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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
# from qwen_vl_utils import process_vision_info
# import gradio as gr
# from PIL import Image
# # Load the model and processor
# model = Qwen2VLForConditionalGeneration.from_pretrained(
# "Qwen/QVQ-72B-Preview", torch_dtype="auto", device_map="auto"
# )
# 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."}
# ],
# },
# {
# "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
# )
# return output_text[0] if output_text else "No output generated."
# # Define the Gradio interface
# with gr.Blocks() as demo:
# gr.Markdown("# 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()
# ------------------------------------------------------------------------------------------------------------------------------------
import gradio as gr
from transformers import AutoProcessor, AutoModelForImageTextToText
# Load the processor and model
model_name = "Qwen/QVQ-72B-Preview"
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModelForImageTextToText.from_pretrained(model_name)
# Define the prediction function
def process_image_and_question(image, question):
if image is None or not question:
return "Please provide both an image and a question."
# Process the inputs
inputs = processor(images=image, text=question, return_tensors="pt")
# Generate the output
outputs = model.generate(**inputs)
answer = processor.batch_decode(outputs, skip_special_tokens=True)[0]
return answer
# Define the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# 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()
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