Spaces:
Sleeping
Sleeping
File size: 4,010 Bytes
59d89c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import os
os.environ["STREAMLIT_SERVER_ENABLE_FILE_WATCHER"] = "false"
import logging
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
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() or ""
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
)
return text_splitter.split_text(text)
# Function to create a FAISS vectorstore using Hugging Face embeddings
def get_vectorstore(text_chunks):
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
# Function to set up the conversational retrieval chain with memory
def get_conversation_chain(vectorstore):
try:
groq_api_key = os.getenv("GROQ_API_KEY")
llm = ChatGroq(model="llama3-8b-8192", api_key=groq_api_key, 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:
response = st.session_state.conversation({'question': user_question})
if 'chat_history' in response:
for i, msg in enumerate(response['chat_history']):
if i % 2 == 0:
st.write(f"**You:** {msg.content}")
else:
st.write(f"**Bot:** {msg.content}")
else:
st.write(f"**Bot:** {response['answer']}")
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
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() |