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import spaces |
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import torch |
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import re |
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import gradio as gr |
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from threading import Thread |
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from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM |
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import subprocess |
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) |
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model_id = "vikhyatk/moondream2" |
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revision = "2024-04-02" |
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tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision) |
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moondream = AutoModelForCausalLM.from_pretrained( |
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model_id, trust_remote_code=True, revision=revision, |
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torch_dtype=torch.bfloat16, device_map={"": "cuda"}, |
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attn_implementation="flash_attention_2" |
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) |
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moondream.eval() |
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@spaces.GPU(duration=10) |
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def answer_question(img, prompt): |
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image_embeds = moondream.encode_image(img) |
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streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True) |
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thread = Thread( |
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target=moondream.answer_question, |
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kwargs={ |
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"image_embeds": image_embeds, |
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"question": prompt, |
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"tokenizer": tokenizer, |
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"streamer": streamer, |
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}, |
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) |
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thread.start() |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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yield buffer.strip() |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# 🌔 moondream2 |
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A tiny vision language model. [GitHub](https://github.com/vikhyat/moondream) |
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""" |
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) |
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with gr.Row(): |
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prompt = gr.Textbox(label="Input", value="Describe this image.", scale=4) |
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submit = gr.Button("Submit") |
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with gr.Row(): |
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img = gr.Image(type="pil", label="Upload an Image") |
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output = gr.TextArea(label="Response") |
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submit.click(answer_question, [img, prompt], output) |
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prompt.submit(answer_question, [img, prompt], output) |
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demo.queue().launch() |
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