app1.py

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.Image("data/redhen.ico")
    gr.Markdown(
        """
        # Super Rapid Annotator - Multimodal vision tool to annotate videos with LLaVA framework
        """
    )
    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()