import streamlit as st import os from PyPDF2 import PdfReader import openpyxl from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import GooglePalmEmbeddings from langchain.llms import GooglePalm from langchain.vectorstores import FAISS from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationBufferMemory os.environ['GOOGLE_API_KEY'] = 'AIzaSyD8uzXToT4I2ABs7qo_XiuKh8-L2nuWCEM' def get_pdf_text(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 get_excel_text(excel_docs): text = "" for excel_doc in excel_docs: workbook = openpyxl.load_workbook(filename=excel_doc) for sheet in workbook: for row in sheet: for cell in row: text += str(cell.value) + " " return text.strip() def get_text_chunks(text): text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20) chunks = text_splitter.split_text(text) return chunks def get_vector_store(text_chunks): embeddings = GooglePalmEmbeddings() vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) return vector_store def get_conversational_chain(vector_store): llm = GooglePalm() memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(), memory=memory) return conversation_chain def get_user_input(user_question): with st.container(): response = st.session_state.conversation({'question': user_question}) st.session_state.chatHistory = response['chat_history'] file_contents = "" left , right = st.columns((2,1)) with left: for i, message in enumerate(st.session_state.chatHistory): if i % 2 == 0: st.markdown(f'
User: {message.content}
', unsafe_allow_html=True) else: st.markdown(f'
Bot: {message.content}
', unsafe_allow_html=True) with right: for message in st.session_state.chatHistory: file_contents += f"{message.content}\n" file_name = "Chat_History.txt" def main(): st.set_page_config("DocChat") # Define Streamlit app layout st.markdown("

🧾 DocChat - Chat with multiple documents

", unsafe_allow_html=True) st.caption("🚀 Chat bot developed By :- [Dinesh Abeysinghe](https://www.linkedin.com/in/dinesh-abeysinghe-bb773293) | [GitHub Source Code](https://github.com/dineshabey/AI-TypeTalkChat.git) | [About model](https://arxiv.org/abs/2004.13637) ") st.markdown("
First need to upload PDF file or Excel file. Then you can start chat with document related things Please click like button❤️ and support me and enjoy it.
", unsafe_allow_html=True) st.write("---") with st.container(): with st.sidebar: st.title("Settings") st.subheader("Upload Documents") st.markdown("**PDF files:**") pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True) if st.button("Process PDF file"): with st.spinner("Processing PDFs..."): raw_text = get_pdf_text(pdf_docs) text_chunks = get_text_chunks(raw_text) vector_store = get_vector_store(text_chunks) st.session_state.conversation = get_conversational_chain(vector_store) st.success("PDF processed successfully!") st.markdown("**Excel files:**") excel_docs = st.file_uploader("Upload Excel Files", accept_multiple_files=True) if st.button("Process Excel file"): with st.spinner("Processing Excel files..."): raw_text = get_excel_text(excel_docs) text_chunks = get_text_chunks(raw_text) vector_store = get_vector_store(text_chunks) st.session_state.conversation = get_conversational_chain(vector_store) st.success("Excel file processed successfully!") with st.container(): st.subheader("Document Q&A") user_question = st.text_input("Ask a Question from the document") if "conversation" not in st.session_state: st.session_state.conversation = None if "chatHistory" not in st.session_state: st.session_state.chatHistory = None if user_question: get_user_input(user_question) if __name__ == "__main__": main()