| 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() | |