import torch
import gradio as gr
from transformers import TextIteratorStreamer, AutoProcessor, LlavaForConditionalGeneration
from PIL import Image
import requests
import threading
import spaces
import accelerate

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Krypton 🕋</h1>
<p>This uses an Open Source model from <a href="https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers"><b>xtuner/llava-llama-3-8b-v1_1-transformers</b></a></p>
</div>
'''

model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
model = LlavaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True
).to('cuda')

processor = AutoProcessor.from_pretrained(model_id)

@spaces.GPU(duration=120)
def krypton(input_image):

    pil_image = Image.fromarray(input_image.astype('uint8'), 'RGB')
    # image = Image.open(requests.get(url, stream=True).raw)
    prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
            "<|start_header_id|>assistant<|end_header_id|>\n\n")
    inputs = processor(prompt, pil_image, return_tensors='pt').to('cuda', torch.float16)
    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)
    output_text = processor.decode(outputs[0][:2], skip_special_tokens=True)
    return output_text

with gr.Blocks(fill_height=True) as demo:
    gr.Markdown(DESCRIPTION)
    gr.Interface(
        fn=krypton,
        inputs="image",
        outputs="text",
        fill_height=True
    )

if __name__ == "__main__":
    demo.launch()