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 = "2024-04-02"
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(duration=10)
def answer_questions(image_tuples, prompt_text):
    print(f"prompt_text:\n{prompt_text}\n")
    print(f"image_tuples:\n{image_tuples}\n")
    
    prompts = [p.strip() for p in prompt_text.split(',')]  # Splitting and cleaning prompts
    image_embeds = [img[0] for img in image_tuples if img[0] is not None]  # Extracting images from tuples, ignoring None
    
    print(f"image_embeds:\n{image_embeds}\n")
    print(f"split prompts:\n{prompts}\n")

    #image_embeds = [moondream.encode_image(img) for img in images]
    
    answers = moondream.batch_answer(
        images=image_embeds,
        prompts=prompts,
        tokenizer=tokenizer,
    )
    
    for question, answer in zip(prompts, answers):
        print(f"Q: {question}")
        print(f"A: {answer}")
        print()
        
    return ["\n".join(ans) for ans in answers]

with gr.Blocks() as demo:
    gr.Markdown("# 🌔 moondream2\nA tiny vision language model. [GitHub](https://github.com/vikhyatk/moondream)")
    with gr.Row():
        img = gr.Gallery(label="Upload Images", type="pil")
        prompt = gr.Textbox(label="Input Prompts", placeholder="Enter prompts separated by commas. Ex: Describe this image, What is in this image?", lines=2)
        submit = gr.Button("Submit")
    output = gr.TextArea(label="Responses", lines=4)
    submit.click(answer_questions, [img, prompt], output)

demo.queue().launch()