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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() | |