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# Importing necessary libraries
import spaces
from transformers import AutoProcessor, AutoModelForCausalLM
# Load model and processor from Hugging Face
model_id = "microsoft/Florence-2-large-ft"
model = (
AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval().cuda()
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
@spaces.GPU(duration=120)
def run_example(task_prompt, image, text_input=None):
"""
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.
"""
# If there is no text input, use the task prompt as the prompt
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
# Process the image and text input
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Generate the answer using the model
generated_ids = model.generate(
input_ids=inputs["input_ids"].cuda(),
pixel_values=inputs["pixel_values"].cuda(),
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
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