Update app.py

app.py

@@ -1,8 +1,47 @@
-import streamlit as st
+import re
+
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+from datasets import load_dataset
+import torch
 from PIL import Image
+import numpy as np
+import streamlit as st
+
+processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
+model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+#image = Image.open(r"C:\Invoices\Sample Invoices\sample invoice 1.tif")
+#image = image.convert("RGB")
+#print(np.array(image).shape)
+
+
+# load document image
+dataset = load_dataset("hf-internal-testing/example-documents", split="test")
+image = dataset[2]["image"]
+
+
+task_prompt = "<s_rvlcdip>"
+decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+pixel_values = processor(image, return_tensors="pt").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
+print(processor.token2json(sequence))
 
-# Load model directly
-from transformers import AutoTokenizer, AutoModel
 
-tokenizer = AutoTokenizer.from_pretrained("jinhybr/OCR-Donut-CORD")
-model = AutoModel.from_pretrained("jinhybr/OCR-Donut-CORD")