app.py

import streamlit as st # data app development
import subprocess # process in the os
from subprocess import STDOUT, check_call #os process manipulation
import os #os proces manipulation
import base64 # byte object into a pdf file
import camelot as cam # extracting tables from PDFs


# to run this only once 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 the 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.b64encode(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 of the table

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)