app.py

```python
import gradio as gr
import json
import re
import spaces

from docling.document_converter import (
    DocumentConverter,
    PdfFormatOption
)

from docling.datamodel.pipeline_options import (
    PdfPipelineOptions,
    TesseractCliOcrOptions
)

from docling.datamodel.base_models import InputFormat


# -----------------------------
# Helpers
# -----------------------------

def clean_amount(x):
    if not x:
        return ""
    return x.replace(",", "").strip()


def convert_date(d):
    m = re.match(r"(\d{2})-(\d{2})-(\d{2,4})", d)
    if not m:
        return d

    dd, mm, yy = m.groups()

    yyyy = "20" + yy if len(yy) == 2 else yy

    return f"{yyyy}-{mm}-{dd}"


def detect_voucher_type(text):
    text = text.upper()

    receipt_words = [
        "CR",
        "DEPOSIT",
        "SUBSIDY",
        "INTEREST",
        "CREDIT",
        "NEFT",
        "IMPS",
        "RTGS"
    ]

    for word in receipt_words:
        if word in text:
            return "Receipt"

    return "Payment"


# -----------------------------
# Transaction Extraction
# -----------------------------

def extract_transactions(doc_dict):

    transactions = []

    tables = doc_dict.get("tables", [])

    for table in tables:

        cells = table.get("data", {}).get("table_cells", [])

        grouped_rows = {}

        for cell in cells:
            row_index = cell.get("start_row_offset_idx")

            grouped_rows.setdefault(row_index, []).append(cell)

        for _, row_cells in sorted(grouped_rows.items()):

            row_text = " ".join(
                c.get("text", "") for c in row_cells
            )

            date_match = re.search(
                r"\d{2}-\d{2}-\d{2,4}",
                row_text
            )

            if not date_match:
                continue

            amounts = re.findall(
                r"\d{1,3}(?:,\d{3})*(?:\.\d{2})|\d+\.\d{2}",
                row_text
            )

            if len(amounts) < 2:
                continue

            date = convert_date(date_match.group(0))

            amount = clean_amount(amounts[-2])

            closing_balance = clean_amount(amounts[-1])

            narration = row_text

            narration = narration.replace(
                date_match.group(0),
                ""
            )

            for amt in amounts[-2:]:
                narration = narration.replace(amt, "")

            narration = re.sub(
                r"\s+",
                " ",
                narration
            ).strip()

            transactions.append({
                "date": date,
                "description": narration[:80],
                "raw_narration": narration,
                "cheque_reference": "",
                "voucher_type": detect_voucher_type(narration),
                "amount": amount,
                "closing_balance": closing_balance
            })

    return {
        "success": True,
        "total_transactions": len(transactions),
        "transactions": transactions
    }


# -----------------------------
# Main Convert Function
# -----------------------------

@spaces.GPU
def convert_document(file, output_format):

    if file is None:
        return "No file uploaded", {}

    try:

        pdf_opts = PdfPipelineOptions(
            do_ocr=True,
            ocr_options=TesseractCliOcrOptions(
                lang=["eng"]
            )
        )

        converter = DocumentConverter(
            format_options={
                InputFormat.PDF:
                PdfFormatOption(
                    pipeline_options=pdf_opts
                )
            }
        )

        result = converter.convert(file.name)

        doc = result.document

        # Markdown
        if output_format == "Markdown":

            converted_text = doc.export_to_markdown()

        # Raw JSON
        elif output_format == "JSON":

            converted_text = json.dumps(
                doc.export_to_dict(),
                indent=2,
                ensure_ascii=False
            )

        # Clean Transaction JSON
        elif output_format == "Bank Transaction JSON":

            tx_json = extract_transactions(
                doc.export_to_dict()
            )

            converted_text = json.dumps(
                tx_json,
                indent=2,
                ensure_ascii=False
            )

        else:
            converted_text = "Unsupported format"

        metadata = {
            "status": "success",
            "pages": len(
                getattr(doc, "pages", [])
            ),
            "output_format": output_format
        }

        return converted_text, metadata

    except Exception as e:

        return (
            f"ERROR: {str(e)}",
            {
                "status": "error"
            }
        )


# -----------------------------
# UI
# -----------------------------

with gr.Blocks() as app:

    gr.Markdown(
        "# 📄 Docling OCR + Bank Statement Extractor"
    )

    gr.Markdown(
        "Upload PDF and extract Markdown, JSON, or clean bank transactions."
    )

    with gr.Row():

        file_input = gr.File(
            label="Upload PDF",
            file_types=[".pdf"]
        )

        format_input = gr.Radio(
            [
                "Markdown",
                "JSON",
                "Bank Transaction JSON"
            ],
            value="Bank Transaction JSON",
            label="Choose Output Format"
        )

    output_text = gr.Textbox(
        label="Converted Output",
        lines=25
    )

    output_metadata = gr.JSON(
        label="Metadata"
    )

    convert_button = gr.Button(
        "Convert"
    )

    convert_button.click(
        fn=convert_document,
        inputs=[
            file_input,
            format_input
        ],
        outputs=[
            output_text,
            output_metadata
        ],
        api_name="/convert_document"
    )

app.launch(debug=True)
```