side bar and memory clear

app.py

@@ -172,17 +172,17 @@ qa = ConversationalRetrievalChain.from_llm(
 
 
 #---------------------------------------------------------
-
 import streamlit as st
 import time
 
+
 # App title
 st.set_page_config(page_title="🤖💼 🇲🇦 Financial advisor is Here")
 
 # Replicate Credentials
 with st.sidebar:
-    st.title(' Mokawil.AI is Here 🤖💼 🇲🇦')
-    st.markdown('📖 an AI-powered advisor designed to assist founders (or anyone aspiring to start their own company) with various aspects of business in Morocco, including legal considerations, budget planning, available investors, and strategies for success.')
+    st.title('Mokawil.AI is Here 🤖💼 🇲🇦')
+    st.markdown('🤖 an AI-powered advisor designed to assist founders (or anyone aspiring to start their own company) with various aspects of business in Morocco, including legal considerations, budget planning, available investors, and strategies for success.')
 
 # Store LLM generated responses
 if "messages" not in st.session_state.keys():
@@ -191,33 +191,47 @@ if "messages" not in st.session_state.keys():
 # Display or clear chat messages
 for message in st.session_state.messages:
     if message["role"] == "user" :
-      with st.chat_message(message["role"], avatar="👨‍💻"):
+      with st.chat_message(message["role"], avatar="user.png"):
           st.write(message["content"])
     else : 
-      with st.chat_message(message["role"], avatar="🤖"):
+      with st.chat_message(message["role"], avatar="logo.png"):
           st.write(message["content"])
 
 def clear_chat_history():
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True,
+        input_key='question',
+        output_key='answer'
+    )
+    qa = ConversationalRetrievalChain.from_llm(
+        llm,
+        chain_type="stuff",
+        retriever=VectorStore.as_retriever(search_kwargs={"k": 5}),
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
     st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
 
 st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
 
 # Function for generating LLaMA2 response
-def generate_llama2_response(prompt_input):
+def generate_llm_response(prompt_input):
     res = qa(f'''{prompt_input}''')
     return res['answer']
 
 # User-provided prompt
 if prompt := st.chat_input("What is up?"):
     st.session_state.messages.append({"role": "user", "content": prompt})
-    with st.chat_message("user", avatar="👨‍💻"):
+    with st.chat_message("user", avatar="user.png"):
         st.write(prompt)
 
 # Generate a new response if last message is not from assistant
 if st.session_state.messages[-1]["role"] != "assistant":
-  with st.chat_message("assistant", avatar="🤖"):
+  with st.chat_message("assistant", avatar="logo.png"):
       with st.spinner("Thinking..."):
-          response = generate_llama2_response(st.session_state.messages[-1]["content"])
+          response = generate_llm_response(st.session_state.messages[-1]["content"])
       placeholder = st.empty()
       full_response = ''
       for item in response:
@@ -230,19 +244,20 @@ if st.session_state.messages[-1]["role"] != "assistant":
 
 # Example prompt
 with st.sidebar : 
-    st.title('Input examples')
+  st.title('Examples :')
+
 def promptExample1():
-    prompt = "how can I start my company example 1"
+    prompt = "how can I start my company in morocco?"
     st.session_state.messages.append({"role": "user", "content": prompt})
 
 # Example prompt
 def promptExample2():
-    prompt = "how can I start my company example 2"
+    prompt = "What are some recommended cities for starting a business in finance"
     st.session_state.messages.append({"role": "user", "content": prompt})
 
 # Example prompt
 def promptExample3():
-    prompt = "how can I start my company example 3"
+    prompt = "what is the estimate money I need for starting my company"
     st.session_state.messages.append({"role": "user", "content": prompt})