Create app.py

app.py

@@ -0,0 +1,41 @@
+import argparse
+import gradio as gr
+import torch
+from PIL import Image
+
+from donut import DonutModel
+import argparse
+import gradio as gr
+import torch
+from PIL import Image
+
+from donut import DonutModel
+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")
+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}>"
+
+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",
+)
+demo.launch(debug=True)