import PyPDF2
import pandas as pd
import numpy as np


lines = []
sites = []
kinds = []
total_offqc_wip_inv = []
total_offqc_scale_inv = []
total_offqc_truck_inv = []
total_offqc_rail_inv = []
total_offqc_boat_inv = []


# creating a pdf file object
pdfFileObj = open('PDFs/Inventory_Summary.pdf', 'rb')

# creating a pdf reader object
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
count = pdfReader.numPages

# creating a page object

pageObj0 = pdfReader.getPage(0)
pageObj1 = pdfReader.getPage(1)
pageObj2 = pdfReader.getPage(2)
pageObj3 = pdfReader.getPage(3)
pageObj4 = pdfReader.getPage(4)
pageObj5 = pdfReader.getPage(5)

# extracting text from page
page0 = pageObj0.extractText().strip()
page1 = pageObj1.extractText().strip()
page2 = pageObj2.extractText().strip()
page3 = pageObj3.extractText().strip()
page4 = pageObj4.extractText().strip()
page5 = pageObj5.extractText().strip()

corrected_page0 = page0.split('07:43am')[+1]
corrected_page1 = page1.split('07:43am')[+1]
corrected_page2 = page2.split('07:43am')[+1]
corrected_page3 = page3.split('07:43am')[+1]
corrected_page4 = page4.split('07:43am')[+1]
corrected_page5 = page5.split('07:43am')[+1]

for line in page0.splitlines():
    if 'Site' in line:
       for word in line.split():
           if word != 'Site':
                sites.append(word)
    if 'All Shifts' in line:
        for word in line.split():
            if word != 'All':
                if word != 'Shifts':
                    kinds.append(word)
    if 'Total OffQc WIP Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'WIP':
                        if word != 'Inv':
                            total_offqc_wip_inv.append(word)
    if 'Total OffQc Scale Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Scale':
                        if word != 'Inv':
                            total_offqc_scale_inv.append(word)
    if 'Total OffQc Truck Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Truck':
                        if word != 'Inv':
                            total_offqc_truck_inv.append(word)
for line in page1.splitlines():
    if 'Total OffQc Rail Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Rail':
                        if word != 'Inv':
                            total_offqc_rail_inv.append(word)
    if 'Total OffQc Boat Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Boat':
                        if word != 'Inv':
                            total_offqc_boat_inv.append(word)
for line in page3.splitlines():
    if 'Site' in line:
        for word in line.split():
           if word != 'Site':
                sites.append(word)
    if 'All Shifts' in line:
        for word in line.split():
            if word != 'All':
                if word != 'Shifts':
                    kinds.append(word)
    if 'Total OffQc WIP Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'WIP':
                        if word != 'Inv':
                            total_offqc_wip_inv.append(word)
    if 'Total OffQc Scale Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Scale':
                        if word != 'Inv':
                            total_offqc_scale_inv.append(word)
    if 'Total OffQc Truck Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Truck':
                        if word != 'Inv':
                            total_offqc_truck_inv.append(word)
for line in page4.splitlines():
    if 'Total OffQc Rail Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Rail':
                        if word != 'Inv':
                            total_offqc_rail_inv.append(word)
    if 'Total OffQc Boat Inv' in line:
        for word in line.split():
            if word != 'Total':
                if word != 'OffQc':
                    if word != 'Boat':
                        if word != 'Inv':
                            total_offqc_boat_inv.append(word)
sites.append("Total")

d = np.column_stack([sites, kinds, total_offqc_wip_inv, total_offqc_scale_inv, total_offqc_truck_inv, total_offqc_rail_inv, total_offqc_boat_inv])


df = pd.DataFrame(d)

# closing the pdf file object
pdfFileObj.close()