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| import re | |
| import torch | |
| from fastapi import FastAPI, File, UploadFile | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from transformers import DonutProcessor, VisionEncoderDecoderModel | |
| from PIL import Image | |
| from io import BytesIO | |
| origins = [ | |
| "https://duithive.vercel.app", | |
| "https://staging-duithive.vercel.app", | |
| "http://localhost:3000", | |
| ] | |
| app = FastAPI() | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=origins, | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2", use_fast=False) | |
| model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| task_prompt = "<s_cord-v2>" | |
| decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids | |
| # def generateOutput(fileData): | |
| # pil_image = Image.open(BytesIO(fileData)) | |
| # resized_image = pil_image.resize((800, 600)).convert('RGB') | |
| # rgb_image = Image.new('RGB', resized_image.size) | |
| # rgb_image.paste(resized_image) | |
| # output_buffer = BytesIO() | |
| # rgb_image.save(output_buffer, format="JPEG", quality = 100) | |
| # jpeg_image = Image.open(BytesIO(output_buffer.getvalue())) | |
| # pixel_values = processor(jpeg_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, | |
| # pad_token_id=processor.tokenizer.pad_token_id, | |
| # eos_token_id=processor.tokenizer.eos_token_id, | |
| # use_cache=True, | |
| # bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| # return_dict_in_generate=True, | |
| # ) | |
| # return outputs | |
| def generateOutput(fileData): | |
| pil_image = Image.open(BytesIO(fileData)) | |
| pil_image.resize((800, 600)) | |
| pixel_values = processor(pil_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, | |
| pad_token_id=processor.tokenizer.pad_token_id, | |
| eos_token_id=processor.tokenizer.eos_token_id, | |
| use_cache=True, | |
| bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True, | |
| ) | |
| return outputs | |
| async def analyze_image(file: UploadFile = File(...)): | |
| content = await file.read() | |
| outputs = generateOutput(content) | |
| 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) | |