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# GPT Chatbot
# Create Conda virtual environment
# conda create --name gpt_chatbot python=3.9.4
# conda activate gpt_chatbot
# Installation
# pip install streamlit pypdf2 langchain python-dotenv faiss-cpu openai huggingface_hub
# pip install tiktoken
# pip install InstructorEmbedding sentence_transformers
# Could not import tiktoken python package. This is needed in order to for OpenAIEmbeddings. Please install it with `pip install tiktoken`.
# run the app using the following command in anaconda VS Code terminal
# streamlit run app.py
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS # FAISS instead of PineCone
from langchain.llms import OpenAI
from langchain.llms import HuggingFaceHub
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
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(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):
embeddings = OpenAIEmbeddings()
#embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = OpenAI()
#llm = ChatOpenAI()
#llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
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
def handle_userinput(user_question):
# st.session_state.conversation contains all the configuration from our vectorstore and memory.
response = st.session_state.conversation({'question': user_question})
# st.write(response)
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)
def main():
load_dotenv()
st.set_page_config(page_title="Chat with multiple law journal PDFs",
page_icon=":books:")
st.write(css, unsafe_allow_html=True)
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)
#st.write(user_template.replace("{{MSG}}", "hello robot"), unsafe_allow_html=True)
#st.write(bot_template.replace("{{MSG}}", "hello human"), unsafe_allow_html=True)
# "https://i.ibb.co/rdZC7LZ/Photo-logo-1.png"
# "https://huggingface.co/spaces/gli-mrunal/GPT_instruct_chatbot/blob/main/images/bot.jpg"
# "https://huggingface.co/spaces/gli-mrunal/GPT_instruct_chatbot/blob/main/images/CSUN_Matadors_logo.svg.png"
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"):
# --------------- get pdf text -------------------
raw_text = get_pdf_text(pdf_docs)
#st.write(raw_text)
# ---------- get the text chunks -------------------------
text_chunks = get_text_chunks(raw_text)
#st.write(text_chunks)
# -------------- create vector store------------------------
# https://openai.com/pricing --> Embedding Models
# Chose to use the best embedding model - intructor_xl ranked higher than OpenAi's embeddings from huggingface leaderboard
# https://huggingface.co/spaces/mteb/leaderboard
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
#conversation = get_conversation_chain(vectorstore)
#st.session_state.conversation
if __name__ == '__main__':
main() |