feat: initial version

Signed-off-by: ivelin <ivelin.eth@gmail.com>

app.py

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+import re
+import gradio as gr
+
+import torch
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+
+pretrained_repo_name = "ivelin/donut-refexp-draft"
+
+processor = DonutProcessor.from_pretrained(pretrained_repo_name)
+model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+
+def process_document(image, question):
+    # prepare encoder inputs
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+
+    # prepare decoder inputs
+    task_prompt = "<s_refexp><s_prompt>{user_input}</s_prompt><s_refexp>"
+    prompt = task_prompt.replace("{user_input}", question)
+    decoder_input_ids = processor.tokenizer(
+        prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+    # generate answer
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.max_position_embeddings,
+        early_stopping=True,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        num_beams=1,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+
+    # postprocess
+    sequence = processor.batch_decode(outputs.sequences)[0]
+    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(
+        processor.tokenizer.pad_token, "")
+    # remove first task start token
+    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
+
+    return processor.token2json(sequence)
+
+
+description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on UIBert RefExp Dataset (UI Referring Expression). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>"
+
+demo = gr.Interface(
+    fn=process_document,
+    inputs=["image", "text"],
+    outputs="json",
+    title="Demo: Donut 🍩 for DocVQA",
+    description=description,
+    article=article,
+    enable_queue=True,
+    examples=[["example_1.png", "When is the coffee break?"], [
+        "example_2.jpeg", "What's the population of Stoddard?"]],
+    cache_examples=False)
+
+demo.launch()

packages.txt

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+# debian dependencies
+# https://huggingface.co/docs/hub/spaces-dependencies

requirements.txt

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+Pillow