File size: 4,373 Bytes
4abc04d b402914 4abc04d b402914 4abc04d b402914 4abc04d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
from PyPDF2 import PdfReader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.chains import load_chain
from langchain.llms import OpenAI
import streamlit as st
# import pyautogui
import os, shutil
def delete_directory(directory_path):
try:
shutil.rmtree(directory_path)
print(f"Directory '{directory_path}' successfully deleted.")
except Exception as e:
print(f"Error deleting directory '{directory_path}': {e}")
st.set_page_config(page_title="Query any Pdf", page_icon="📄")
st.title("📄 PDF Query Bot 📄")
st.write("Made with ❤️ by Mainak")
def return_response(query,document_search,chain):
query = query
docs = document_search.similarity_search(query)
result = chain.run(input_documents=docs, question=query)
return result
uploaded_file = st.file_uploader("Upload a PDF File", type=["pdf"])
# API key input box
api_key = st.text_input("Enter Your OpenAI API Key",type="password")
if not uploaded_file:
try:
delete_directory('faiss_index')
except:
pass
if st.button('Submit'):
if api_key:
if uploaded_file is not None:
# Read text from the uploaded file
os.environ["OPENAI_API_KEY"] = api_key
with st.spinner('Wait for it...'):
pdfreader = PdfReader(uploaded_file)
# read text from pdf
raw_text = ''
for i, page in enumerate(pdfreader.pages):
content = page.extract_text()
if content:
raw_text += content
text_splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 800,
chunk_overlap = 200,
length_function = len,
)
texts = text_splitter.split_text(raw_text)
embeddings = OpenAIEmbeddings()
document_search = FAISS.from_texts(texts, embeddings)
document_search.save_local("faiss_index")
else:
st.warning("Please enter your Pdf File")
else:
st.warning("Please enter your API key")
if os.path.exists("faiss_index"):
# if st.checkbox("chat"):
if api_key:
if uploaded_file is not None:
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("What is up?"):
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
os.environ["OPENAI_API_KEY"] = api_key
embeddings = OpenAIEmbeddings()
document_search = FAISS.load_local("faiss_index", embeddings)
chain = load_qa_chain(OpenAI(), chain_type="stuff")
if prompt is None:
re='Ask me anything about the pdf'
# elif prompt=='exit':
# delete_directory('faiss_index')
# pyautogui.hotkey('f5') #Simulates F5 key press = page refresh
else:
with st.spinner('Typping...'):
re=return_response(str(prompt),document_search,chain)
response = f"PDF Mate: {re}"
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
else:
st.warning("Please enter your Pdf File")
else:
st.warning("Please enter your API key")
else:
pass |