app.py

import streamlit as st # data app development
import subprocess # process in the os
from subprocess import STDOUT, check_call #os process manipuation
import os #os process manipuation
import base64 # byte object into a pdf file 
import camelot as cam # extracting tables from PDFs 
import cv2
# to run this only once and it's cached
@st.cache
def gh():
    """install ghostscript on the linux machine"""
    proc = subprocess.Popen('apt-get install -y ghostscript', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
    proc.wait()

gh()



st.title("PDF Table Extractor")
st.subheader("with `Camelot` Python library")

st.image("https://raw.githubusercontent.com/camelot-dev/camelot/master/docs/_static/camelot.png", width=200)


# file uploader on streamlit 

input_pdf = st.file_uploader(label = "upload your pdf here", type = 'pdf')

st.markdown("### Page Number")

page_number = st.text_input("Enter the page # from where you want to extract the PDF eg: 3", value = 1)

# run this only when a PDF is uploaded

if input_pdf is not None:
    # byte object into a PDF file 
    with open("input.pdf", "wb") as f:
        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
        f.write(base64.b64decode(base64_pdf))
    f.close()

    # read the pdf and parse it using stream
    table = cam.read_pdf("input.pdf", pages = page_number, flavor = 'stream')

    st.markdown("### Number of Tables")

    # display the output after parsing 
    st.write(table)

    # display the table

    if len(table) > 0:

        # extract the index value of the table
        
        option = st.selectbox(label = "Select the Table to be displayed", options = range(len(table) + 1))

        st.markdown('### Output Table')

        # display the dataframe
        
        st.dataframe(table[int(option)-1].df)