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import streamlit as st
from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings # General embeddings from HuggingFace models.
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import LlamaCpp # For loading transformer models.
from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader
from tempfile import NamedTemporaryFile
from huggingface_hub import hf_hub_download
def get_pdf_text(pdf_docs):
with NamedTemporaryFile() as temp_file:
temp_file.write(pdf_docs.getvalue())
temp_file.seek(0)
pdf_loader = PyPDFLoader(temp_file.name)
pdf_doc = pdf_loader.load()
return pdf_doc
def get_text_file(docs):
with NamedTemporaryFile() as temp_file:
temp_file.write(docs.getvalue())
temp_file.seek(0)
text_loader = TextLoader(temp_file.name)
text_doc = text_loader.load()
return text_doc
def get_csv_file(docs):
with NamedTemporaryFile() as temp_file:
temp_file.write(docs.getvalue())
temp_file.seek(0)
text_loader = CSVLoader(temp_file.name)
text_doc = text_loader.load()
return text_doc
def get_json_file(docs):
with NamedTemporaryFile() as temp_file:
temp_file.write(docs.getvalue())
temp_file.seek(0)
json_loader = JSONLoader(temp_file.name,
jq_schema='.scans[].relationships',
text_content=False)
json_doc = json_loader.load()
return json_doc
def get_text_chunks(documents):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
documents = text_splitter.split_documents(documents)
return documents
def get_vectorstore(text_chunks):
# Load the desired embeddings model.
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2',
model_kwargs={'device': 'cpu'})
vectorstore = FAISS.from_documents(text_chunks, embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
model_name_or_path = 'TheBloke/Llama-2-7B-chat-GGUF'
model_basename = 'llama-2-7b-chat.Q2_K.gguf'
model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
llm = LlamaCpp(model_path=model_path,
n_ctx=4086,
input={"temperature": 0.75, "max_length": 2000, "top_p": 1},
verbose=True, )
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):
print('user_question => ', 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():
load_dotenv()
st.set_page_config(page_title="Chat with multiple 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)
with st.sidebar:
st.subheader("Your documents")
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
doc_list = []
for file in docs:
print('file - type : ', file.type)
if file.type == 'text/plain':
# file is .txt
doc_list.extend(get_text_file(file))
elif file.type in ['application/octet-stream', 'application/pdf']:
# file is .pdf
doc_list.extend(get_pdf_text(file))
elif file.type == 'text/csv':
# file is .csv
doc_list.extend(get_csv_file(file))
elif file.type == 'application/json':
# file is .json
doc_list.extend(get_json_file(file))
# get the text chunks
text_chunks = get_text_chunks(doc_list)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(
vectorstore)
if __name__ == '__main__':
main()