Upload app.py
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app.py
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import os
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import pickle
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import streamlit as st
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from streamlit_extras.add_vertical_space import add_vertical_space
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from PyPDF2 import PdfReader
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from openai.embeddings_utils import get_embedding
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import openai
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from dotenv import load_dotenv
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.llms import OpenAI
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from langchain.chains.question_answering import load_qa_chain
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from langchain.callbacks import get_openai_callback
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# Sidebar contents
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with st.sidebar:
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st.title('🤗LLM Chat App💬')
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st.markdown('''
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## About
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OpenAI based LLM-powered chatbot built using:
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- [OpenAI](https://platform.openai.com/docs/models) LLM model
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- [Streamlit](https://streamlit.io/)
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- [LangChain](https://python.langchain.com/)
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''')
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add_vertical_space(5)
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st.write('Made with ❤️ by Harry')
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# Load environment variables
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# load_dotenv()
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# # Retrieve OpenAI API key
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# openai_api_key = os.getenv("OPENAI_API_KEY")
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# if openai_api_key is None:
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# raise ValueError("The OPENAI_API_KEY environment variable is not set")
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# # Set the OpenAI API key for the OpenAI library
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# openai.api_key = openai_api_key
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def extract_text_from_pdf(pdf):
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pdf_reader = PdfReader(pdf)
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text = ""
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for page in pdf_reader.pages:
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text += page.extract_text()
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return text
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def get_embeddings(text_list):
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return [get_embedding(text) for text in text_list]
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def main():
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st.header("Chat with PDF 💬")
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# Upload a PDF file
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pdf = st.file_uploader("Upload your PDF file", type='pdf')
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if pdf is not None:
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# Extract text from the PDF
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text = extract_text_from_pdf(pdf)
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# Split text into chunks
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=1000,
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chunk_overlap=200,
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length_function=len
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)
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chunks = text_splitter.split_text(text=text)
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# chunks data with langchain
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#chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size - chunk_overlap)]
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st.write("PDF content successfully extracted.")
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#st.write("Below is chunks data")
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#st.write(chunks)
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# Create or load embeddings
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store_name = pdf.name[:-4]
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st.write(f'Processing: {store_name}')
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if os.path.exists(f"{store_name}.pkl"):
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with open(f"{store_name}.pkl", "rb") as f:
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VectorStore = pickle.load(f)
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st.write('Embeddings loaded from the disk')
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else:
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embeddings = OpenAIEmbeddings()
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VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
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with open(f"{store_name}.pkl", "wb") as f:
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pickle.dump(VectorStore, f)
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st.write('Embeddings created and saved to disk')
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# Accept user questions/query
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query = st.text_input("Ask questions about your PDF file:")
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if query:
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docs = VectorStore.similarity_search(query=query, k=3)
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llm = OpenAI(model_name="gpt-3.5-turbo")
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chain = load_qa_chain(llm=llm, chain_type="stuff")
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with get_openai_callback() as cb:
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response = chain.run(input_documents=docs, question=query)
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print(cb)
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st.write(response)
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if __name__ == '__main__':
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main()
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