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import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI
from langchain_community.llms import HuggingFaceHub
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_community.document_loaders import DirectoryLoader
from htmlTemplates import css, bot_template, user_template
from langchain.globals import set_verbose
set_verbose(False) # Updated function call
def read_files_from_directory(directory):
files = []
for filename in os.listdir(directory):
if filename.endswith(".pdf"):
files.append(os.path.join(directory, filename))
return 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
def get_text_chunks(raw_text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(raw_text)
return chunks
def get_vector_store(text_chunks):
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
embeddings = OpenAIEmbeddings(openai_api_key=os.getenv('OPENAI_API_KEY'))
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
if not os.getenv('OPENAI_API_KEY') and not os.getenv('LAW_GPT_MODEL_URL'):
raise ValueError("Please provide either OPENAI_API_KEY or LAW_GPT_MODEL_URL in the .env file")
# Use LAW GPT model if LAW_GPT_MODEL_URL is provided
if os.getenv('LAW_GPT_MODEL_URL'):
llm = HuggingFaceHub(repo_id=os.getenv('LAW_GPT_MODEL_URL'))
else:
llm = ChatOpenAI(openai_api_key=os.getenv('OPENAI_API_KEY'))
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
# get handler user input method
def handle_user_input(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(user_template.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
else:
st.write(bot_template.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
else:
st.write("No data is loaded for RAG. Please upload a PDFs files to the data/ directory.")
def main():
load_dotenv()
st.set_page_config(page_title="EULawGPT - LLM model that can understand and reason about EU public domain data", page_icon=":books:")
st.write(css, unsafe_allow_html=True)
#load knowledge data PDF
files = read_files_from_directory('./data')
raw_knowledge_text = get_pdf_text(files)
raw_knowledge_chunks = get_text_chunks(raw_knowledge_text)
vectorstore_knowledge = get_vector_store(raw_knowledge_chunks)
st.session_state.conversation = get_conversation_chain(vectorstore_knowledge)
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.title("EU Law GPT")
st.write("EU Law GPT is a LLM model that can understand and reason about EU public domain data")
st.subheader('Popular questions:')
if st.button("What is happening in Equador?"):
handle_user_input("What is happening in Equador?")
if st.button("What EU will do with Ecuador crisis?"):
handle_user_input("What EU will do with Ecuador crisis?")
st.subheader('Ask anything:')
user_question = st.text_input("Ask a question about EU Law and Parlament work")
if user_question:
handle_user_input(user_question)
if __name__ == '__main__':
main() |