app.py

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

# Model and processor initialization
model_name = "Qwen/QVQ-72B-Preview"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.float16
)

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True
)

processor = AutoProcessor.from_pretrained(
    model_name,
    trust_remote_code=True
)

# 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):
    try:
        # Convert image to PIL format if needed
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image).convert("RGB")
        
        # Prepare messages
        if not text_input:
            text_input = "Please describe this image in detail."
            
        messages = [
            {
                "role": "system",
                "content": "You are a helpful and harmless assistant."
            },
            {
                "role": "user",
                "content": [
                    {"image": image},
                    {"text": text_input}
                ]
            }
        ]

        # Process inputs
        response = model.chat(tokenizer, messages)
        return response
        
    except Exception as e:
        return f"Error processing image: {str(e)}"

# 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)