Upload App_For_PDF_To_Dataframe.py

App_For_PDF_To_Dataframe.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Feb 19 20:23:31 2022
+
+@author: nperuma
+"""
+
+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 
+
+# 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("Extract the contents in ease")
+
+st.image("https://raw.githubusercontent.com/camelot-dev/camelot/master/docs/_static/camelot.png", width=150)
+
+
+
+# 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)
+        
+              
+
+    @st.cache
+    def convert_df(df):
+          # IMPORTANT: Cache the conversion to prevent computation on every rerun
+          return df.to_csv().encode('utf-8')
+    
+    csv = convert_df(table[int(option)-1].df)
+
+    st.download_button(
+          label="Download data as CSV",
+          data=csv,
+          file_name='Data_table.csv',
+          mime='text/csv',
+      )
+