create main.py

main.py

@@ -0,0 +1,45 @@
+import re
+import torch
+from fastapi import FastAPI, File, UploadFile
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+from PIL import Image
+from io import BytesIO
+
+app = FastAPI()
+
+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))
+    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
+
+@app.post("/ocr/")
+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)
+