Spaces:
Sleeping
Sleeping
File size: 6,380 Bytes
9be167d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
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
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
import os
OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
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 = 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):
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")
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)
# get the text chunks
text_chunks = get_text_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(
vectorstore)
if __name__ == '__main__':
main()
# Attempting uninstall: tokenizers
# Found existing installation: tokenizers 0.15.2
# Uninstalling tokenizers-0.15.2:
# Successfully uninstalled tokenizers-0.15.2
# Attempting uninstall: faiss-cpu
# Found existing installation: faiss-cpu 1.8.0.post1
# Uninstalling faiss-cpu-1.8.0.post1:
# Successfully uninstalled faiss-cpu-1.8.0.post1
# Attempting uninstall: python-dotenv
# Found existing installation: python-dotenv 1.0.1
# Uninstalling python-dotenv-1.0.1:
# Successfully uninstalled python-dotenv-1.0.1
# Attempting uninstall: pydantic
# Found existing installation: pydantic 2.10.2
# Uninstalling pydantic-2.10.2:
# Successfully uninstalled pydantic-2.10.2
# Attempting uninstall: protobuf
# Found existing installation: protobuf 4.25.5
# Uninstalling protobuf-4.25.5:
# Successfully uninstalled protobuf-4.25.5
# Attempting uninstall: torch
# Found existing installation: torch 2.1.1
# Uninstalling torch-2.1.1:
# Successfully uninstalled torch-2.1.1
# Attempting uninstall: tiktoken
# Found existing installation: tiktoken 0.7.0
# Uninstalling tiktoken-0.7.0:
# Successfully uninstalled tiktoken-0.7.0
# Attempting uninstall: huggingface-hub
# Found existing installation: huggingface-hub 0.23.4
# Uninstalling huggingface-hub-0.23.4:
# Successfully uninstalled huggingface-hub-0.23.4
# Attempting uninstall: dataclasses-json
# Found existing installation: dataclasses-json 0.6.7
# Uninstalling dataclasses-json-0.6.7:
# Successfully uninstalled dataclasses-json-0.6.7
# Attempting uninstall: transformers
# Found existing installation: transformers 4.35.2
# Uninstalling transformers-4.35.2:
# Successfully uninstalled transformers-4.35.2
# Attempting uninstall: openai
# Found existing installation: openai 1.57.4
# Uninstalling openai-1.57.4:
# Successfully uninstalled openai-1.57.4
# Attempting uninstall: langchain
# Found existing installation: langchain 0.2.5
# Uninstalling langchain-0.2.5:
# Successfully uninstalled langchain-0.2.5
# Attempting uninstall: sentence-transformers
# Found existing installation: sentence-transformers 3.0.1
# Uninstalling sentence-transformers-3.0.1:
# Successfully uninstalled sentence-transformers-3.0.1
# Attempting uninstall: altair
# Found existing installation: altair 5.5.0
# Uninstalling altair-5.5.0:
# Successfully uninstalled altair-5.5.0
# Attempting uninstall: streamlit
# Found existing installation: streamlit 1.41.1
# Uninstalling streamlit-1.41.1:
# Successfully uninstalled streamlit-1.41.1 |