import argparse import torch import re import gradio as gr from threading import Thread from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM parser = argparse.ArgumentParser() if torch.cuda.is_available(): device, dtype = "cuda", torch.float16 else: device, dtype = "cpu", torch.float32 model_id = "vikhyatk/moondream2" tokenizer = AutoTokenizer.from_pretrained(model_id, revision="2024-03-04") moondream = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, revision="2024-03-04" ).to(device=device, dtype=dtype) moondream.eval() def answer_question(img, prompt): image_embeds = moondream.encode_image(img) streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True) thread = Thread( target=moondream.answer_question, kwargs={ "image_embeds": image_embeds, "question": prompt, "tokenizer": tokenizer, "streamer": streamer, }, ) thread.start() buffer = "" for new_text in streamer: clean_text = re.sub("<$|<END$", "", new_text) buffer += clean_text yield buffer with gr.Blocks() as demo: gr.Markdown( """ # 🌔 moondream2 A tiny vision language model. [GitHub](https://github.com/vikhyat/moondream) """ ) with gr.Row(): prompt = gr.Textbox(label="Input", placeholder="Type here...", scale=4) submit = gr.Button("Submit") with gr.Row(): img = gr.Image(type="pil", label="Upload an Image") output = gr.TextArea(label="Response") submit.click(answer_question, [img, prompt], output) prompt.submit(answer_question, [img, prompt], output) demo.queue().launch()