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import spaces
import torch
import re
import gradio as gr
from threading import Thread
from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
model_id = "vikhyatk/moondream2"
revision = "2024-05-20"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
moondream = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
revision=revision,
torch_dtype=torch.bfloat16,
device_map={"": "cuda"},
attn_implementation="flash_attention_2"
)
moondream.eval()
@spaces.GPU(duration=10)
def answer_question(img, prompt):
if img is None:
raise gr.Error("Please upload an image.")
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:
buffer += new_text
yield buffer.strip()
with gr.Blocks() as demo:
gr.Markdown(
"""
# myAI - AMI Vision Module
A lightweight Computer Vision model by @vikhyat - 🌔 [moondream2](https://github.com/vikhyat/moondream)
"""
)
with gr.Row():
prompt = gr.Textbox(label="Input", value="Identify people in this image", 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() |