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import re
import time
import torch
from transformers import DonutProcessor, VisionEncoderDecoderModel
from config import settings
from functools import lru_cache
import os
import requests
@lru_cache(maxsize=1)
def load_model(model_url: str):
"""
Load the processor and model dynamically based on the model URL.
:param model_url: The URL for the model to use.
:return: The processor, model, and device.
"""
# Assuming the model URL follows a pattern like "https://huggingface.co/{model_name}"
model_name = model_url.split("/")[-1] # Extract model name from the URL
processor = DonutProcessor.from_pretrained(model_name)
model = VisionEncoderDecoderModel.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
return processor, model, device
def process_document_donut(image, model_url: str):
"""
Process the document using the DONUT model.
:param image: The input image to process.
:param model_url: The model URL to use for inference.
:return: A tuple of the result and processing time.
"""
worker_pid = os.getpid()
print(f"Handling inference request with worker PID: {worker_pid}")
start_time = time.time()
# Load the model dynamically based on the model_url
processor, model, device = load_model(model_url)
# Prepare encoder inputs
pixel_values = processor(image, return_tensors="pt").pixel_values
# Prepare decoder inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
# Generate answer
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,
)
# Postprocess the result
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
end_time = time.time()
processing_time = end_time - start_time
print(f"Inference done, worker PID: {worker_pid}")
return processor.token2json(sequence), processing_time |