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import spaces
import torch
import re
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from PIL import Image
if torch.cuda.is_available():
device, dtype = "cuda", torch.float16
else:
device, dtype = "cpu", torch.float32
model_id = "vikhyatk/moondream2"
revision = "2024-08-26"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
moondream = AutoModelForCausalLM.from_pretrained(
model_id, trust_remote_code=True, revision=revision, torch_dtype=dtype
).to(device=device)
moondream.eval()
@spaces.GPU
def answer_questions(image_tuples, prompt_text):
result = ""
Q_and_A = ""
prompts = [p.strip() for p in prompt_text.split(',')]
image_embeds = [img[0] for img in image_tuples if img[0] is not None]
#print(f"\nprompts: {prompts}\n\n")
answers = []
for prompt in prompts:
image_answers = moondream.batch_answer(
images=[img.convert("RGB") for img in image_embeds],
prompts=[prompt] * len(image_embeds),
tokenizer=tokenizer,
)
answers.append(image_answers)
for i, prompt in enumerate(prompts):
Q_and_A += f"### Q: {prompt}\n"
for j, image_tuple in enumerate(image_tuples):
image_name = f"image{j+1}"
answer_text = answers[i][j]
Q_and_A += f"**{image_name} A:** \n {answer_text} \n\n"
result = {'headers': prompts, 'data': answers}
#print(f"result\n{result}\n\nQ_and_A\n{Q_and_A}\n\n")
return Q_and_A, result
with gr.Blocks() as demo:
gr.Markdown("# MoonDream WebUI")
gr.Markdown("## 🌔 WebUI is modify by https://huggingface.co/spaces/Csplk/moondream2-batch-processing")
gr.Markdown("## 🌔 moondream2 - A tiny vision language model. [GitHub](https://github.com/vikhyatk/moondream)")
with gr.Row():
img = gr.Gallery(label="Upload Images", type="pil", preview=True, columns=4)
with gr.Row():
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)
with gr.Row():
submit = gr.Button("Submit")
with gr.Row():
output = gr.Markdown(label="Questions and Answers", line_breaks=True)
with gr.Row():
output2 = gr.Dataframe(label="Structured Dataframe", type="array", wrap=True)
submit.click(answer_questions, [img, prompt], [output, output2])
demo.queue().launch()
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