Update app.py

app.py

@@ -1,8 +1,6 @@
-import streamlit as st
-from streamlit_chat import message
-from streamlit.components.v1 import html
 from dataclasses import dataclass
 from typing import Literal
+import streamlit as st
 import os
 from llamaapi import LlamaAPI
 from langchain_experimental.llms import ChatLlamaAPI
@@ -11,6 +9,7 @@ import pinecone
 from langchain.vectorstores import Pinecone
 from langchain.prompts import PromptTemplate
 from langchain.chains import RetrievalQA
+import streamlit.components.v1 as components
 from langchain_groq import ChatGroq
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ChatMessageHistory, ConversationBufferMemory
@@ -64,6 +63,7 @@ def initialize_session_state():
 
         PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
         
+        #chain_type_kwargs = {"prompt": PROMPT}
         message_history = ChatMessageHistory()
         memory = ConversationBufferMemory(
             memory_key="chat_history",
@@ -85,11 +85,17 @@ def initialize_session_state():
 
 def on_click_callback():
     human_prompt = st.session_state.human_prompt
-    st.session_state.human_prompt = ""
-    response = st.session_state.conversation(human_prompt)
+    st.session_state.human_prompt=""
+    response = st.session_state.conversation(
+        human_prompt
+    )
     llm_response = response['answer']
-    st.session_state.history.append(Message("👤 Human", human_prompt))
-    st.session_state.history.append(Message("👨🏻‍⚖️ Ai", llm_response))
+    st.session_state.history.append(
+        Message("👤 Human", human_prompt)
+    )
+    st.session_state.history.append(
+        Message("👨🏻‍⚖️ Ai", llm_response)
+    )
 
 
 initialize_session_state()
@@ -118,38 +124,23 @@ st.markdown(
     """
 )
 
-if "history" not in st.session_state:
-    st.session_state.history = []
-
-if "generated" not in st.session_state:
-    st.session_state.generated = []
-
-if "past" not in st.session_state:
-    st.session_state.past = []
-
-chat_placeholder = st.empty()
-
-with chat_placeholder.container():
-    for i, chat in enumerate(st.session_state.history):
-        if chat.origin == "👤 Human":
-            message(chat.message, is_user=True, key=f"{i}_user")
-        else:
-            message(chat.message, key=f"{i}")
-
-def on_input_change():
-    user_input = st.session_state.user_input
-    st.session_state.past.append(user_input)
-    response = st.session_state.conversation(user_input)
-    llm_response = response['answer']
-    st.session_state.generated.append({"type": "normal", "data": f"The message from Bot\nWith new line\n{user_input}"})
-    st.session_state.history.append(Message("👤 Human", user_input))
-    st.session_state.history.append(Message("👨🏻‍⚖️ Ai", llm_response))
-
-def on_btn_click():
-    del st.session_state.past[:]
-    del st.session_state.generated[:]
-    del st.session_state.history[:]
-
-with st.container():
-    st.text_input("User Input:", on_change=on_input_change, key="user_input")
-    st.button("Clear message", on_click=on_btn_click)
+chat_placeholder = st.container()
+prompt_placeholder = st.form("chat-form")
+
+with chat_placeholder:
+    for chat in st.session_state.history:
+        st.markdown(f"{chat.origin} : {chat.message}")
+
+with prompt_placeholder:
+    st.markdown("**Chat**")
+    cols = st.columns((6, 1))
+    cols[0].text_input(
+        "Chat",
+        label_visibility="collapsed",
+        key="human_prompt",
+    )
+    cols[1].form_submit_button(
+        "Submit",
+        type="primary",
+        on_click=on_click_callback,
+    )