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from langchain_community.vectorstores.faiss import FAISS |
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from langchain_community.document_loaders import WebBaseLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.chains.combine_documents import create_stuff_documents_chain |
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from langchain_core.prompts import ChatPromptTemplate |
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from langchain.chains import create_retrieval_chain |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain_groq import ChatGroq |
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import streamlit as st |
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import os |
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import time |
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from PyPDF2 import PdfReader |
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import tempfile |
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def get_pdf_processed(pdf_docs): |
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text="" |
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for pdf in pdf_docs: |
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pdf_reader= PdfReader(pdf) |
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for page in pdf_reader.pages: |
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text += page.extract_text() |
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return text |
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st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") |
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st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200) |
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def initialize_vector_store(option): |
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if option: |
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if option == "Website": |
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website_link = st.text_input("Enter the website link:") |
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if st.button("Submit & Process"): |
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with st.spinner("Loading website content..."): |
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st.session_state.loader = WebBaseLoader(website_link) |
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st.session_state.docs = st.session_state.loader.load() |
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st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs) |
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st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings) |
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st.success("Website content loaded successfully!") |
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elif option == "PDF(s)": |
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pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True) |
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if st.button("Submit & Process"): |
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with st.spinner("Loading pdf..."): |
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st.session_state.docs = get_pdf_processed(pdf_files) |
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st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs) |
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st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings) |
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st.success("PDF content loaded successfully!") |
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def get_conversational_chain(): |
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llm = ChatGroq(model="mixtral-8x7b-32768") |
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prompt = ChatPromptTemplate.from_template( |
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""" |
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Answer the question based on the provided context only. |
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Please provide the most accurate response based on the question |
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<context> |
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{context} |
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</context> |
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Questions:{input} |
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""" |
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) |
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document_chain = create_stuff_documents_chain(llm,prompt) |
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retriever = st.session_state.vector.as_retriever() if st.session_state.vector else None |
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retrieval_chain = create_retrieval_chain(retriever,document_chain) |
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return retrieval_chain |
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def user_input(prompt): |
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chain = get_conversational_chain() |
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start =time.process_time() |
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response = chain.invoke({"input":prompt}) |
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st.write(response['answer']) |
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st.write("Response time: ", time.process_time() - start) |
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with st.expander("Did not like the response? Check out more here"): |
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for i, doc in enumerate(response['context']): |
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st.write(doc.page_content) |
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st.write("-----------------------------") |
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def main(): |
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st.title("Ask your questions from pdf(s) or website") |
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option = None |
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option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None) |
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initialize_vector_store(option) |
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prompt = st.text_input("Input your question here") |
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if prompt: |
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user_input(prompt) |
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if __name__ == "__main__": |
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main() |
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