Spaces:
Build error
Build error
File size: 3,950 Bytes
81feaed fc12dc4 81feaed c499cc4 81feaed 972f10b 81feaed fc12dc4 81feaed 972f10b 81feaed 48df697 c499cc4 5160970 81feaed 1f5c267 81feaed a923c0e 2ae08a1 81feaed | 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 | 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, GooglePalmEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.chat_models import ChatGooglePalm
from langchain.llms import HuggingFaceHub
from htmlTemplates import bot_template, user_template, css
from transformers import pipeline
def get_pdf_text(pdf_files):
text = ""
for pdf_file in pdf_files:
reader = PdfReader(pdf_file)
for page in reader.pages:
text += page.extract_text()
return text
def get_chunk_text(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_vector_store(text_chunks):
# For OpenAI Embeddings
# embeddings = OpenAIEmbeddings()
# For Huggingface Embeddings
# embeddings = HuggingFaceInstructEmbeddings(model_name = "hkunlp/instructor-xl")
# For GooglePalm Embeddings
embeddings = GooglePalmEmbeddings(model_name = "models/embedding-gecko-001")
vectorstore = FAISS.from_texts(texts = text_chunks, embedding = embeddings)
return vectorstore
def get_conversation_chain(vector_store):
# OpenAI Model
# llm = ChatOpenAI()
# HuggingFace Model
# llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
# GooglePalm Model
llm = ChatGooglePalm(model_name="models/chat-bison-001", model_kwargs={"temperature":0.7, "max_length":800})
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm = llm,
retriever = vector_store.as_retriever(),
memory = memory
)
return conversation_chain
def handle_user_input(question):
response = st.session_state.conversation({'question':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 Your own 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 Your own PDFs :books:')
question = st.text_input("Ask anything to your PDF: ")
if question:
handle_user_input(question)
with st.sidebar:
st.subheader("Upload your Documents Here: ")
pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)
if st.button("OK"):
with st.spinner("Processing your PDFs..."):
# Get PDF Text
raw_text = get_pdf_text(pdf_files)
# Get Text Chunks
text_chunks = get_chunk_text(raw_text)
# Create Vector Store
vector_store = get_vector_store(text_chunks)
st.write("DONE")
# Create conversation chain
st.session_state.conversation = get_conversation_chain(vector_store)
if __name__ == '__main__':
main() |