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# 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()
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