Add application file

app.py

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+import os
+import gradio as gr
+import re
+import torch
+import cv2
+import numpy as np
+
+from PIL import Image
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+
+title = "OCR using Donut"
+description = """
+This demo application uses `naver-clova-ix/donut-base` model to extract text from images.
+"""
+article = "Check out [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) documentation that this demo is based off of."
+
+checkpoint = "naver-clova-ix/donut-base"
+
+processor = DonutProcessor.from_pretrained(checkpoint)
+model = VisionEncoderDecoderModel.from_pretrained(checkpoint)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# prepare decoder inputs
+task_prompt = "<s_synthdog>"
+decoder_input_ids = processor.tokenizer(
+    task_prompt, add_special_tokens=False, return_tensors="pt"
+).input_ids
+
+
+def convert_image_GRAY2BGR(image):
+    if len(np.asarray(image).shape) != 3:
+        image = cv2.cvtColor(np.array(image), cv2.COLOR_GRAY2BGR)
+        image = Image.fromarray(np.uint8(image))
+    return image
+
+
+def predict(image):
+    image = convert_image_GRAY2BGR(image)
+    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,
+        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,
+    )
+
+    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)["text_sequence"]
+
+
+# We instantiate the Textbox class
+input_textbox = gr.Textbox(
+    label="Type your prompt here:", placeholder="John Doe", lines=2
+)
+
+gr.Interface(
+    fn=predict,
+    inputs="image",
+    outputs="text",
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+        os.path.join(os.path.dirname(__file__), "../data/sample/sample-1.png"),
+        os.path.join(os.path.dirname(__file__), "../data/sample/lorem_ipsum.png"),
+    ],
+).launch()