| # import os | |
| # import logging | |
| # from dotenv import load_dotenv | |
| # import streamlit as st | |
| # from PyPDF2 import PdfReader | |
| # from langchain.text_splitter import CharacterTextSplitter | |
| # # from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| # from langchain_cohere import CohereEmbeddings | |
| # from langchain.vectorstores import FAISS | |
| # from langchain.memory import ConversationBufferMemory | |
| # from langchain.chains import ConversationalRetrievalChain | |
| # # from langchain.llms import Ollama | |
| # from langchain_groq import ChatGroq | |
| # # Load environment variables | |
| # load_dotenv() | |
| # # Set up logging | |
| # logging.basicConfig( | |
| # level=logging.INFO, | |
| # format='%(asctime)s - %(levelname)s - %(message)s' | |
| # ) | |
| # # Function to extract text from PDF files | |
| # 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 | |
| # # Function to split the extracted text into chunks | |
| # def get_text_chunks(text): | |
| # text_splitter = CharacterTextSplitter( | |
| # separator="\n", | |
| # chunk_size=1000, | |
| # chunk_overlap=200, | |
| # length_function=len | |
| # ) | |
| # chunks = text_splitter.split_text(text) | |
| # return chunks | |
| # # Function to create a FAISS vectorstore | |
| # # def get_vectorstore(text_chunks): | |
| # # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") | |
| # # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| # # return vectorstore | |
| # def get_vectorstore(text_chunks): | |
| # cohere_api_key = os.getenv("COHERE_API_KEY") | |
| # embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key) | |
| # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| # return vectorstore | |
| # # Function to set up the conversational retrieval chain | |
| # def get_conversation_chain(vectorstore): | |
| # try: | |
| # # llm = Ollama(model="llama3.2:1b") | |
| # llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5) | |
| # memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| # conversation_chain = ConversationalRetrievalChain.from_llm( | |
| # llm=llm, | |
| # retriever=vectorstore.as_retriever(), | |
| # memory=memory | |
| # ) | |
| # logging.info("Conversation chain created successfully.") | |
| # return conversation_chain | |
| # except Exception as e: | |
| # logging.error(f"Error creating conversation chain: {e}") | |
| # st.error("An error occurred while setting up the conversation chain.") | |
| # # Handle user input | |
| # def handle_userinput(user_question): | |
| # if st.session_state.conversation is not None: | |
| # response = st.session_state.conversation({'question': user_question}) | |
| # st.session_state.chat_history = response['chat_history'] | |
| # for i, message in enumerate(st.session_state.chat_history): | |
| # if i % 2 == 0: | |
| # st.write(f"*User:* {message.content}") | |
| # else: | |
| # st.write(f"*Bot:* {message.content}") | |
| # else: | |
| # st.warning("Please process the documents first.") | |
| # # Main function to run the Streamlit app | |
| # def main(): | |
| # load_dotenv() | |
| # st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:") | |
| # 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:") | |
| # user_question = st.text_input("Ask a question about your documents:") | |
| # if user_question: | |
| # handle_userinput(user_question) | |
| # with st.sidebar: | |
| # st.subheader("Your documents") | |
| # pdf_docs = st.file_uploader( | |
| # "Upload your PDFs here and click on 'Process'", accept_multiple_files=True | |
| # ) | |
| # if st.button("Process"): | |
| # with st.spinner("Processing..."): | |
| # raw_text = get_pdf_text(pdf_docs) | |
| # text_chunks = get_text_chunks(raw_text) | |
| # vectorstore = get_vectorstore(text_chunks) | |
| # st.session_state.conversation = get_conversation_chain(vectorstore) | |
| # if __name__ == '__main__': | |
| # main() | |
| import os | |
| import logging | |
| from dotenv import load_dotenv | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from docx import Document # Import for handling Word files | |
