| from dotenv import load_dotenv |
| import streamlit as st |
| from PyPDF2 import PdfReader |
| from langchain.text_splitter import CharacterTextSplitter |
| from langchain.embeddings.openai 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 |
|
|
|
|
| def main(): |
| load_dotenv() |
| st.set_page_config(page_title="Ask your PDF") |
| st.header("Ask your PDF 💬") |
| |
| |
| pdf = st.file_uploader("Upload your PDF", type="pdf") |
| |
| |
| if pdf is not None: |
| pdf_reader = PdfReader(pdf) |
| 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("Ask a question about your PDF:") |
| 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) |
| |
|
|
| if __name__ == '__main__': |
| main() |
