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import pdfplumber
import pandas as pd
import re
import tempfile
def extract_toshiba_data(pdf_file):
data = []
purchase_order, order_date = None, None
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
text = page.extract_text().splitlines()
# Extract Purchase Order and Order Date if not already found
if not purchase_order or not order_date:
for line in text:
po_match = re.search(r'Purchase Order\s*:\s*(P\d+)', line)
date_match = re.search(r'Order Date\s*:\s*([\d-]+)', line)
if po_match:
purchase_order = po_match.group(1)
if date_match:
order_date = date_match.group(1)
# Extract item details using patterns
for line in text:
# Match each line with expected pattern for item rows
item_match = re.match(r'(\d+)\s+(\d+)\s+(.*?)\s+([\d-]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)', line)
if item_match:
pos = int(item_match.group(1)) # Position number
item_code = item_match.group(2) # Item Code
item_name = item_match.group(3).strip() # Item Name/Description (if available)
delivery_date = item_match.group(4) # Delivery Date
quantity = float(item_match.group(5)) # Quantity
basic_price = float(item_match.group(6)) # Basic Price
amount = float(item_match.group(7)) # Calculated Amount
sub_total = float(item_match.group(8)) # Subtotal or final price
data.append([purchase_order, order_date, pos, item_code, item_name, delivery_date, quantity, basic_price, amount, sub_total])
# Define DataFrame with the corrected structure
df = pd.DataFrame(data, columns=["Purchase Order", "Order Date", "Pos", "Item Code", "Item Name", "Delivery Date", "Quantity", "Basic Price", "Amount", "SUB TOTAL"])
# Save to Excel file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx")
df.to_excel(temp_file.name, index=False)
return temp_file.name
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