app.py

import spaces
import gradio as gr
import fitz  # PyMuPDF
import ocrmypdf
import tempfile
import os

def extract_text_markdown(doc):
    markdown_output = ""

    for page in doc:
        blocks = page.get_text("dict")["blocks"]
        elements = []

        for b in blocks:
            y = b["bbox"][1]
            if b["type"] == 0:  # Texto
                for line in b["lines"]:
                    line_y = line["bbox"][1]
                    line_text = " ".join([span["text"] for span in line["spans"]]).strip()
                    if line_text:
                        elements.append((line_y, line_text))
            elif b["type"] == 1:  # Imagen
                elements.append((y, "[imagen]()"))  # Enlace vacío

        # Ordenar por posición vertical
        elements.sort(key=lambda x: x[0])

        # Reconstruir con saltos
        previous_y = None
        for y, content in elements:
            if previous_y is not None and abs(y - previous_y) > 10:
                markdown_output += "\n"
            markdown_output += content + "\n"
            previous_y = y

        markdown_output += "\n---\n\n"

    return markdown_output.strip()

@spaces.GPU
def convert(pdf_file):
    original_doc = fitz.open(pdf_file)
    plain_text = "\n".join([page.get_text() for page in original_doc])

    # Aplicar OCR solo si el PDF no tiene texto
    if len(plain_text.strip()) < 100:
        ocr_temp_path = tempfile.NamedTemporaryFile(suffix=".pdf", delete=False).name
        ocrmypdf.ocr(pdf_file, ocr_temp_path, force_ocr=True)
        doc = fitz.open(ocr_temp_path)
    else:
        doc = original_doc

    markdown = extract_text_markdown(doc)
    metadata = {}  # Si necesitas metadatos, se pueden agregar aquí
    return markdown, metadata

gr.Interface(
    fn=convert,
    inputs=[gr.File(label="Sube tu PDF", type="filepath")],
    outputs=[gr.Text(label="Markdown estructurado"), gr.JSON(label="Metadata")],
).launch()