donut/single_eval.py

from transformers import DonutProcessor, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
import re
import torch
from PIL import Image
import time
from fastapi import FastAPI, UploadFile, File
import io
import os   

# print("Set up config")
# config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
# config_vision.encoder.image_size = [1920, 2560] # (height, width)
# config_vision.decoder.max_length = 768

# processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
# model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2", config=config_vision)

# processor.image_processor.size = [1920, 2560][::-1] # should be (width, height)
# processor.image_processor.do_align_long_axis = False

# # dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split)
# device = "cuda" if torch.cuda.is_available() else "cpu"
# model.eval()
# model.to(device)

print("Print ipconfig")
os.system("ipconfig")

print("Starting server")
app = FastAPI()

@app.get("/test")
async def test():
    return {"message": "Test"}

# @app.post("/process")
# async def process_image(file: UploadFile= File(...)):

#     request_object_content = await file.read()
#     input_image = Image.open(io.BytesIO(request_object_content))

#     # prepare encoder inputs
#     pixel_values = processor(input_image.convert("RGB"), return_tensors="pt").pixel_values
#     pixel_values = pixel_values.to(device)
#     # prepare decoder inputs
#     task_prompt = "<s_cord-v2>"
#     decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
#     decoder_input_ids = decoder_input_ids.to(device)

#     print("Start processing")
#     # autoregressively generate sequence
#     start_time = time.time()
#     outputs = model.generate(
#             pixel_values,
#             decoder_input_ids=decoder_input_ids,
#             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,
#         )
#     processing_time = (time.time() - start_time)

#     # turn into JSON
#     seq = processor.batch_decode(outputs.sequences)[0]
#     seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
#     seq = re.sub(r"<.*?>", "", seq, count=1).strip()  # remove first task start token
#     seq = processor.token2json(seq)
#     return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"}