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import streamlit as st
import utils
# https://github.com/serkanyasr/RAG-with-LangChain-URL-PDF/blob/main/utils.py
st.set_page_config(layout="centered")
st.markdown("<h1 style='font-size:24px;'>RAG with LangChain & GenAI: Any url</h1>", unsafe_allow_html=True)
# st.title("RAG with LangChain & GenAI: Any url")
# URL text box for user input
url_input = st.text_input("Enter a URL to be queried:", "")
# Input text box for user input
user_input = st.text_input("Enter your Question below:", "")
# Display the user input
# st.write("You entered:", user_input)
# st.write("URL entered:", url_input)
sumbit_btn = st.button(label="Submit",key="url_btn")
if sumbit_btn:
with st.spinner("Processing..."):
st.success("Response: Answering with RAG...")
response = utils.rag_with_url(url_input,user_input)
st.markdown(response)
# st.title("Retrieval-Augmented Generation (RAG) with LangChain : PDF ")
# st.divider()
# col_input , col_rag , col_normal = st.columns([3,5,5])
# with col_input:
# selected_file = st.file_uploader("PDF File", type=["pdf"])
# st.divider()
# prompt = st.text_input("Prompt",key="pdf_prompt")
# st.divider()
# sumbit_btn = st.button(label="Submit",key="pdf_btn")
# if sumbit_btn:
# with col_rag:
# with st.spinner("Processing..."):
# st.success("Response: Answering with RAG...")
# response,relevant_documents = utils.rag_with_pdf(file_path=f"./data/{selected_file.name}",
# prompt=prompt)
# st.markdown(response)
# st.divider()
# st.info("Documents")
# for doc in relevant_documents:
# st.caption(doc.page_content)
# st.markdown(f"Source: {doc.metadata}")
# st.divider()
# with col_normal:
# with st.spinner("Processing..."):
# st.info("Response: Answering without RAG...")
# response = utils.ask_gemini(prompt)
# st.markdown(response)
# st.divider()
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