import spaces
import os
import time
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
import gradio as gr
from threading import Thread
from PIL import Image

# Model and processor initialization
processor = AutoProcessor.from_pretrained("Qwen/QVQ-72B-Preview")
model = AutoModelForImageTextToText.from_pretrained("Qwen/QVQ-72B-Preview").cuda().eval()

# Footer
footer = """
<div style="text-align: center; margin-top: 20px;">
    <p>Powered by QVQ-72B Model</p>
</div>
"""

# Vision model function
@spaces.GPU()
def process_image(image, text_input=None):
    # Convert image to PIL format
    image = Image.fromarray(image).convert("RGB")
    
    # Prepare inputs
    if text_input:
        inputs = processor(text=text_input, images=image, return_tensors="pt").to("cuda:0")
    else:
        inputs = processor(images=image, return_tensors="pt").to("cuda:0")
    
    # Generate output
    outputs = model.generate(**inputs, max_new_tokens=1000)
    
    # Decode response
    response = processor.batch_decode(outputs, skip_special_tokens=True)[0]
    
    return response

# CSS styling
css = """
footer {
    visibility: hidden;
}
"""

# Gradio interface
with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
    with gr.Row():
        input_img = gr.Image(label="Input Image")
    with gr.Row():
        text_input = gr.Textbox(label="Question (Optional)")
    with gr.Row():
        submit_btn = gr.Button(value="Submit")
    with gr.Row():
        output_text = gr.Textbox(label="Response")

    submit_btn.click(process_image, [input_img, text_input], [output_text])
    
    gr.HTML(footer)

# Launch the app
demo.launch(debug=True)