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266a753 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | """Gradio app wrapping the official `commonforms` package to convert PDFs
into fillable forms using jbarrow's FFDNet-L object detector (CPU ONNX).
- Paper: <https://arxiv.org/abs/2509.16506>
- Model: <https://huggingface.co/jbarrow/FFDNet-L-cpu>
- Package: <https://pypi.org/project/commonforms/>
Detecta 3 classes de campos: text boxes, checkboxes (choice buttons) e signatures.
"""
from __future__ import annotations
import os
# Força CPU antes de qualquer import que possa inicializar CUDA.
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
os.environ.setdefault("NVIDIA_VISIBLE_DEVICES", "")
import inspect
import tempfile
from pathlib import Path
import gradio as gr
from commonforms import prepare_form
from huggingface_hub import hf_hub_download
_PARAMS = inspect.signature(prepare_form).parameters
print(f"[commonforms] prepare_form signature: {list(_PARAMS.keys())}")
# Pre-baixa o ONNX uma vez no startup. O `commonforms` usa ultralytics YOLO
# por baixo, que só aceita caminho LOCAL no parâmetro `model_or_path`.
_MODEL_REPO = "jbarrow/FFDNet-L-cpu"
_MODEL_FILE = "FFDNet-L.onnx"
print(f"[commonforms] baixando {_MODEL_REPO}/{_MODEL_FILE}...")
_ONNX_PATH = hf_hub_download(repo_id=_MODEL_REPO, filename=_MODEL_FILE)
print(f"[commonforms] ONNX local: {_ONNX_PATH}")
def detect_fields(
pdf_path: str | None,
image_size: int,
use_signature_fields: bool,
keep_existing_fields: bool,
) -> str:
if not pdf_path:
raise gr.Error("Envie um PDF.")
src = Path(pdf_path)
if not src.exists():
raise gr.Error(f"Arquivo não encontrado: {src}")
_, out_str = tempfile.mkstemp(suffix="_fillable.pdf")
out = Path(out_str)
optional = {
"image_size": int(image_size),
"use_signature_fields": bool(use_signature_fields),
"keep_existing_fields": bool(keep_existing_fields),
"device": "cpu",
"model_or_path": _ONNX_PATH,
}
accepted = {k: v for k, v in optional.items() if k in _PARAMS}
print(f"[commonforms] calling prepare_form with kwargs: {accepted}")
try:
prepare_form(str(src), str(out), **accepted)
except Exception as exc:
raise gr.Error(f"Falha ao processar PDF: {exc}") from exc
return str(out)
with gr.Blocks(title="CommonForms — Form Field Detector") as demo:
gr.Markdown(
"# CommonForms — Form Field Detector\n"
"Converte um PDF em formulário preenchível usando **FFDNet-L** "
"(`jbarrow/FFDNet-L-cpu`, Object Detection ONNX em CPU). "
"Detecta *text boxes*, *checkboxes* e *signature fields*.\n\n"
"Paper: [arxiv 2509.16506](<https://arxiv.org/abs/2509.16506>) · "
"Modelo: [jbarrow/FFDNet-L-cpu](<https://huggingface.co/jbarrow/FFDNet-L-cpu>)"
)
with gr.Row():
with gr.Column():
pdf_in = gr.File(
label="PDF de entrada",
file_types=[".pdf"],
type="filepath",
)
image_size = gr.Slider(
minimum=512,
maximum=2048,
value=1600,
step=32,
label="Image size (px)",
info="Tamanho usado na inferência. Maior = mais preciso, mais lento.",
)
use_sig = gr.Checkbox(
value=False,
label="Incluir signature fields",
info="Detecta áreas de assinatura além de text/checkbox.",
)
keep = gr.Checkbox(
value=False,
label="Manter campos já existentes",
info="Preserva widgets AcroForm que já estavam no PDF.",
)
btn = gr.Button("Detectar campos", variant="primary")
with gr.Column():
pdf_out = gr.File(label="PDF preenchível")
btn.click(
fn=detect_fields,
inputs=[pdf_in, image_size, use_sig, keep],
outputs=pdf_out,
api_name="detect",
)
if __name__ == "__main__":
demo.queue(max_size=4).launch()
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