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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.memory import ConversationBufferMemory
from langchain.chains import ConversationRetrievalChain
from langchain.llms import ChatOpenAI
def get_html(html):
text = ""
for pdf in html:
pdf_reader= PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_chunk_text(raw_text):
text_splitter = CharacterTextSplitter(seperator="\n", chunk_size=1000, chunk_overlap=20,length_function=len)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
# embeddings = OpenAIEmbeddings()
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vector_store = FAISS.from_texts(texts=text_chunks,embedding = embeddings)
return vector_store
def get_conversation_chain(vector_store):
llm = ChatOpenAI()
memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
conversation_chain = ConversationRetrievalChain.from_llm(
llm = llm,
retriever = vector_store.as_retriever(),
memory = memory
)
return conversation_chain
def handle_input(user_input):
response = st.session_state.conversation({"question":user_input})
st.write(response)
def main():
load_dotenv()
st.set_page_config(page_title="Reads your html",page_icon=":books:")
if "conversation" not in st.session_state:
st.session_state.conversation = None
st.header("Get your best Element")
user_input = st.text_input("Pass your Element with its information")
if user_input:
handle_input(user_input)
with st.sidebar:
st.subheader("your html")
html_docs = st.file_uploader("upload your html file and click process")
if st.button("process"):
with st.spinner("processing"):
#get pdf text
raw_text = get_html(html_docs)
#get the text chunks
text_chunks = get_chunk_text(raw_text)
#create vector store
vector_store = get_vector_store(text_chunks)
#create conversation chain
st.session_state.conversation = get_conversation_chain(vector_store)
if __name__ == '__main__':
main() |