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ChatBotV1

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Update app.py

ec0738f verified over 2 years ago

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import streamlit as st
import os
import random, string

from langchain.chains import LLMChain
from langchain_core.prompts import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
)
from langchain_core.messages import SystemMessage
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain_groq import ChatGroq

if 'chat_list' not in st.session_state:
    st.session_state.chat_list = []


def arr():
    for c_list in st.session_state.chat_list:
        with st.chat_message("user"):
            st.write("Question : " + c_list["ques"])
        with st.chat_message("machine"):
            st.write("Answer : " + c_list["ans"])

def main():

    """
    This function is the main entry point of the application. It sets up the Groq client, the Streamlit interface, and handles the chat interaction.
    """

    # Get Groq API key
    groq_api_key = st.secrets["Groq_API_key"]
    model = 'llama3-8b-8192'
    # Initialize Groq Langchain chat object and conversation
    groq_chat = ChatGroq(
        groq_api_key=groq_api_key, 
        model_name=model
    )

    st.title('Langchain Chatbot With llama3-8b-8192 model')
    
    # print("Hello! I'm your friendly Groq chatbot. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
    st.markdown("Hello! I'm your friendly Groq chatbot, dev by GJ. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")

    system_prompt = 'You are a friendly conversational chatbot'
    conversational_memory_length = 5 # number of previous messages the chatbot will remember during the conversation

    if 'memory' not in st.session_state:
        st.session_state.memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
    # st.write(st.session_state.memory)


    # user_question = st.text_input("Ask a question: ")
    user_question = st.chat_input("Ask a question:")
    if user_question:
        # Construct a chat prompt template using various components
        prompt = ChatPromptTemplate.from_messages(
            [
                SystemMessage(
                    content=system_prompt
                ),  # This is the persistent system prompt that is always included at the start of the chat.

                MessagesPlaceholder(
                    variable_name="chat_history"
                ),  # This placeholder will be replaced by the actual chat history during the conversation. It helps in maintaining context.

                HumanMessagePromptTemplate.from_template(
                    "{human_input}"
                ),  # This template is where the user's current input will be injected into the prompt.
            ]
        )

        # Create a conversation chain using the LangChain LLM (Language Learning Model)
        conversation = LLMChain(
            llm=groq_chat,  # The Groq LangChain chat object initialized earlier.
            prompt=prompt,  # The constructed prompt template.
            verbose=False,   # TRUE Enables verbose output, which can be useful for debugging.
            memory=st.session_state.memory,  # The conversational memory object that stores and manages the conversation history.
        )
        # The chatbot's answer is generated by sending the full prompt to the Groq API.
        response = conversation.predict(human_input=user_question)
        # st.text("Question: " + user_question)
        # st.text("Chatbot: " + response)
        result = {"ques":user_question, "ans":response}
        st.session_state.chat_list.append(result)
        arr()
        # st.write(st.session_state.memory)

if __name__ == "__main__":
    main()

	import streamlit as st
	import os
	import random, string

	from langchain.chains import LLMChain
	from langchain_core.prompts import (
	ChatPromptTemplate,
	HumanMessagePromptTemplate,
	MessagesPlaceholder,
	)
	from langchain_core.messages import SystemMessage
	from langchain.chains.conversation.memory import ConversationBufferWindowMemory
	from langchain_groq import ChatGroq

	if 'chat_list' not in st.session_state:
	st.session_state.chat_list = []


	def arr():
	for c_list in st.session_state.chat_list:
	with st.chat_message("user"):
	st.write("Question : " + c_list["ques"])
	with st.chat_message("machine"):
	st.write("Answer : " + c_list["ans"])

	def main():

	"""
	This function is the main entry point of the application. It sets up the Groq client, the Streamlit interface, and handles the chat interaction.
	"""

	# Get Groq API key
	groq_api_key = st.secrets["Groq_API_key"]
	model = 'llama3-8b-8192'
	# Initialize Groq Langchain chat object and conversation
	groq_chat = ChatGroq(
	groq_api_key=groq_api_key,
	model_name=model
	)

	st.title('Langchain Chatbot With llama3-8b-8192 model')

	# print("Hello! I'm your friendly Groq chatbot. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
	st.markdown("Hello! I'm your friendly Groq chatbot, dev by GJ. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")

	system_prompt = 'You are a friendly conversational chatbot'
	conversational_memory_length = 5 # number of previous messages the chatbot will remember during the conversation

	if 'memory' not in st.session_state:
	st.session_state.memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
	# st.write(st.session_state.memory)


	# user_question = st.text_input("Ask a question: ")
	user_question = st.chat_input("Ask a question:")
	if user_question:
	# Construct a chat prompt template using various components
	prompt = ChatPromptTemplate.from_messages(
	[
	SystemMessage(
	content=system_prompt
	), # This is the persistent system prompt that is always included at the start of the chat.

	MessagesPlaceholder(
	variable_name="chat_history"
	), # This placeholder will be replaced by the actual chat history during the conversation. It helps in maintaining context.

	HumanMessagePromptTemplate.from_template(
	"{human_input}"
	), # This template is where the user's current input will be injected into the prompt.
	]
	)

	# Create a conversation chain using the LangChain LLM (Language Learning Model)
	conversation = LLMChain(
	llm=groq_chat, # The Groq LangChain chat object initialized earlier.
	prompt=prompt, # The constructed prompt template.
	verbose=False, # TRUE Enables verbose output, which can be useful for debugging.
	memory=st.session_state.memory, # The conversational memory object that stores and manages the conversation history.
	)
	# The chatbot's answer is generated by sending the full prompt to the Groq API.
	response = conversation.predict(human_input=user_question)
	# st.text("Question: " + user_question)
	# st.text("Chatbot: " + response)
	result = {"ques":user_question, "ans":response}
	st.session_state.chat_list.append(result)
	arr()
	# st.write(st.session_state.memory)

	if __name__ == "__main__":
	main()