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import streamlit as st
from QnA import Q_A
import re,time
from QnA import get_hugging_face_model , summarize ,get_groq_model
def summarize_data(documents,api_key):
if api_key.startswith('gsk'):
llm = get_groq_model(api_key)
else:
llm= get_hugging_face_model(api_key=api_key)
summary = summarize(documents,llm)
return summary
def QA_Bot(vectorstore,API_KEY,documents):
summary_response = None
st.title("Q&A Bot")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
summary_response = summarize_data(documents,API_KEY)
print(summary_response)
# React to user input
if prompt := st.chat_input("What is up?"):
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
ai_response = Q_A(vectorstore,prompt,API_KEY)
response = f"Echo: {ai_response}"
# Display assistant response in chat message container
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
for chunk in re.split(r'(\s+)', response):
full_response += chunk + " "
time.sleep(0.01)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": full_response})
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