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import streamlit as st
from dotenv import load_dotenv
import pickle
from PyPDF2 import PdfReader
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.callbacks import get_openai_callback
import os
# Sidebar contents
with st.sidebar:
st.title('🤗💬 LLM Chat App')
st.markdown('''
## About
This app is an LLM-powered chatbot built using:
- [Streamlit](https://streamlit.io/)
- [LangChain](https://python.langchain.com/)
- [OpenAI](https://platform.openai.com/docs/models) LLM model
''')
add_vertical_space(5)
st.write('Made with ❤️ by [Prompt Engineer](https://youtube.com/@engineerprompt)')
load_dotenv()
def main():
st.header("Chat with PDF 💬")
# upload a PDF file
pdf = st.file_uploader("Upload your PDF", type='pdf')
# st.write(pdf)
if pdf is not None:
pdf_reader = PdfReader(pdf)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text=text)
# # embeddings
store_name = pdf.name[:-4]
st.write(f'{store_name}')
# st.write(chunks)
if os.path.exists(f"{store_name}.pkl"):
with open(f"{store_name}.pkl", "rb") as f:
VectorStore = pickle.load(f)
# st.write('Embeddings Loaded from the Disk')s
else:
embeddings = OpenAIEmbeddings()
VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
with open(f"{store_name}.pkl", "wb") as f:
pickle.dump(VectorStore, f)
# embeddings = OpenAIEmbeddings()
# VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
# Accept user questions/query
query = st.text_input("Ask questions about your PDF file:")
# st.write(query)
if query:
docs = VectorStore.similarity_search(query=query, k=3)
llm = OpenAI()
chain = load_qa_chain(llm=llm, chain_type="stuff")
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=query)
print(cb)
st.write(response)
if __name__ == '__main__':
main() |