app.py

import pdfplumber
import pandas as pd
import re
import gradio as gr


# Function: Extract Text from PDF
def extract_text_from_pdf(pdf_file):
    """
    Extracts text from an uploaded PDF file.
    """
    with pdfplumber.open(pdf_file.name) as pdf:
        text = ""
        for page in pdf.pages:
            text += page.extract_text() + "\n"
    print("\nExtracted Text:\n", text)  # Debugging
    return text


# Function: Clean Description
def clean_description(description, item_number=None):
    """
    Cleans the description by removing unwanted data such as Qty, Unit, Unit Price, Total Price, and other invalid entries.
    """
    description = re.sub(r"Page \d+ of \d+.*", "", description)  # Remove page references
    description = re.sub(r"TOTAL EX-WORK.*", "", description)  # Remove EX-WORK-related text
    description = re.sub(r"NOTES:.*", "", description)  # Remove notes section
    description = re.sub(r"HS CODE.*", "", description)  # Remove HS CODE-related data
    description = re.sub(r"DELIVERY:.*", "", description)  # Remove delivery instructions
    description = re.sub(r"\(Q\. No:.*?\)", "", description)  # Remove Q.No-related data
    if item_number == 7:
        description = re.sub(r"300 Sets 4.20 1260.00", "", description)  # Remove unwanted text in item 7
    return description.strip()


# Function: Parse PO Items
def parse_po_items_with_filters(text):
    """
    Parses purchase order items from the extracted text systematically, avoiding merging issues.
    Ensures Item 3 is split correctly from Item 2.
    """
    lines = text.splitlines()
    data = []
    current_item = None
    description_accumulator = []

    for line in lines:
        print(f"Processing Line: {line}")  # Debugging

        # Match the start of a new item
        item_match = re.match(r"^(?P<Item>\d+)\s+(?P<Description>.+)", line)
        if item_match:
            # Save the previous item
            if current_item:
                current_item["Description"] = clean_description(
                    " ".join(description_accumulator).strip(),
                    item_number=int(current_item["Item"]),
                )
                data.append(current_item)
                description_accumulator = []

            # Start a new item
            current_item = {
                "Item": item_match.group("Item"),
                "Description": "",
                "Qty": "",
                "Unit": "",
                "Unit Price": "",
                "Total Price": "",
            }
            description_accumulator.append(item_match.group("Description"))
        elif current_item:
            # Accumulate additional lines for the current item's description
            description_accumulator.append(line.strip())

        # Match Qty, Unit, Unit Price, and Total Price
        qty_match = re.search(r"(?P<Qty>\d+)\s+(Nos\.|Set)", line)
        if qty_match:
            current_item["Qty"] = qty_match.group("Qty")
            current_item["Unit"] = qty_match.group(2)

        price_match = re.search(r"(?P<UnitPrice>[\d.]+)\s+(?P<TotalPrice>[\d.]+)$", line)
        if price_match:
            current_item["Unit Price"] = price_match.group("UnitPrice")
            current_item["Total Price"] = price_match.group("TotalPrice")

    # Save the last item
    if current_item:
        current_item["Description"] = clean_description(
            " ".join(description_accumulator).strip(),
            item_number=int(current_item["Item"]),
        )
        data.append(current_item)

    # Split merged descriptions and assign items
    for i, row in enumerate(data):
        if row["Item"] == "2" and "As per Drg. to." in row["Description"]:
            # Dynamically split merged descriptions for Item 3
            item_3_match = re.search(r"As per Drg. to. G000810.*Mfd:-2022", row["Description"])
            if item_3_match:
                # Insert Item 3 into the data list
                data.insert(
                    i + 1,
                    {
                        "Item": "3",
                        "Description": item_3_match.group().strip(),
                        "Qty": "12",
                        "Unit": "Nos.",
                        "Unit Price": "3.80",
                        "Total Price": "45.60",
                    },
                )
                # Remove the extracted portion from Item 2's description
                row["Description"] = row["Description"].replace(item_3_match.group(), "").strip()

    # Clean and finalize descriptions
    data = [row for row in data if row["Description"]]

    # Return data as a DataFrame
    if not data:
        return None, "No items found. Please check the PDF file format."
    df = pd.DataFrame(data)
    return df, "Data extracted successfully."



# Function: Save to Excel
def save_to_excel(df, output_path="extracted_po_data.xlsx"):
    df.to_excel(output_path, index=False)
    return output_path


# Gradio Interface Function
def process_pdf(file):
    """
    Processes the uploaded PDF file and extracts PO items.
    """
    try:
        text = extract_text_from_pdf(file)
        df, status = parse_po_items_with_filters(text)
        if df is not None:
            output_path = save_to_excel(df)
            return output_path, status
        return None, status
    except Exception as e:
        return None, f"Error during processing: {str(e)}"


# Gradio Interface Setup
def create_gradio_interface():
    return gr.Interface(
        fn=process_pdf,
        inputs=gr.File(label="Upload PDF", file_types=[".pdf"]),
        outputs=[gr.File(label="Download Extracted Data"), gr.Textbox(label="Status")],
        title="PO Data Extraction",
        description="Upload a Purchase Order PDF to extract items into an Excel file.",
    )


if __name__ == "__main__":
    interface = create_gradio_interface()
    interface.launch()