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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 | |
| with st.sidebar: | |
| st.title('PDF Chat App') | |
| st.markdown(''' | |
| ## About | |
| This app is an LLM-powered PDF chatbot built using: | |
| - [Streamlit](https://streamlit.io/) | |
| - [LangChain](https://python.langchain.com/) | |
| - [OpenAI](https://platform.openai.com/docs/models) LLM model | |
| ## How it works | |
| - Load up a PDF file | |
| - Extract the text from the PDF file | |
| - Split the text into chunks | |
| - Create embeddings using OpenAI, which are vectors of floating-point numbers that measure the relatedness of text strings | |
| - Save these embeddings as vectors in a vector store, such as FAISS | |
| - Use a similarity search to ask a question | |
| - Get the answer and tokens used from OpenAI | |
| ''') | |
| st.write('Made with 🤖 by [Cazimir Roman](https://cazimir.dev)') | |
| def load_app(): | |
| # upload a PDF file | |
| 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 = RecursiveCharacterTextSplitter( | |
| chunk_size = 1000, | |
| chunk_overlap=200, | |
| length_function=len | |
| ) | |
| chunks = text_splitter.split_text(text=text) | |
| store_name = pdf.name[:-4] | |
| # check if vector store exists. if not, create one | |
| if os.path.exists(f"{store_name}.pkl"): | |
| with open(f"{store_name}.pkl", "rb") as f: | |
| vectorStore = pickle.load(f) | |
| st.success('Text embeddings loaded from disk') | |
| else: | |
| with st.spinner("Creating vector store embeddings..."): | |
| embeddings = OpenAIEmbeddings() | |
| vectorStore = FAISS.from_texts(chunks, embeddings) | |
| with open(f"{store_name}.pkl", "wb") as f: | |
| pickle.dump(vectorStore, f) | |
| st.success('Embeddings computation completed') | |
| # Accept user question/query | |
| st.divider() | |
| query = st.text_input("Ask a question about your PDF file") | |
| if query: | |
| st.write(f"You asked: {query}") | |
| with st.spinner("Thinking..."): | |
| # top 3 that are most similar to our query | |
| docs = vectorStore.similarity_search(query) | |
| llm = OpenAI(temperature=0) | |
| chain = load_qa_chain(llm=llm, chain_type="stuff") | |
| with get_openai_callback() as cb: | |
| response = chain.run(input_documents=docs, question=query) | |
| st.write(response) | |
| def main(): | |
| print("Main called") | |
| st.header("Chat with your PDF") | |
| container = st.container() | |
| with container: | |
| open_ai_key = os.getenv("OPENAI_API_KEY") | |
| api_key = container.text_input("Enter your OpenAI API key", type="password", value="" if open_ai_key == None else open_ai_key) | |
| # You can find it here: https://platform.openai.com/account/api-keys | |
| submit = container.button("Submit") | |
| if open_ai_key: | |
| load_app() | |
| # submit button is pressed | |
| if submit: | |
| # check if api key length correct | |
| if len(api_key) == 51: | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| load_app() | |
| else: | |
| st.error("Api key is not correct") | |
| if __name__ == '__main__': | |
| main() |