modules/llm_captioner.py

import torch
from PIL import Image
import numpy as np
from transformers import AutoProcessor, AutoModelForCausalLM

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model = None
processor = None

def _load_captioning_model():
    """Load the Florence-2"""
    global model, processor
    if model is None or processor is None:
        print("Loading Florence-2 model for image captioning...")
        model = AutoModelForCausalLM.from_pretrained(
            "microsoft/Florence-2-large", 
            torch_dtype=torch_dtype, 
            trust_remote_code=True
        ).to(device)

        processor = AutoProcessor.from_pretrained(
            "microsoft/Florence-2-large", 
            trust_remote_code=True
        )
        print("Florence-2 model loaded successfully.")

def unload_captioning_model():
    """Unload the Florence-2"""
    global model, processor
    if model is not None:
        del model
        model = None
    if processor is not None:
        del processor
        processor = None
    torch.cuda.empty_cache()
    print("Florence-2 model unloaded successfully.")

prompt = "<MORE_DETAILED_CAPTION>"

# The image parameter now directly accepts a PIL Image object
def caption_image(image: np.array):
    """
    Args:
        image_np (np.ndarray): The input image as a NumPy array (e.g., HxWx3, RGB).
                                Gradio passes this when type="numpy" is set.
    """

    _load_captioning_model()

    image_pil = Image.fromarray(image)

    inputs = processor(text=prompt, images=image_pil, return_tensors="pt").to(device, torch_dtype)

    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=True)[0]

    return generated_text