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import gradio as gr
import torch
import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
def load_and_preprocess_image(image, processor):
"""
Load an image and preprocess it for the model.
"""
pixel_values = processor(image, return_tensors="pt").pixel_values
return pixel_values
def generate_text_from_image(model, image, processor, device):
"""
Generate text from an image using the trained model.
"""
# Load and preprocess the image
pixel_values = load_and_preprocess_image(image, processor)
pixel_values = pixel_values.to(device)
# Generate output using model
model.eval()
with torch.no_grad():
task_prompt = "<s_receipt>" # <s_cord-v2> for v1
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
decoder_input_ids = decoder_input_ids.to(device)
generated_outputs = model.generate(
pixel_values,
decoder_input_ids=decoder_input_ids,
max_length=model.decoder.config.max_position_embeddings,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
early_stopping=True,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True
)
# Decode generated output
decoded_text = processor.batch_decode(generated_outputs.sequences)[0]
decoded_text = decoded_text.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
decoded_text = re.sub(r"<.*?>", "", decoded_text, count=1).strip() # remove first task start token
decoded_text = processor.token2json(decoded_text)
return decoded_text
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
processor = DonutProcessor.from_pretrained("AdamCodd/donut-receipts-extract")
model = VisionEncoderDecoderModel.from_pretrained("AdamCodd/donut-receipts-extract")
model.to(device)
def process_image(image):
extracted_text = generate_text_from_image(model, image, processor, device)
print("Extracted Text:", extracted_text)
return extracted_text
image = gr.Image(type='pil')
label = gr.JSON()
intf = gr.Interface(fn=process_image, inputs=image, outputs=label)
intf.launch(inline=False)
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