app.py

import gradio as gr
from transformers import AutoModel, AutoProcessor
from PIL import Image
import torch
import numpy as np

model_name_or_path = "lyttt/VLV_captioner"
model = AutoModel.from_pretrained(model_name_or_path, revision="master", trust_remote_code=True,low_cpu_mem_usage=False)

def greet(image):
    if image.dtype != np.uint8:
        image = (np.clip(image, 0, 1) * 255).astype(np.uint8)
    image = Image.fromarray(image, mode='RGB')
    with torch.no_grad():
        outputs = model([image]).generated_text[0]
    return output

demo = gr.Interface(fn=greet, inputs="image", outputs="text")
demo.launch()