app.py

import spaces
import torch
import re
import gradio as gr
from threading import Thread
from transformers import AutoTokenizer, AutoModelForCausalLM
#from PIL import ImageDraw
#from torchvision.transforms.v2 import Resize
#import subprocess

#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

#subprocess.run('cp -r moondream/torch clients/python/moondream/torch')
#subprocess.run('pip install moondream[gpu]')

#def load_moondream():
#    """Load Moondream model and tokenizer."""
#    model = AutoModelForCausalLM.from_pretrained(
#        "vikhyatk/moondream2", trust_remote_code=True, device_map={"": "cuda"}
#    )
#    tokenizer = AutoTokenizer.from_pretrained("vikhyatk/moondream2")
#    return model, tokenizer

#"""Load Moondream model and tokenizer."""
#moondream = AutoModelForCausalLM.from_pretrained(
#    "vikhyatk/moondream2", trust_remote_code=True, device_map={"": "cuda"}
#)
#tokenizer = AutoTokenizer.from_pretrained("vikhyatk/moondream2")

#model_id = "vikhyatk/moondream2"
#revision = "2025-01-09"
#tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
#moondream = AutoModelForCausalLM.from_pretrained(
#    model_id, trust_remote_code=True, revision=revision,
#    torch_dtype=torch.bfloat16, device_map={"": "cuda"},
#)

#moondream.eval()

moondream = AutoModelForCausalLM.from_pretrained(
    "vikhyatk/moondream2",
    revision="2025-06-21",
    trust_remote_code=True,
    device_map={"": "cuda"}  # ...or 'mps', on Apple Silicon
)

@spaces.GPU(durtion="150")
def answer_questions(image_tuples, prompt_text):
    result = ""
    Q_and_A = ""
    prompts = [p.strip() for p in prompt_text.split('?')]
    image_embeds = [img[0] for img in image_tuples if img[0] is not None]
    answers = []

    for prompt in prompts:
        answers.append(moondream.batch_answer(
            images=[img.convert("RGB") for img in image_embeds],
            prompts=[prompt] * len(image_embeds),
            tokenizer=tokenizer
        ))

    for i, prompt in enumerate(prompts):
        Q_and_A += f"### Q: {prompt}\n"
        for j, image_tuple in enumerate(image_tuples):
            image_name = f"image{j+1}"
            answer_text = answers[i][j]
            Q_and_A += f"**{image_name} A:** \n {answer_text} \n"

    result = {'headers': prompts, 'data': answers}
    #print("result\n{}\n\nQ_and_A\n{}\n\n".format(result, Q_and_A))
    return Q_and_A, result

with gr.Blocks() as demo:
    gr.Markdown("# moondream2 unofficial batch processing demo")
    gr.Markdown("1. Select images\n2. Enter one or more prompts separated by commas. Ex: Describe this image, What is in this image?\n\n")
    gr.Markdown("**Currently each image will be sent as a batch with the prompts thus asking each prompt on each image**")
    gr.Markdown("*Running on free CPU space tier currently so results may take a bit to process compared to duplicating space and using GPU space hardware*")
    gr.Markdown("A tiny vision language model. [moondream2](https://huggingface.co/vikhyatk/moondream2)")
    with gr.Row():
        img = gr.Gallery(label="Upload Images", type="pil", preview=True, columns=4)
    with gr.Row():
        prompt = gr.Textbox(label="Input Prompts", placeholder="Enter prompts (one prompt for each image provided) separated by question marks. Ex: Describe this image? What is in this image?", lines=8)
    with gr.Row():
        submit = gr.Button("Submit")
    with gr.Row():
        output = gr.Markdown(label="Questions and Answers", line_breaks=True)
    with gr.Row():
        output2 = gr.Dataframe(label="Structured Dataframe", type="array", wrap=True)
    submit.click(answer_questions, inputs=[img, prompt], outputs=[output, output2])

demo.queue().launch()