| import io # Import for handling byte streams | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain_cohere import CohereEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain_groq import ChatGroq | |
| # Load environment variables | |
| load_dotenv() | |
| # Set up logging | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(levelname)s - %(message)s' | |
| ) | |
| # Function to extract text from PDF files | |
| 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 | |
| # Function to extract text from Word files | |
| def get_word_text(word_docs): | |
| text = "" | |
| for word in word_docs: | |
| doc = Document(io.BytesIO(word.read())) # Read the Word document from bytes | |
| for para in doc.paragraphs: | |
| text += para.text + "\n" # Append each paragraph followed by a newline | |
| return text | |
| # Function to extract text from TXT files | |
| def get_txt_text(txt_docs): | |
| text = "" | |
| for txt in txt_docs: | |
| text += txt.read().decode("utf-8") + "\n" # Read and decode the text file content | |
| return text | |
| # Function to split the extracted text into chunks | |
| def get_text_chunks(text): | |
| text_splitter = CharacterTextSplitter( | |
| separator="\n", | |
| chunk_size=1000, | |
| chunk_overlap=200, | |
| length_function=len | |
| ) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vectorstore(text_chunks): | |
| cohere_api_key = os.getenv("COHERE_API_KEY") | |
| embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key) | |
| vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| return vectorstore | |
| # Function to set up the conversational retrieval chain | |
| def get_conversation_chain(vectorstore): | |
| try: | |
| llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5) | |
| memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| conversation_chain = ConversationalRetrievalChain.from_llm( | |
| llm=llm, | |
| retriever=vectorstore.as_retriever(), | |
| memory=memory | |
| ) | |
| logging.info("Conversation chain created successfully.") | |
| return conversation_chain | |
| except Exception as e: | |
| logging.error(f"Error creating conversation chain: {e}") | |
| st.error("An error occurred while setting up the conversation chain.") | |
| # Handle user input | |
| def handle_userinput(user_question): | |
| if st.session_state.conversation is not None: | |
| response = st.session_state.conversation({'question': user_question}) | |
| st.session_state.chat_history = response['chat_history'] | |
| for i, message in enumerate(st.session_state.chat_history): | |
| if i % 2 == 0: | |
| st.write(f"*User:* {message.content}") | |
| else: | |
| st.write(f"*Bot:* {message.content}") | |
| else: | |
| st.warning("Please process the documents first.") | |
| # Main function to run the Streamlit app | |
| def main(): | |
| load_dotenv() | |
| st.set_page_config(page_title="Chat with multiple documents", page_icon=":books:") | |
| 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 documents :books:") | |
| user_question = st.text_input("Ask a question about your documents:") | |
| if user_question: | |
| handle_userinput(user_question) | |
| with st.sidebar: | |
| st.subheader("Your documents") | |
| pdf_docs = st.file_uploader( | |
| "Upload your PDFs here", accept_multiple_files=True, type=["pdf"] | |
| ) | |
| word_docs = st.file_uploader( | |
| "Upload your Word documents here", accept_multiple_files=True, type=["docx"] | |
| ) | |
| txt_docs = st.file_uploader( | |
| "Upload your TXT files here", accept_multiple_files=True, type=["txt"] | |
| ) | |
| if st.button("Process"): | |
| with st.spinner("Processing..."): | |
| raw_text = "" | |
| if pdf_docs: | |
| raw_text += get_pdf_text(pdf_docs) | |
| if word_docs: | |
| raw_text += get_word_text(word_docs) | |
| if txt_docs: | |
| raw_text += get_txt_text(txt_docs) | |
| if raw_text: # Only process if there is any raw text extracted. | |
| text_chunks = get_text_chunks(raw_text) | |
| vectorstore = get_vectorstore(text_chunks) | |
| st.session_state.conversation = get_conversation_chain(vectorstore) | |
| else: | |
| st.warning("No documents were uploaded or processed.") | |
| if __name__ == '__main__': | |
| main() | |