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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 | |
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) |