# import gradio as gr
# from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
# from threading import Thread
# from qwen_vl_utils import process_vision_info
# import torch
# import time

# # Check if a GPU is available
# device = "cuda" if torch.cuda.is_available() else "cpu"

# local_path = "Fancy-MLLM/R1-OneVision-7B"

# # Load the model on the appropriate device (GPU if available, otherwise CPU)
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     local_path, torch_dtype="auto", device_map=device
# )
# processor = AutoProcessor.from_pretrained(local_path)

# def generate_output(image, text, button_click):
#     # Prepare input data
#     messages = [
#         {
#             "role": "user",
#             "content": [
#                 {"type": "image", "image": image, 'min_pixels': 1003520, 'max_pixels': 12845056},
#                 {"type": "text", "text": text},
#             ],
#         }
#     ]
    
#     # Prepare inputs for the model
#     text_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
#     image_inputs, video_inputs = process_vision_info(messages)
#     inputs = processor(
#         text=[text_input],
#         images=image_inputs,
#         videos=video_inputs,
#         padding=True,
#         return_tensors="pt",
#     )
    
#     # Move inputs to the same device as the model
#     inputs = inputs.to(model.device)

#     streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
#     generation_kwargs = dict(
#         **inputs,
#         streamer=streamer,
#         max_new_tokens=4096,
#         top_p=0.001,
#         top_k=1,
#         temperature=0.01,
#         repetition_penalty=1.0,
#     )
    
#     thread = Thread(target=model.generate, kwargs=generation_kwargs)
#     thread.start()
#     generated_text = ''
    
#     try:
#         for new_text in streamer:
#             generated_text += new_text
#             yield f"‎{generated_text}"
#     except Exception as e:
#         print(f"Error: {e}")
#         yield f"Error occurred: {str(e)}"

# Css = """
# #output-markdown {
#     overflow-y: auto;
#     white-space: pre-wrap; 
#     word-wrap: break-word;
# }
# #output-markdown .math {
#     overflow-x: auto;
#     max-width: 100%;
# }
# .markdown-text {
#     white-space: pre-wrap;
#     word-wrap: break-word;
# }
# .markdown-output {
#     min-height: 20vh;
#     max-width: 100%;
#     overflow-y: auto;
# }
# #qwen-md .katex-display { display: inline; }
# #qwen-md .katex-display>.katex { display: inline; }
# #qwen-md .katex-display>.katex>.katex-html { display: inline; }
# """

# with gr.Blocks(css=Css) as demo:
#     gr.HTML("""<center><font size=8>🦖 R1-OneVision Demo</center>""")

#     with gr.Row():
#         with gr.Column():
#             input_image = gr.Image(type="pil", label="Upload")  # **改回 PIL 处理**
#             input_text = gr.Textbox(label="Input your question")
#             with gr.Row():
#                 clear_btn = gr.ClearButton([input_image, input_text])
#                 submit_btn = gr.Button("Submit", variant="primary")

#         with gr.Column():
#             output_text = gr.Markdown(elem_id="qwen-md", container=True, elem_classes="markdown-output")

#     submit_btn.click(fn=generate_output, inputs=[input_image, input_text], outputs=output_text)

# demo.launch(share=False)


import gradio as gr
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import time
import torch
import spaces

MODEL_ID = "Fancy-MLLM/R1-OneVision-7B"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to("cuda").eval()

@spaces.GPU
def model_inference(input_dict, history):
    text = input_dict["text"]
    files = input_dict["files"]

    # Load images if provided
    if len(files) > 1:
        images = [load_image(image) for image in files]
    elif len(files) == 1:
        images = [load_image(files[0])]
    else:
        images = []

    # Validate input
    if text == "" and not images:
        gr.Error("Please input a query and optionally image(s).")
        return
    if text == "" and images:
        gr.Error("Please input a text query along with the image(s).")
        return

    # Prepare messages for the model
    messages = [
        {
            "role": "user",
            "content": [
                *[{"type": "image", "image": image} for image in images],
                {"type": "text", "text": text},
            ],
        }
    ]

    # Apply chat template and process inputs
    prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(
        text=[prompt],
        images=images if images else None,
        return_tensors="pt",
        padding=True,
    ).to("cuda")

    # Set up streamer for real-time output
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=2048)

    # Start generation in a separate thread
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    # Stream the output
    buffer = ""
    yield "Thinking..."
    for new_text in streamer:
        buffer += new_text
        time.sleep(0.01)
        yield buffer


demo = gr.ChatInterface(
    fn=model_inference,
    description="# **Fancy-MLLM/R1-OneVision-7B**",
    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"),
    stop_btn="Stop Generation",
    multimodal=True,
    cache_examples=False,
)

demo.launch(debug=True)