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import os | |
import streamlit as st | |
from PyPDF2 import PdfReader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.vectorstores import FAISS | |
from langchain.chains.question_answering import load_qa_chain | |
from langchain.llms import OpenAI | |
from langchain.callbacks import get_openai_callback | |
from dotenv import load_dotenv | |
# Load environment variables | |
load_dotenv() | |
def main(): | |
st.set_page_config(page_title="PDF Chat") | |
st.header("Chat with your PDFs 💬") | |
#Credit | |
st.write(" ") | |
st.write("Visit us [here](https://ai-solutions.ai) for more AI Solutions.") | |
st.write(" ") | |
st.write(" ") | |
# Upload PDF files | |
pdf_files = st.file_uploader("Upload your PDF files (please do not upload anything confidential for this demo)", type="pdf", accept_multiple_files=True) | |
if pdf_files: | |
for idx, pdf_file in enumerate(pdf_files): | |
try: | |
pdf_reader = PdfReader(pdf_file) | |
text = "" | |
for page in pdf_reader.pages: | |
text += page.extract_text() | |
text_splitter = CharacterTextSplitter( | |
separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len | |
) | |
chunks = text_splitter.split_text(text) | |
embeddings = OpenAIEmbeddings() | |
knowledge_base = FAISS.from_texts(chunks, embeddings) | |
user_question = st.text_input(f"Ask a question about '{pdf_file.name}':", key=f"question_{idx}") | |
if user_question: | |
docs = knowledge_base.similarity_search(user_question) | |
llm = OpenAI() | |
chain = load_qa_chain(llm, chain_type="stuff") | |
with get_openai_callback() as cb: | |
response = chain.run(input_documents=docs, question=user_question) | |
print(cb) | |
st.write(response) | |
except Exception as e: | |
st.error(f"An error occurred while processing '{pdf_file.name}'. This file may be protected by the author, or contain scanned text which this basic demo is not set up to process.") | |
if __name__ == "__main__": | |
main() |