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import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_community.document_loaders import WebBaseLoader
# from langchain_community.embeddings import OllamaEmbeddings
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores.faiss import FAISS
import time
from PyPDF2 import PdfReader
import tempfile
st.title("Ask your questions from pdf(s) or website")
option = None
# Prompt user to choose between PDFs or website
option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None)
def get_pdf_processed(pdf_docs):
text=""
for pdf in pdf_docs:
pdf_reader= PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def llm_model():
llm = ChatGroq(model="mixtral-8x7b-32768")
prompt = ChatPromptTemplate.from_template(
"""
Answer the question based on the provided context only.
Please provide the most accurate response based on the question
<context>
{context}
</context>
Questions:{input}
"""
)
document_chain = create_stuff_documents_chain(llm,prompt)
retriever = st.session_state.vector.as_retriever() if st.session_state.vector else None
retrieval_chain = create_retrieval_chain(retriever,document_chain)
prompt = st.text_input("Input your question here")
if prompt:
start =time.process_time()
response = retrieval_chain.invoke({"input":prompt})
print("Response time :", time.process_time()-start)
st.write(response['answer'])
st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model = 'models/embedding-001')
st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200)
if option:
if option == "Website":
website_link = st.text_input("Enter the website link:")
if website_link:
st.session_state.loader = WebBaseLoader(website_link)
st.session_state.docs = st.session_state.loader.load()
st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)
llm_model()
elif option == "PDF(s)":
pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)
if pdf_files:
st.session_state.docs = get_pdf_processed(pdf_files)
st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings)
llm_model()
# with st.expander("Document Similarity Search"):
# for i, doc in enumerate(response['context']):
# st.write(doc.page_content)
# st.write("-----------------------------")
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