Update app.py

app.py

@@ -2,12 +2,6 @@ __import__('pysqlite3')
 import sys
 sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
 
-# DATABASES = {
-#     'default': {
-#         'ENGINE': 'django.db.backends.sqlite3',
-#         'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
-#     }
-# }
 import streamlit as st
 from huggingface_hub import InferenceClient
 from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, PromptTemplate
@@ -119,7 +113,7 @@ def rag_query(query):
     ]
 
     # Get the response from the client
-    response_content = client.chat_completion(messages=messages, max_tokens=500, stream=False)
+    response_content = client.chat_completion(messages=messages, max_tokens=1024, stream=False)
 
     # Process the response content
     response = response_content.choices[0].message.content.split("Answer:")[-1].strip()
@@ -127,7 +121,7 @@ def rag_query(query):
     # If the response is empty or very short, or if no relevant documents were found, use the LLM's default knowledge
     if not context or len(response.split()) < 35 or not retrieved_docs:
         messages = [{"role": "user", "content": query}]
-        response_content = client.chat_completion(messages=messages, max_tokens=500, stream=False)
+        response_content = client.chat_completion(messages=messages, max_tokens=1024, stream=False)
         response = response_content.choices[0].message.content
 
     # Append the response to memory
@@ -135,35 +129,32 @@ def rag_query(query):
 
     return response
 
-def process_feedback(query, response, feedback):
-   # st.write(f"Feedback received: {'👍' if feedback else '👎'} for query: {query}")
-    if feedback:
-        # If thumbs up, store the response in memory buffer
-        memory.chat_memory.add_ai_message(response)
-    else:
-        # If thumbs down, remove the response from memory buffer and regenerate the response
-       # memory.chat_memory.messages = [msg for msg in memory.chat_memory.messages if msg.get("content") != response]
-        new_query=f"{query}. Give better response"
-        new_response = rag_query(new_query)
-        st.markdown(new_response)
-        memory.chat_memory.add_ai_message(new_response)
+# def process_feedback(query, response, feedback):
+#    # st.write(f"Feedback received: {'👍' if feedback else '👎'} for query: {query}")
+#     if feedback:
+#         # If thumbs up, store the response in memory buffer
+#         memory.chat_memory.add_ai_message(response)
+#     else:
+#         # If thumbs down, remove the response from memory buffer and regenerate the response
+#        # memory.chat_memory.messages = [msg for msg in memory.chat_memory.messages if msg.get("content") != response]
+#         new_query=f"{query}. Give better response"
+#         new_response = rag_query(new_query)
+#         st.markdown(new_response)
+#         memory.chat_memory.add_ai_message(new_response)
 
 # Streamlit interface
 
-st.title("Welcome to our RAG-Based Chatbot")
+st.title("Chào mừng bạn đã đến với MBAL Chatbot")
 st.markdown("***")
 st.info('''
-        To use Our Mistral supported Chatbot, click Chat.
-         
-        To push data, click on Store Document.
-        ''')
+        Tôi sẽ giải đáp các thắc mắc của bạn liên quan đến các sản phẩm bảo hiểm nhân thọ của MB Ageas Life''')
 
-col1, col2 = st.columns(2)
+# col1, col2 = st.columns(2)
 
-with col1:
-    chat = st.button("Chat")
-    if chat:
-        st.switch_page("pages/chatbot.py")
+# with col1:
+#     chat = st.button("Chat")
+#     if chat:
+st.switch_page("pages/chatbot.py")
 
 
 st.markdown("<div style='text-align:center;'></div>", unsafe_allow_html=True)