Update

app.py

@@ -19,10 +19,10 @@ def demo_process(input_img):
 
 task_prompt = f"<s_cord-v2>"
 
-image = Image.open("./sample_image_cord_test_receipt_00004.png")
-image.save("cord_sample_receipt1.png")
-image = Image.open("./sample_image_cord_test_receipt_00012.png")
-image.save("cord_sample_receipt2.png")
+# image = Image.open("./sample_image_cord_test_receipt_00004.png")
+# image.save("cord_sample_receipt1.png")
+# image = Image.open("./sample_image_cord_test_receipt_00012.png")
+# image.save("cord_sample_receipt2.png")
 
 pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
 # pretrained_model: DonutModel = DonutModel.from_pretrained("result", local_files_only=True)
@@ -40,7 +40,7 @@ More CORD receipt images are available at https://huggingface.co/datasets/naver-
 More details are available at:
 - Paper: https://arxiv.org/abs/2111.15664
 - GitHub: https://github.com/clovaai/donut""",
-    examples=[["cord_sample_receipt1.png"], ["cord_sample_receipt2.png"]],
+    examples=[["sample_image_cord_test_receipt_00004.png"], ["sample_image_cord_test_receipt_00012.png"]],
     cache_examples=False,
 )
 

receipts_app.py

@@ -0,0 +1,51 @@
+"""
+Donut
+Copyright (c) 2022-present NAVER Corp.
+MIT License
+
+https://github.com/clovaai/donut
+"""
+import gradio as gr
+import torch
+from PIL import Image
+
+from donut import DonutModel
+
+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
+
+# task_prompt = f"<s_cord-v2>"
+task_prompt = f"<s_receipts>"
+
+# image = Image.open("./sample_image_cord_test_receipt_00004.png")
+# image.save("cord_sample_receipt1.png")
+# image = Image.open("./sample_image_cord_test_receipt_00012.png")
+# image.save("cord_sample_receipt2.png")
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
+# pretrained_model: DonutModel = DonutModel.from_pretrained("result", local_files_only=True)
+pretrained_model.to(device)
+pretrained_model.eval()
+
+demo = gr.Interface(
+    fn=demo_process,
+    inputs= gr.inputs.Image(type="pil"),
+    outputs="json",
+    title=f"Donut 🍩 demonstration for `cord-v2` task",
+    description="""This model is trained with 800 Indonesian receipt images of CORD dataset. <br>
+Demonstrations for other types of documents/tasks are available at https://github.com/clovaai/donut <br>
+More CORD receipt images are available at https://huggingface.co/datasets/naver-clova-ix/cord-v2
+
+More details are available at:
+- Paper: https://arxiv.org/abs/2111.15664
+- GitHub: https://github.com/clovaai/donut""",
+    examples=[["sample_image_cord_test_receipt_00004.png"], ["sample_image_cord_test_receipt_00012.png"]],
+    cache_examples=False,
+)
+
+demo.launch(server_name="0.0.0.0")