Spaces:
Build error
Build error
File size: 1,561 Bytes
daf0288 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
from typing import List, Tuple, Set, Dict
from huggingface_hub import hf_hub_download
import re
from PIL import Image
from transformers import NougatProcessor, VisionEncoderDecoderModel
from datasets import load_dataset
import torch
from doctrfiles import DetectionResult
# Numpy image type
import numpy.typing as npt
from numpy import uint8
ImageType = npt.NDArray[uint8]
def run_nougat(inputs: List[Tuple[int, ImageType]])-> List[DetectionResult]:
processor = NougatProcessor.from_pretrained("facebook/nougat-base")
model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
detection_results =[]
for index, np_img in inputs:
image = Image.fromarray(np_img)
pixel_values = processor(image, return_tensors="pt").pixel_values
# generate transcription (here we only generate 30 tokens)
outputs = model.generate(
pixel_values.to(device),
min_length=1,
max_new_tokens=30,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
)
sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
sequence = processor.post_process_generation(sequence, fix_markdown=False)
# note: we're using repr here such for the sake of printing the \n characters, feel free to just print the sequence
text = sequence
detection_results.append(DetectionResult(score=1, text=text, index=index))
return detection_results
|