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import os
import streamlit as st
import re
from tempfile import NamedTemporaryFile
import time
import pathlib
#from PyPDF2 import PdfReader
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from langchain_community.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import PyPDFLoader
from langchain.memory import ConversationBufferWindowMemory
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.memory.chat_message_histories.streamlit import StreamlitChatMessageHistory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.llms import HuggingFaceHub
# sidebar contents
with st.sidebar:
st.title('DOC-QA DEMO ')
st.markdown('''
## About
Detail this application:
- LLM model: Phi-2-4bit
- Hardware resource : Huggingface space 8 vCPU 32 GB
''')
def split_docs(documents,chunk_size=1000):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=200)
sp_docs = text_splitter.split_documents(documents)
return sp_docs
@st.cache_resource
def load_llama2_llamaCpp():
core_model_name = "phi-2.Q4_K_M.gguf"
#n_gpu_layers = 32
n_batch = 512
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(
model_path=core_model_name,
#n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
callback_manager=callback_manager,
verbose=True,n_ctx = 4096, temperature = 0.1, max_tokens = 128
)
return llm
def set_custom_prompt():
custom_prompt_template = """ Use the following pieces of information from context to answer the user's question.
If you don't know the answer, don't try to make up an answer.
Context : {context}
Question : {question}
Please answer the questions in a concise and straightforward manner.
Helpful answer:
"""
prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context',
'question',
])
return prompt
@st.cache_resource
def load_embeddings():
embeddings = HuggingFaceEmbeddings(model_name = "thenlper/gte-base",
model_kwargs = {'device': 'cpu'})
return embeddings
def main():
data = []
sp_docs_list = []
msgs = StreamlitChatMessageHistory(key="langchain_messages")
print(msgs)
if "messages" not in st.session_state:
st.session_state.messages = []
# repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
# llm = HuggingFaceHub(
# repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_length": 128})
llm = load_llama2_llamaCpp()
qa_prompt = set_custom_prompt()
embeddings = load_embeddings()
uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
if uploaded_file is not None :
with NamedTemporaryFile(dir='PDF', suffix='.pdf', delete=False) as f:
f.write(uploaded_file.getbuffer())
print(f.name)
#filename = f.name
loader = PyPDFLoader(f.name)
pages = loader.load_and_split()
data.extend(pages)
#st.write(pages)
f.close()
os.unlink(f.name)
os.path.exists(f.name)
if len(data) > 0 :
embeddings = load_embeddings()
sp_docs = split_docs(documents = data)
st.write(f"This document have {len(sp_docs)} chunks")
sp_docs_list.extend(sp_docs)
try:
db = FAISS.from_documents(sp_docs_list, embeddings)
memory = ConversationBufferMemory(memory_key="chat_history",
return_messages=True,
input_key="query",
output_key="result")
qa_chain = RetrievalQA.from_chain_type(
llm = llm,
chain_type = "stuff",
retriever = db.as_retriever(search_kwargs = {'k':3}),
return_source_documents = True,
memory = memory,
chain_type_kwargs = {"prompt":qa_prompt})
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if query := st.chat_input("What is up?"):
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(query)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": query})
start = time.time()
response = qa_chain({'query': query})
with st.chat_message("assistant"):
st.markdown(response['result'])
end = time.time()
st.write("Respone time:",int(end-start),"sec")
print(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response['result']})
with st.expander("See the related documents"):
for count, url in enumerate(response['source_documents']):
st.write(str(count+1)+":", url)
clear_button = st.button("Start new convo")
if clear_button :
st.session_state.messages = []
qa_chain.memory.chat_memory.clear()
except:
st.write("Plaese upload your pdf file.")
if __name__ == '__main__':
main() |