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import os
import re
import gradio as gr
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
import traceback
# 1) Load pretrained Donut model and processor
MODEL_NAME = "naver-clova-ix/donut-base-finetuned-cord-v2"
processor = DonutProcessor.from_pretrained(MODEL_NAME)
model = VisionEncoderDecoderModel.from_pretrained(MODEL_NAME)
# 2) Set device and move model
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# 3) Inference function with debugging
def ocr_donut(image):
try:
if image is None:
return {"error": "No image provided."}
# Prepare prompt and inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(
task_prompt,
add_special_tokens=False,
return_tensors="pt"
).input_ids.to(device)
# Convert to tensor
pixel_values = processor(image.convert("RGB"), return_tensors="pt").pixel_values.to(device)
# Generate outputs
outputs = model.generate(
pixel_values,
decoder_input_ids=decoder_input_ids,
max_length=model.config.decoder.max_position_embeddings,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
# Decode and clean up
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
json_output = processor.token2json(sequence)
return {"result": json_output}
except Exception:
tb = traceback.format_exc()
print(tb)
return {"error": tb}
# 4) Build Gradio interface
demo = gr.Interface(
fn=ocr_donut,
inputs=gr.Image(type="pil", label="Upload Document Image"),
outputs=gr.JSON(label="Output"),
title="Donut OCR Gradio App",
description="Upload a document image and get structured JSON output. Errors will be shown for debugging."
)
# 5) Launch for Spaces
demo.launch(
server_name="0.0.0.0",
server_port=int(os.environ.get("PORT", 7860)),
debug=True
)
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