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import spaces
import gradio as gr
import fitz # PyMuPDF
import ocrmypdf
import tempfile
import os
def extract_text_markdown(doc):
markdown_output = ""
image_counter = 1
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, f"[imagen_{image_counter}]()"))
image_counter += 1
elements.sort(key=lambda x: x[0])
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()
def needs_ocr(doc):
text_length = sum(len(page.get_text().strip()) for page in doc)
image_count = sum(len(page.get_images(full=True)) for page in doc)
return text_length < 500 or image_count > 0
@spaces.GPU
def convert(pdf_file):
original_doc = fitz.open(pdf_file)
if needs_ocr(original_doc):
try:
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)
os.remove(ocr_temp_path)
except Exception as e:
return f"Error al aplicar OCR: {e}", {}
else:
doc = original_doc
markdown = extract_text_markdown(doc)
metadata = {} # Puedes agregar metadatos aquí si lo necesitas
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()
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