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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() | |