app.py

import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import gradio as gr

# 加载模型和处理器
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-7B-Instruct", 
    torch_dtype="auto", 
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# 定义处理函数
def recognize_and_analyze(image, text_prompt):
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": text_prompt},
            ],
        }
    ]

    # 准备推理输入数据
    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(model.device)

    # 推理：生成输出文本
    generated_ids = model.generate(**inputs, max_new_tokens=128)
    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]

# 设置Gradio界面
interface = gr.Interface(
    fn=recognize_and_analyze,
    inputs=[
        gr.inputs.Image(type="filepath", label="上传图像"),
        gr.inputs.Textbox(label="输入描述文本"),
    ],
    outputs=gr.outputs.Textbox(label="识别结果"),
    title="Qwen2.5-VL 物体识别与分析",
    description="上传图像并输入描述文本以获取识别和分析结果。",
)

# 启动Gradio应用
if __name__ == "__main__":
    interface.launch()