File size: 3,817 Bytes
fdad4bd ec0738f fdad4bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | 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() |