Spaces:
Sleeping
Sleeping
File size: 3,162 Bytes
5f2b5c3 |
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 |
# *** Installing Necessary Libraries ***
# !pip install streamlit
# !pip install groq
# !pip install keras
# !pip install langchain
# !pip install langchain_groq
# !pip install dotenv
# *** Importing Necessary Packages ***
import streamlit as st
import os
from groq import Groq
import random
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain_groq import ChatGroq
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
load_dotenv()
api_key = os.environ['GROQ_API_KEY'] # Retriving API Key from environment file
def main():
st.title("Chai pe Charcha with Arghya") # Define a title for the chatbot Front End
# Add customization options to the sidebar
st.sidebar.title('Select an LLM') # Define a title for the chatbot Side Bar
model = st.sidebar.selectbox(
'Choose a model',
['mixtral-8x7b-32768', 'llama2-70b-4096', 'Gemma-7b-lt'] # Define a choices for LLM Model
)
conversational_memory_length = st.sidebar.slider('Conversational memory length:',
1, 10, value = 5) # Define a slider to choose the lengh of converstaion in Side Bar
memory=ConversationBufferWindowMemory(k=conversational_memory_length) # Store the user chosen length as memory for future use
user_question = st.text_area("What's in your mind..") # Define a prompt for question area
# session state variable
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']}) # Storing the context of the conversation
# Initialize Groq Langchain chat object and conversation
groq_chat = ChatGroq(
groq_api_key = api_key,
model_name=model # Initializing the Groq ChatBot
)
conversation = ConversationChain(
llm=groq_chat,
memory=memory # Initializing the conversation chain
)
if st.button("Submit & Process"):
if user_question:
with st.spinner("Processing..."):
response = conversation(user_question) # Generating response for User's Question
message = {'human':user_question,'AI':response['response']}
st.session_state.chat_history.append(message) # Appending the QnA to chat history
st.write("Chatbot:", response['response']) # Writing back the response in Front End
if __name__ == "__main__":
main() |