import re
import pandas as pd
import gradio as gr


def extract_po_data(text):
    """
    Extracts purchase order data from the text into structured rows with ITEM, DESCRIPTION, QTY, UNIT, UNIT PRICE, TOTAL PRICE.
    Args:
        text (str): Raw text extracted from the PDF.
    Returns:
        tuple: A DataFrame containing structured data and a status message.
    """
    lines = text.splitlines()
    data = []

    for line in lines:
        # Match table row patterns with a flexible regex
        row_match = re.match(
            r"^\s*(?P<Item>\d+)\s+(?P<Description>.+?)\s+(?P<Qty>\d+)\s+(?P<Unit>(Nos\.|Set))\s+(?P<UnitPrice>[\d.]+)\s+(?P<TotalPrice>[\d.]+)\s*$",
            line,
        )
        if row_match:
            # Extract fields
            item = row_match.group("Item")
            description = format_description(row_match.group("Description"))
            qty = row_match.group("Qty")
            unit = row_match.group("Unit")
            unit_price = row_match.group("UnitPrice")
            total_price = row_match.group("TotalPrice")

            # Append to the data list
            data.append(
                {
                    "ITEM": item,
                    "DESCRIPTION": description,
                    "QTY": qty,
                    "UNIT": unit,
                    "UNIT PRICE": unit_price,
                    "TOTAL PRICE": total_price,
                }
            )
        else:
            # Log invalid row for debugging
            print(f"Skipping line (does not match expected format): {line}")

    # Convert to DataFrame
    if not data:
        return None, "No valid data found in the provided text."
    df = pd.DataFrame(data)
    return df, "Data extracted successfully."


def format_description(description):
    """
    Formats the description field into multiple lines based on predefined structure.
    Args:
        description (str): Raw description text.
    Returns:
        str: Formatted description with line breaks.
    """
    # Define patterns for splitting the description
    line1 = re.search(r"Stainless Steel RATING AND DIAGRAM PLATE", description)
    line2 = re.search(r"As per Drg\.No\..*?[A-Z0-9]+\s", description)
    line3 = re.search(r"SIZE\s*:\s*\d+mm\s*X\s*\d+mm\s*X\s*[\d.]+mm\s*Thick", description)
    line4 = re.search(r"With Serial No:.*", description)

    # Build the formatted description
    lines = []
    if line1:
        lines.append(line1.group().strip())
    if line2:
        lines.append(line2.group().strip())
    if line3:
        lines.append(line3.group().strip())
    if line4:
        lines.append(line4.group().strip())

    return "\n".join(lines)


def save_to_excel(df, output_path="extracted_po_data.xlsx"):
    """
    Saves the extracted data to an Excel file.
    Args:
        df (pd.DataFrame): DataFrame containing the structured data.
        output_path (str): Path to save the Excel file.
    Returns:
        str: Path to the saved file.
    """
    df.to_excel(output_path, index=False)
    return output_path


def process_text_input(text):
    """
    Processes the raw text input, extracts data, and saves it to an Excel file.
    Args:
        text (str): Raw text input.
    Returns:
        str: Path to the saved Excel file.
    """
    df, status = extract_po_data(text)
    if df is not None:
        output_path = save_to_excel(df)
        return output_path, status
    return None, status


# Gradio Interface
def create_interface():
    """
    Creates a Gradio interface for processing PO data.
    """
    interface = gr.Interface(
        fn=process_text_input,
        inputs=gr.Textbox(label="Paste Raw Text from PDF", lines=10, placeholder="Paste extracted text here..."),
        outputs=[
            gr.File(label="Download Extracted Excel"),
            gr.Textbox(label="Status"),
        ],
        title="PO Data Extraction",
        description="Paste the raw text from the PDF to extract purchase order data into an Excel file.",
    )
    return interface


if __name__ == "__main__":
    # Run the Gradio app
    app = create_interface()
    app.launch()