initial commit

app.py

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+"""
+Donut
+Copyright (c) 2022-present NAVER Corp.
+MIT License
+
+https://github.com/clovaai/donut
+"""
+import argparse
+
+import gradio as gr
+import torch
+from PIL import Image
+
+from donut import DonutModel
+
+
+def demo_process_vqa(input_img, question):
+    global pretrained_model, task_prompt, task_name
+    input_img = Image.fromarray(input_img)
+    user_prompt = task_prompt.replace("{user_input}", question)
+    output = pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0]
+    return output
+
+
+def demo_process(input_img):
+    global pretrained_model, task_prompt, task_name
+    input_img = Image.fromarray(input_img)
+    output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0]
+    return output
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task", type=str, default="cord-v2")
+    parser.add_argument("--pretrained_path", type=str, default="naver-clova-ix/donut-base-finetuned-cord-v2")
+    parser.add_argument("--port", type=int, default=None)
+    parser.add_argument("--url", type=str, default=None)
+    parser.add_argument("--sample_img_path", type=str)
+    args, left_argv = parser.parse_known_args()
+
+    task_name = args.task
+    if "docvqa" == task_name:
+        task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
+    else:  # rvlcdip, cord, ...
+        task_prompt = f"<s_{task_name}>"
+
+    example_sample = []
+    if args.sample_img_path:
+        example_sample.append(args.sample_img_path)
+
+    pretrained_model = DonutModel.from_pretrained(args.pretrained_path)
+
+    if torch.cuda.is_available():
+        pretrained_model.half()
+        device = torch.device("cuda")
+        pretrained_model.to(device)
+    else:
+        pretrained_model.encoder.to(torch.bfloat16)
+
+    pretrained_model.eval()
+
+    demo = gr.Interface(
+        fn=demo_process_vqa if task_name == "docvqa" else demo_process,
+        inputs=["image", "text"] if task_name == "docvqa" else "image",
+        outputs="json",
+        title=f"Donut 🍩 demonstration for `{task_name}` task",
+        examples=[example_sample] if example_sample else None,
+    )
+    demo.launch(server_name=args.url, server_port=args.port)

requirements.txt

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+donut-python
+gradio