import spaces import torch import re import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM from PIL import Image if torch.cuda.is_available(): device, dtype = "cuda", torch.float16 else: device, dtype = "cpu", torch.float32 model_id = "vikhyatk/moondream2" revision = "2024-04-02" tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision) moondream = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, revision=revision, torch_dtype=dtype ).to(device=device) moondream.eval() @spaces.GPU(duration=10) def answer_questions(image_tuples, prompt_text): result = "" print(f"prompt_text: {prompt_text}\n") prompts = [p.strip() for p in prompt_text.split(',')] # Splitting and cleaning prompts print(f"prompts: {prompts}\n") image_embeds = [img[0] for img in image_tuples if img[0] is not None] # Extracting images from tuples, ignoring None # Check if the lengths of image_embeds and prompts are equal if len(image_embeds) != len(prompts): return ("Error: The number of images input and prompts input (seperate by commas in input text field) must be the same.") answers = moondream.batch_answer( images=image_embeds, prompts=prompts, tokenizer=tokenizer, ) for question, answer in zip(prompts, answers): print(f"Q: {question}") print(f"A: {answer}") print() result += (f"Q: {question}\nA: {answer}\n\n") return result with gr.Blocks() as demo: gr.Markdown("# moondream2 unofficial batch processing demo") gr.Markdown("# 🌔 moondream2\nA tiny vision language model. [GitHub](https://github.com/vikhyatk/moondream)") with gr.Row(): img = gr.Gallery(label="Upload Images", type="pil") prompt = gr.Textbox(label="Input Prompts", placeholder="Enter prompts (one prompt for each image provided) separated by commas. Ex: Describe this image, What is in this image?", lines=2) submit = gr.Button("Submit") output = gr.TextArea(label="Responses", lines=4) submit.click(answer_questions, [img, prompt], output) demo.queue().launch()