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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.embeddings import HuggingFaceInstructEmbeddings, OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
from langchain.chat_models import ChatOpenAI
load_dotenv()
def update_api_token(model_choice, api_token):
dotenv_file = '.env'
if model_choice == "OpenAI":
with open(dotenv_file, 'r') as file:
lines = file.readlines()
with open(dotenv_file, 'w') as file:
for line in lines:
if line.startswith("OPENAI_API_KEY"):
file.write(f"OPENAI_API_KEY={api_token}\n")
else:
file.write(line)
os.environ['OPENAI_API_KEY'] = api_token
elif model_choice == "HuggingFace":
with open(dotenv_file, 'r') as file:
lines = file.readlines()
with open(dotenv_file, 'w') as file:
for line in lines:
if line.startswith("HUGGINGFACEHUB_API_TOKEN"):
file.write(f"HUGGINGFACEHUB_API_TOKEN={api_token}\n")
else:
file.write(line)
os.environ['HUGGINGFACEHUB_API_TOKEN'] = api_token
def validate_token(model_choice):
if 'validation_done' not in st.session_state:
try:
if model_choice == "OpenAI":
st.session_state.EMBEDDINGS = OpenAIEmbeddings()
st.session_state.LLM = ChatOpenAI()
else:
st.session_state.EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
st.session_state.LLM = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature": 0.5, "max_length": 512})
st.session_state.validation_done = True
return True
except Exception as e:
return False
else:
return True
def get_pdf_text(pdf_docs):
text = ""
if pdf_docs:
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):
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(llm, embeddings, vectorstore=None):
if llm is None or embeddings is None:
raise ValueError("LLM or EMBEDDINGS is not initialized.")
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
if vectorstore is None:
dummy_text = [""]
vectorstore = FAISS.from_texts(texts=dummy_text, embedding=embeddings)
retriever = vectorstore.as_retriever()
conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)
return conversation_chain
def handle_userinput(user_question):
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)
def main():
global LLM, EMBEDDINGS
LLM = None
EMBEDDINGS = None
st.set_page_config(page_title="MultiDoc_ChatBot", page_icon=":mag:")
st.write(css, unsafe_allow_html=True)
st.header("Chat with multiple PDFs :mag:")
# User options for LLM and Embeddings
model_choice = st.radio("Choose your model source", ("OpenAI", "HuggingFace"))
api_token = st.text_input("Enter your API token", type="password")
if st.button("Save API Token"):
update_api_token(model_choice, api_token)
with st.spinner("Validating API Token..."):
if validate_token(model_choice):
st.success(f"{model_choice} API token saved and model uploaded!")
else:
st.error("Invalid API token. Please try again.")
print("LLM : ", st.session_state.LLM)
print("EMBEDDINGS : ", st.session_state.EMBEDDINGS)
if 'LLM' in st.session_state:
LLM = st.session_state.LLM
if 'EMBEDDINGS' in st.session_state:
EMBEDDINGS = st.session_state.EMBEDDINGS
if "user_question" not in st.session_state:
st.session_state.user_question = ""
user_question = st.text_input("Ask a question about your documents:", key="question_input", value=st.session_state.user_question)
submit_button = st.button("Submit")
if submit_button and user_question:
if LLM is None or EMBEDDINGS is None:
st.error("LLM or EMBEDDINGS is not initialized.")
else:
if "conversation" not in st.session_state:
st.session_state.conversation = get_conversation_chain(LLM, EMBEDDINGS)
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
handle_userinput(user_question)
st.session_state.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"):
if LLM is None or EMBEDDINGS is None:
st.error("LLM or EMBEDDINGS is not initialized.")
else:
with st.spinner("Processing"):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
vectorstore = get_vectorstore(text_chunks, EMBEDDINGS)
st.session_state.conversation = get_conversation_chain(LLM, EMBEDDINGS, vectorstore=vectorstore)
if __name__ == '__main__':
main()
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