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# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#
# This space is created by SANJOG GHONGE for testing and learning purpose.
#
# If you want to remove this space or credits please contact me on my email id [[email protected]].
#
# Citation : @misc{qvq-72b-preview,
# title = {QVQ: To See the World with Wisdom},
# url = {https://qwenlm.github.io/blog/qvq-72b-preview/},
# author = {Qwen Team},
# month = {December},
# year = {2024}
# }
# @article{Qwen2VL,
# title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
# author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai,
# Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang,
# Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou,
# Jingren and Lin, Junyang},
# journal={arXiv preprint arXiv:2409.12191},
# year={2024}
# }
#
# -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
from qwen_vl_utils import process_vision_info
import gradio as gr
from PIL import Image
import torch
# Create a configuration for quantization
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype="float16",
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
# Load the model and processor
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/QVQ-72B-Preview", device_map="auto",
quantization_config=quantization_config,
offload_folder="offload",
)
processor = AutoProcessor.from_pretrained("Qwen/QVQ-72B-Preview")
# Function to process the image and question
def process_image_and_question(image, question):
if image is None or question.strip() == "":
return "Please provide both an image and a question."
# Prepare the input message
messages = [
{
"role": "system",
"content": [
# {"type": "text", "text": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}
{"type": "text", "text": "You are helpful assistant, you give answer in JSON"}
],
},
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": question},
],
}
]
# Process the inputs
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Generate the output
generated_ids = model.generate(**inputs, max_new_tokens=8192)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0] if output_text else "No output generated.")
return output_text[0] if output_text else "No output generated."
# Define the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Sanjog Test : Image and Question Answering\nProvide an image (JPG/PNG) and a related question to get an answer.")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Image (JPG/PNG)")
question_input = gr.Textbox(label="Enter your question")
with gr.Column():
output_box = gr.Textbox(label="Result", interactive=False)
with gr.Row():
clear_button = gr.Button("Clear")
submit_button = gr.Button("Submit")
# Define button functionality
clear_button.click(lambda: (None, "", ""), inputs=[], outputs=[image_input, question_input, output_box])
submit_button.click(process_image_and_question, inputs=[image_input, question_input], outputs=output_box)
# Launch the interface
demo.launch()
print(torch.cuda.memory_summary())
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