# # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
# # 
# # 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 [ghongesanjog@gmail.com].
# #
# # 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()