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from fastapi import FastAPI, UploadFile, File
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import torch
import io
import os
from typing import Union
# Patch to remove flash-attn dependency
from transformers.dynamic_module_utils import get_imports
def fixed_get_imports(filename: Union[str, os.PathLike]) -> list[str]:
"""Work around for flash-attn imports."""
if not str(filename).endswith("/modeling_florence2.py"):
return get_imports(filename)
imports = get_imports(filename)
if "flash_attn" in imports:
imports.remove("flash_attn")
return imports
device = "cuda" if torch.cuda.is_available() else "cpu"
# Apply the patch
from unittest.mock import patch
with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
model = AutoModelForCausalLM.from_pretrained("numberPlate_model_2", trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("numberPlate_model_2", trust_remote_code=True)
# Initialize FastAPI
app = FastAPI()
def process_image(image, task_token):
inputs = processor(text=task_token, images=image, return_tensors="pt", padding=True).to(device)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
num_beams=3,
do_sample=False
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_result = processor.post_process_generation(generated_text, task=task_token, image_size=(image.width, image.height))
return parsed_result
@app.post("/process-image/")
async def process_image_endpoint(file: UploadFile = File(...), task_token: str = "<OD>"):
image = Image.open(io.BytesIO(await file.read())).convert("RGB")
result = process_image(image, task_token)
return result
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