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| import streamlit as st | |
| import os | |
| from PyPDF2 import PdfReader | |
| import openpyxl | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.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.write("User: ", message.content) | |
| else: | |
| st.write("Bot: ", message.content) | |
| st.success("Done !") | |
| 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") | |
| st.header("DocChat - Chat with multiple documents") | |
| 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") | |
| st.write('Ask a question : ') | |
| 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() | |