import gradio as gr
import spaces
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "llava-hf/llava-1.5-7b-hf"

prompt_format = "USER: <image>\n{}\nASSISTANT:"
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"

model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True,
).cuda()

processor = AutoProcessor.from_pretrained(model_id)

@spaces.GPU
def inference(text, image):
    prompt = prompt_format.format(text)
    inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)
    output = model.generate(**inputs, max_new_tokens=1024)
    return processor.decode(output[0][2:], skip_special_tokens=True)

gr.Interface(fn=inference, inputs=[gr.Text(), gr.Image()], outputs=gr.Text()).launch()