Update app.py

app.py

@@ -40,17 +40,15 @@ def data_ingestion_from_directory():
     index = VectorStoreIndex.from_documents(documents)
     index.storage_context.persist(persist_dir=PERSIST_DIR)
 
-def handle_query(message, chat_history):
+def handle_query(message, history):
     # Prepare the chat history for context
-    context_str = ""
-    for user_message, bot_response in chat_history:
-        context_str += f"User asked: '{user_message}'\nBot answered: '{bot_response}'\n"
+    chat_history = [[msg["text"], ""] for msg in history]
 
     # Prepare the chat prompt template
     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}"
+            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']}"
         )
     ]
     text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
@@ -60,8 +58,8 @@ def handle_query(message, chat_history):
     index = load_index_from_storage(storage_context)
 
     # Use the Llama index to generate a response
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
-    answer = query_engine.query(message)
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str="")
+    answer = query_engine.query(message['text'])
 
     if hasattr(answer, 'response'):
         response = answer.response
@@ -71,7 +69,7 @@ def handle_query(message, chat_history):
         response = "Sorry, I couldn't find an answer."
 
     # Update chat history with the current interaction
-    chat_history.append([message, response])
+    chat_history.append([message['text'], response])
 
     return response
 
@@ -82,7 +80,7 @@ data_ingestion_from_directory()
 # Create the Gradio interface
 interface = gr.ChatInterface(
     fn=handle_query,
-    inputs=gr.Textbox(label="Ask me anything about the document...", placeholder="Type your question here."),
+    examples=[{"text": "hello"}, {"text": "hola"}, {"text": "merhaba"}],
     title="RedfernsTech Q&A Chatbot",
     description="Ask me anything about the uploaded document."
 )