Update chat history with the current interaction
-    chat_history.append([message['text'], response])
+    chat_history.append([message, response])
 
     return response
 
+
 # Example usage: Process PDF ingestion from directory
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()