File size: 2,621 Bytes
c45cf35 ab3537a c45cf35 289ac79 c45cf35 |
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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
# Importing necessary libraries
import sys
import subprocess
from typing import Optional
from PIL import Image
import spaces
from transformers import AutoProcessor, AutoModelForCausalLM
# Local imports
from src.logger import logging
from src.exception import CustomExceptionHandling
# Install the required dependencies
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
# Load model and processor from Hugging Face
model_id = "microsoft/Florence-2-large-ft"
try:
model = (
AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
.to("cuda")
.eval()
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
logging.info("Model and processor loaded successfully.")
# Handle exceptions that may occur during the process
except Exception as e:
# Custom exception handling
raise CustomExceptionHandling(e, sys) from e
@spaces.GPU(duration=120)
def run_example(
task_prompt: str, image: Image.Image, text_input: Optional[str] = None
) -> str:
"""
Runs an example using the given task prompt and image.
Args:
- task_prompt (str): The task prompt for the example.
- image (PIL.Image.Image): The image to be processed.
- text_input (str, optional): Additional text input to be appended to the task prompt. Defaults to None.
Returns:
str: The parsed answer generated by the model.
"""
try:
# If there is no text input, use the task prompt as the prompt
prompt = task_prompt if text_input is None else task_prompt + text_input
# Process the image and text input
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
# Generate the answer using the model
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(
generated_ids, skip_special_tokens=False
)[0]
parsed_answer = processor.post_process_generation(
generated_text, task=task_prompt, image_size=(image.width, image.height)
)
# Return the parsed answer
return parsed_answer
# Handle exceptions that may occur during the process
except Exception as e:
# Custom exception handling
raise CustomExceptionHandling(e, sys) from e
|