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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 transformers import AutoTokenizer, AutoModelForCausalLM |
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from PIL import Image |
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if torch.cuda.is_available(): |
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device, dtype = "cuda", torch.float16 |
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else: |
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device, dtype = "cpu", torch.float32 |
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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, torch_dtype=dtype |
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).to(device=device) |
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moondream.eval() |
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@spaces.GPU |
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def answer_questions(image_tuples, prompt_text): |
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result = "" |
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Q_and_A = "" |
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prompts = [p.strip() for p in prompt_text.split(',')] |
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image_embeds = [img[0] for img in image_tuples if img[0] is not None] |
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answers = [] |
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for prompt in prompts: |
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image_answers = moondream.batch_answer( |
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images=[img.convert("RGB") for img in image_embeds], |
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prompts=[prompt] * len(image_embeds), |
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tokenizer=tokenizer, |
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) |
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answers.append(image_answers) |
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for i, prompt in enumerate(prompts): |
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Q_and_A += f"### Q: {prompt}\n" |
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for j, image_tuple in enumerate(image_tuples): |
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image_name = f"image{j+1}" |
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answer_text = answers[i][j] |
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Q_and_A += f"**{image_name} A:** \n {answer_text} \n\n" |
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result = {'headers': prompts, 'data': answers} |
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return Q_and_A, result |
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with gr.Blocks() as demo: |
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gr.Markdown("# moondream2 unofficial batch processing demo") |
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gr.Markdown("1. Select images\n2. Enter one or more prompts separated by commas. Ex: Describe this image, What is in this image?\n\n") |
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gr.Markdown("**Currently each image will be sent as a batch with the prompts thus asking each promp on each image**") |
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gr.Markdown("*Running on free CPU space tier currently so results may take a bit to process compared to duplicating space and using GPU space hardware*") |
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gr.Markdown("## 🌔 moondream2\nA tiny vision language model. [GitHub](https://github.com/vikhyatk/moondream)") |
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with gr.Row(): |
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img = gr.Gallery(label="Upload Images", type="pil", preview=True, columns=4) |
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with gr.Row(): |
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prompt = gr.Textbox(label="Input Prompts", placeholder="Enter prompts (one prompt for each image provided) separated by commas. Ex: Describe this image, What is in this image?", lines=8) |
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with gr.Row(): |
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submit = gr.Button("Submit") |
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with gr.Row(): |
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output = gr.Markdown(label="Questions and Answers", line_breaks=True) |
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with gr.Row(): |
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output2 = gr.Dataframe(label="Structured Dataframe", type="array", wrap=True) |
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submit.click(answer_questions, [img, prompt], [output, output2]) |
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demo.queue().launch() |
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