Add application file

app.py

@@ -0,0 +1,57 @@
+import re
+
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import gradio as gr
+import torch
+from PIL import Image
+
+print("test1")
+processor = DonutProcessor.from_pretrained("ewfian/donut_cn_invoice")
+print("test2")
+model = VisionEncoderDecoderModel.from_pretrained("ewfian/donut_cn_invoice")
+print("test3")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+task_prompt = "<s_totalAmountInWords>"
+decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+print("test")
+print(decoder_input_ids.shape)
+
+def process_document(image):
+    print("test2")
+
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+
+    print(pixel_values.shape)
+    print(pixel_values)
+
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.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,
+    )
+
+    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()  # remove first task start token
+    return processor.token2json(sequence)
+
+# t = process_document(test_sample)
+# print(t)
+
+demo = gr.Interface(
+    fn=process_document,
+    inputs="image",
+    outputs="json",
+    title="Demo: Donut 🍩 for Invioce Parsing",
+    cache_examples=False)
+
+demo.launch(server_name="0.0.0.0")