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| import streamlit as st | |
| import os | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain_community.embeddings import OpenAIEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.chat_models import ChatOpenAI | |
| def read_pdf(pdf): | |
| text = "" | |
| try: | |
| pdf_reader = PdfReader(pdf, strict = False) | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| except Exception as e: | |
| st.write("Error Reading PDF : {e}") | |
| return text | |
| def get_chunk_data(text): | |
| text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=250, length_function=len) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vector_store(text): | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| embeddings = OpenAIEmbeddings(openai_api_key=api_key) | |
| vectorstore = FAISS.from_texts(texts=text, embedding=embeddings) | |
| return vectorstore | |
| def get_conversation(vectorstore): | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| llm = ChatOpenAI(openai_api_key=api_key) | |
| memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory) | |
| return conversation_chain | |
| def handleInput(user_text, conversation_chain): | |
| res = conversation_chain({'question': user_text}) | |
| chat_history = res['chat_history'] | |
| ans = res['answer'] | |
| st.write(ans) | |
| def main(): | |
| load_dotenv() | |
| st.set_page_config(page_title="Chat with PDF") | |
| 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 PDF") | |
| user_text = st.text_input("Ask question:") | |
| if user_text and st.session_state.conversation: | |
| handleInput(user_text, st.session_state.conversation) | |
| with st.sidebar: | |
| st.subheader("Your Documents") | |
| pdf = st.file_uploader("Upload PDF") | |
| if pdf and st.button("Submit"): | |
| with st.spinner("Processing..."): | |
| # Read data from pdf | |
| raw_text = read_pdf(pdf) | |
| # Split data into chunks | |
| load_chunks = get_chunk_data(raw_text) | |
| # Create a vector store | |
| vector_store = get_vector_store(load_chunks) | |
| # Create conversation chain | |
| conversation_chain = get_conversation(vector_store) | |
| st.session_state.conversation = conversation_chain # Save the conversation chain to session state | |
| if __name__ == '__main__': | |
| main() | |