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import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage
from functions.gptResponse import get_response
from functions.sidebar import sidebar
from functions.web_chain import vectorize, loadUrlData, get_pdf_text
import asyncio
async def main():
sidebar()
st.title("Upload Data")
uploaded_files = st.file_uploader("Upload PDFs", accept_multiple_files=True)
st.warning("If you plan to add more files, after processing initial files, make sure the uploaded files you already processed are removed")
url = st.text_input("Enter a website link")
if "button_pressed" not in st.session_state:
st.session_state.button_pressed = False
if st.button('Process URL and Files'):
st.session_state.button_pressed = True
with st.spinner("Vectorizing Data, wait times vary depending on size..."):
if url:
try:
if "retriever" not in st.session_state:
st.session_state.retriever = vectorize(loadUrlData(url), "document")
except Exception as e:
st.error(f"Failed to load URL: {e}")
if uploaded_files:
try:
texts = get_pdf_text(uploaded_files)
if texts:
if "retriever" not in st.session_state:
st.session_state.retriever = vectorize(texts, "text")
else:
st.session_state.retriever.add_texts(texts)
else:
st.error("PDF has no meta data text")
except Exception as e:
st.error(f"Failed to load PDF: {e}")
st.success("Data is ready to be queried!")
if st.session_state.button_pressed:
if "chat_history" not in st.session_state:
st.session_state.chat_history = [AIMessage(content="Hello, I am a bot. How can I help you?")]
st.title("RAG CHAT")
for message in st.session_state.chat_history:
if isinstance(message, AIMessage):
with st.chat_message("AI"):
st.write(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message("Human"):
st.write(message.content)
user_query = st.chat_input("Type your message here...", key="chat_input")
if user_query:
st.session_state.chat_history.append(HumanMessage(content=user_query))
with st.chat_message("Human"):
st.write(user_query)
if 'retriever' in st.session_state:
try:
ragAnswer = await st.session_state.retriever.amax_marginal_relevance_search(user_query, k=4, fetch_k=10)
context = []
for i, doc in enumerate(ragAnswer):
print(f"{i}: {doc.page_content}")
context.append(doc.page_content)
with st.spinner("Generating Response"):
response = get_response(user_query, st.session_state.chat_history, context)
if response:
st.session_state.chat_history.append(AIMessage(content=response))
with st.chat_message("AI"):
st.write(response)
else:
st.write("No response received.")
except Exception as e:
st.error(f"Error during retrieval or response generation: {e}")
if __name__ == "__main__":
asyncio.run(main())
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