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import streamlit as st
from langchain.chains.llm 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
from dotenv import load_dotenv
import os
load_dotenv()
def main():
groq_api_key = os.getenv("GROQ_API_KEY")
st.title("Chat with Groq!")
st.write("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.sidebar.title('Customization')
system_prompt = st.sidebar.text_input("System prompt:")
model = st.sidebar.selectbox(
'Choose a model',
['llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it']
)
conversational_memory_length = st.sidebar.slider('Conversational memory length:', 1, 10, value = 5)
memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
user_question = st.chat_input("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
)
if user_question:
prompt = ChatPromptTemplate.from_messages(
[
SystemMessage(
content=system_prompt
),
MessagesPlaceholder(
variable_name="chat_history"
),
HumanMessagePromptTemplate.from_template(
"{human_input}"
),
]
)
conversation = LLMChain(
llm=groq_chat,
prompt=prompt,
verbose=True,
memory=memory,
)
response = conversation.predict(human_input=user_question)
message = {"human":user_question,"AI": response}
st.session_state.chat_history.append(message)
st.write("Chatbot:", response)
if __name__ == "__main__":
main() |