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| # importing dependencies | |
| from dotenv import load_dotenv | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import faiss | |
| from langchain.prompts import PromptTemplate | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.chat_models import ChatOpenAI | |
| from htmlTemplates import css, bot_template, user_template | |
| from langchain.embeddings import openai | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| import os | |
| openai.api_key = os.getenv["OPENAI_API_KEY"] | |
| # creating custom template to guide llm model | |
| custom_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. | |
| Chat History: | |
| {chat_history} | |
| Follow Up Input: {question} | |
| Standalone question:""" | |
| CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template) | |
| # extracting text from pdf | |
| def get_pdf_text(docs): | |
| text="" | |
| for pdf in docs: | |
| pdf_reader=PdfReader(pdf) | |
| for page in pdf_reader.pages: | |
| text+=page.extract_text() | |
| return text | |
| # converting text to chunks | |
| def get_chunks(raw_text): | |
| text_splitter=CharacterTextSplitter(separator="\n", | |
| chunk_size=1000, | |
| chunk_overlap=200, | |
| length_function=len) | |
| chunks=text_splitter.split_text(raw_text) | |
| return chunks | |
| # using all-MiniLm embeddings model and faiss to get vectorstore | |
| def get_vectorstore(chunks): | |
| embeddings=OpenAIEmbeddings() | |
| vectorstore=faiss.FAISS.from_texts(texts=chunks,embedding=embeddings) | |
| return vectorstore | |
| # generating conversation chain | |
| def get_conversationchain(vectorstore): | |
| llm=ChatOpenAI(temperature=0.2,model_name='gpt-4-turbo') | |
| memory = ConversationBufferMemory(memory_key='chat_history', | |
| return_messages=True, | |
| output_key='answer') # using conversation buffer memory to hold past information | |
| conversation_chain = ConversationalRetrievalChain.from_llm( | |
| llm=llm, | |
| retriever=vectorstore.as_retriever(), | |
| condense_question_prompt=CUSTOM_QUESTION_PROMPT, | |
| memory=memory) | |
| return conversation_chain | |
| # generating response from user queries and displaying them accordingly | |
| def handle_question(question): | |
| response=st.session_state.conversation({'question': question}) | |
| st.session_state.chat_history=response["chat_history"] | |
| for i,msg in enumerate(st.session_state.chat_history): | |
| if i%2==0: | |
| st.write(user_template.replace("{{MSG}}",msg.content,),unsafe_allow_html=True) | |
| else: | |
| st.write(bot_template.replace("{{MSG}}",msg.content),unsafe_allow_html=True) | |
| def main(): | |
| load_dotenv() | |
| st.set_page_config(page_title="Chat with multiple PDFs",page_icon=":books:") | |
| st.write(css,unsafe_allow_html=True) | |
| if "conversation" not in st.session_state: | |
| st.session_state.conversation=None | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history=None | |
| st.header("Chat with multiple PDFs :books:") | |
| question=st.text_input("Ask question from your document:") | |
| if question: | |
| handle_question(question) | |
| with st.sidebar: | |
| st.subheader("Your documents") | |
| docs=st.file_uploader("Upload your PDF here and click on 'Process'",accept_multiple_files=True) | |
| if st.button("Process"): | |
| with st.spinner("Processing"): | |
| #get the pdf | |
| raw_text=get_pdf_text(docs) | |
| #get the text chunks | |
| text_chunks=get_chunks(raw_text) | |
| #create vectorstore | |
| vectorstore=get_vectorstore(text_chunks) | |
| #create conversation chain | |
| st.session_state.conversation=get_conversationchain(vectorstore) | |
| if __name__ == '__main__': | |
| main() |