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 = "lambertxiao/Vision-Language-Vision-Captioner-Qwen2.5-3B"
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True, low_cpu_mem_usage=False)

# @spaces.GPU(duration=120)
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], 300).generated_text[0]
    def drop_incomplete_tail(text):
        sentences = text.split('.')
        complete_sentences = [s.strip() for s in sentences if s.strip()]
        if not text.strip().endswith('.'):
            complete_sentences = complete_sentences[:-1]
        return '. '.join(complete_sentences) + ('.' if complete_sentences else '')
    return drop_incomplete_tail(outputs)

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