|
import streamlit as st
|
|
import os
|
|
import random
|
|
from groq import Groq
|
|
from langchain.chains import ConversationChain
|
|
from langchain.chains.conversation.memory import ConversationBufferMemory
|
|
from langchain_groq import ChatGroq
|
|
from langchain.prompts import PromptTemplate
|
|
|
|
groq_api_key = "GROQ_API_KEY"
|
|
|
|
def main():
|
|
|
|
st.title("Groq Chatbot")
|
|
st.sidebar.title("Select an LLM")
|
|
model = st.sidebar.selectbox(
|
|
'Choose a model',
|
|
['Mixtral-8x7b-32768', 'llama2-70b-4096']
|
|
)
|
|
|
|
conversational_memory_length = st.sidebar.slider('Conversational Memory Length:', 1, 10, value=5)
|
|
|
|
memory = ConversationBufferMemory(k=conversational_memory_length)
|
|
|
|
user_question = st.text_area("Ask a question...")
|
|
|
|
|
|
if 'chat_history' not in st.session_state:
|
|
st.session_state.chat_history = []
|
|
else:
|
|
for message in st.session_state.chat_history:
|
|
memory.save_context({'input': message['human']}, {'output': message['AI']})
|
|
|
|
groq_chat = ChatGroq(
|
|
groq_api_key= groq_api_key,
|
|
model_name = model
|
|
)
|
|
|
|
conversation = ConversationChain(
|
|
llm = groq_chat,
|
|
memory = memory
|
|
)
|
|
|
|
if user_question:
|
|
response = conversation(user_question)
|
|
message = {"human": user_question, "AI": response['response']}
|
|
st.session_state.chat_history.append(message)
|
|
st.write("ChatBot:", response['response'])
|
|
|
|
if __name__ == "__main__":
|
|
main() |
