to deploy

app.py

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+import re
+import transformers
+from PIL import Image
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import torch
+import random
+import numpy as np
+import gradio as gr
+
+transformers.logging.disable_default_handler()
+processor = DonutProcessor.from_pretrained("daquarti/donut-base-sroie")
+model = VisionEncoderDecoderModel.from_pretrained("daquarti/donut-base-sroie")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+def load_image (f):
+  with Image.open(f) as img:
+    a = img.load()
+    return img.convert('RGB')
+
+def pred (a):
+  #imagen_path = imagen
+  #a = load_image (imagen_path)
+  pixel_values = processor(a, return_tensors="pt").pixel_values
+  task_prompt = "<s>"
+  decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+  outputs = model.generate(
+          pixel_values.to(device),
+          decoder_input_ids=decoder_input_ids.to(device),
+          max_length=model.decoder.config.max_position_embeddings,
+          early_stopping=True,
+          pad_token_id=processor.tokenizer.pad_token_id,
+          eos_token_id=processor.tokenizer.eos_token_id,
+          use_cache=True,
+          num_beams=1,
+          bad_words_ids=[[processor.tokenizer.unk_token_id]],
+          return_dict_in_generate=True,
+      )
+  prediction = processor.batch_decode(outputs.sequences)[0]
+  prediction = processor.token2json(prediction)
+  return str (prediction)
+
+examples = ['1.jpg', '2.jpg']
+demo = gr.Interface(fn=pred, inputs="image", outputs= "text", examples= examples)
+
+
+demo.launch(share=True)   

requirements.txt

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+transformers @ git+https://github.com/huggingface/transformers.git@9ccea7acb1a75dc18d47906dc9baed883ccfeb19
+datasets==2.6.1