ChatPDF_Llama2 / app.py
qorgh346
update app.py
15d201d
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter,RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS, Chroma
from langchain.embeddings import HuggingFaceEmbeddings # General embeddings from HuggingFace models.
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub, LlamaCpp,CTransformers # For loading transformer models.
from langchain.document_loaders import PyPDFLoader
from tempfile import NamedTemporaryFile
def get_pdf_text(pdf_docs):
# text = ''
# pdf_file_ = open(pdf_docs,'rb')
# text = "example hofjin"
# for page in pdf_reader.pages:
# text += page.extract_text()
# return text
with NamedTemporaryFile() as temp_file:
temp_file.write(pdf_docs.getvalue())
temp_file.seek(0)
pdf_loader = PyPDFLoader(temp_file.name)
# print('pdf_loader = ', pdf_loader)
pdf_doc = pdf_loader.load()
# print('pdf_doc = ',pdf_doc)
return pdf_doc
def get_text_chunks(documents):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 1000,
chunk_overlap = 200,
length_function= len
)
# text_splitter = CharacterTextSplitter(
# separator="\n",
# chunk_size=10f00,
# chunk_overlap=200,
# length_function=len
# )
documents = text_splitter.split_documents(documents)
print('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'})
print('embeddings = ', embeddings)
# embeddings = OpenAIEmbeddings()sentence-transformers/all-MiniLM-L6-v2
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
# model_kwargs={'device':'cpu'})
vectorstore = FAISS.from_documents(texts=text_chunks, embedding=embeddings)
# vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
model_path = 'llama-2-7b-chat.Q2_K.gguf'
# llm = ChatOpenAI()
# llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
config = {'max_new_tokens': 2048}
# llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q2_K.bin", model_type="llama", config=config)
llm = LlamaCpp(model_path=model_path,
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):
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 get_text_file(docs):
text = docs.read().decode("utf-8")
return text
def get_csv_file(docs):
import pandas as pd
text = ''
data = pd.read_csv(docs)
for index, row in data.iterrows():
item_name = row[0]
row_text = item_name
for col_name in data.columns[1:]:
row_text += '{} is {} '.format(col_name, row[col_name])
text += row_text + '\n'
return text
def get_json_file(docs):
import json
text = ''
# with open(docs, 'r') as f:
json_data = json.load(docs)
for f_key, f_value in json_data.items():
for s_value in f_value:
text += str(f_key) + str(s_value)
text += '\n'
#print(text)
return text
def get_hwp_file(docs):
pass
def get_docs_file(docs):
pass
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
raw_text += get_text_file(file)
elif file.type in ['application/octet-stream', 'application/pdf']:
#file is .pdf
doc_list.append(get_pdf_text(file))
elif file.type == 'text/csv':
#file is .csv
raw_text += get_csv_file(file)
elif file.type == 'application/json':
# file is .json
raw_text += get_json_file(file)
elif file.type == 'application/x-hwp':
# file is .hwp
raw_text += get_hwp_file(file)
elif file.type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
# file is .docs
raw_text += get_docs_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()