|
import spaces |
|
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="2024-03-06") |
|
moondream = AutoModelForCausalLM.from_pretrained( |
|
model_id, trust_remote_code=True, revision="2024-03-06" |
|
).to(device=device, dtype=dtype) |
|
moondream.eval() |
|
|
|
|
|
@spaces.GPU(duration=10) |
|
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() |
|
|
