R1-Onevision / app.py
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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
local_path = "Fancy-MLLM/R1-OneVision-7B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
local_path, torch_dtype="auto", device_map="cpu"
)
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)
# print(text_input)
# import pdb; pdb.set_trace()
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text_input],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
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}"
# print(f"Current text: {generated_text}") # 调试输出
# yield generated_text # 直接输出原始文本
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;
}
#qwen-md .katex-display { display: inline; }
#qwen-md .katex-display>.katex { display: inline; }
#qwen-md .katex-display>.katex>.katex-html { display: inline; }
"""
# UI 组件
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"),
input_text = gr.Textbox(label="input your question")
with gr.Row():
with gr.Column():
clear_btn = gr.ClearButton([*input_image, input_text])
with gr.Column():
submit_btn = gr.Button("Submit", variant="primary")
with gr.Column():
output_text = gr.Markdown(
label="Generated Response",
max_height="80vh",
min_height="50vh",
container=True,
latex_delimiters=[{
"left": "\\(",
"right": "\\)",
"display": True
}, {
"left": "\\begin\{equation\}",
"right": "\\end\{equation\}",
"display": True
}, {
"left": "\\begin\{align\}",
"right": "\\end\{align\}",
"display": True
}, {
"left": "\\begin\{alignat\}",
"right": "\\end\{alignat\}",
"display": True
}, {
"left": "\\begin\{gather\}",
"right": "\\end\{gather\}",
"display": True
}, {
"left": "\\begin\{CD\}",
"right": "\\end\{CD\}",
"display": True
}, {
"left": "\\[",
"right": "\\]",
"display": True
}],
elem_id="qwen-md")
submit_btn.click(
fn=generate_output,
inputs=[*input_image, input_text],
outputs=output_text,
queue=True
)
demo.launch(share=True)