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| import streamlit as st | |
| from langchain_community.llms import HuggingFaceTextGenInference | |
| import os | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| from langchain.schema import StrOutputParser | |
| # from datetime import datetime | |
| from datetime import datetime, timezone, timedelta | |
| from custom_llm import CustomLLM, custom_chain_with_history | |
| from typing import Optional | |
| from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder | |
| from langchain_core.chat_history import BaseChatMessageHistory | |
| from langchain.memory import ConversationBufferMemory#, PostgresChatMessageHistory | |
| import psycopg2 | |
| import urllib.parse as up | |
| os.environ['LANGCHAIN_TRACING_V2'] = "true" | |
| API_TOKEN = os.getenv('HF_INFER_API') | |
| POSTGRE_URL = os.environ['POSTGRE_URL'] | |
| def get_llm_chain(): | |
| return custom_chain_with_history( | |
| llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"], temperature=0.001), | |
| # llm=CustomLLM(repo_id="google/gemma-7b", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"], temperature=0.001), | |
| # memory=st.session_state.memory.chat_memory, | |
| memory=st.session_state.memory | |
| ) | |
| def get_db_connection(conn_url, password=None): | |
| url = up.urlparse(conn_url) | |
| conn = psycopg2.connect( | |
| database=url.path[1:], | |
| user=url.username, | |
| password=password if password is not None else url.password, | |
| host=url.hostname, | |
| port=url.port | |
| ) | |
| print("Connection to database succesfull!") | |
| return conn | |
| # @st.cache_resource | |
| # def get_memory(): | |
| # return PostgresChatMessageHistory(connection_string=POSTGRE_URL, session_id=str(datetime.timestamp(datetime.now()))) | |
| if 'conn' not in st.session_state: | |
| st.session_state.conn = get_db_connection(POSTGRE_URL) | |
| # if 'cursor' not in st.session_state: | |
| # st.session_state.cursor = st.session_state.conn.cursor() | |
| if 'memory' not in st.session_state: | |
| st.session_state['memory'] = ConversationBufferMemory(return_messages=True) | |
| # st.session_state.memory = PostgresChatMessageHistory(connection_string=POSTGRE_URL, session_id=str(datetime.timestamp(datetime.now()))) | |
| # st.session_state.memory = get_memory() | |
| st.session_state.memory.chat_memory.add_ai_message("Hello, My name is Jonathan Jordan. You can call me Jojo. How can I help you today?") | |
| # st.session_state.memory.add_ai_message("Hello, My name is Jonathan Jordan. You can call me Jojo. How can I help you today?") | |
| if 'chain' not in st.session_state: | |
| # st.session_state['chain'] = custom_chain_with_history( | |
| # llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"], temperature=0.001), | |
| # memory=st.session_state.memory.chat_memory, | |
| # # memory=st.session_state.memory | |
| # ) | |
| st.session_state['chain'] = get_llm_chain() | |
| st.title("Chat With Me") | |
| st.subheader("by Jonathan Jordan") | |
| st.markdown("""<p style="color: yellow;">Note : This conversation will be recorded in our private Database, thank you :)</p>""", unsafe_allow_html=True) | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [{"role":"assistant", "content":"Hello, My name is Jonathan Jordan. You can call me Jojo. How can I help you today?"}] | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # React to user input | |
| if prompt := st.chat_input("Ask me anything.."): | |
| # Display user message in chat message container | |
| st.chat_message("User").markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "User", "content": prompt}) | |
| response = st.session_state.chain.invoke({"question":prompt, "memory":st.session_state.memory}).split("\n<|")[0] | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response) | |
| # st.session_state.memory.add_user_message(prompt) | |
| # st.session_state.memory.add_ai_message(response) | |
| st.session_state.memory.save_context({"question":prompt}, {"output":response}) | |
| st.session_state.memory.chat_memory.messages = st.session_state.memory.chat_memory.messages[-15:] | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |
| # Insert data into the table | |
| try : | |
| try : | |
| cur = st.session_state.conn.cursor() | |
| except: | |
| get_db_connection.clear() | |
| st.session_state.conn = get_db_connection(POSTGRE_URL) | |
| cur = st.session_state.conn.cursor() | |
| cur.execute( | |
| f"INSERT INTO chat_history (input_text, response_text, created_at) VALUES (%s, %s, %s)", | |
| (prompt, response, datetime.now(timezone.utc) + timedelta(hours=7)) | |
| ) | |
| # Commit the transaction | |
| st.session_state.conn.commit() | |
| cur.close() | |
| except Exception as e: | |
| print("ERROR!!!\n", str(e)) | |
| print("User Input :", prompt) | |
| print("Chatbot Response :", response) | |