ui.py

import os
import time
import logging
import gradio as gr
import torch
from PIL import Image

# 导入自定义模块
from models.model_manager import ModelManager
from inference.inference_service import InferenceService

logger = logging.getLogger(__name__)

class GradioUI:
    def __init__(self, inference_service: InferenceService):
        self.inference_service = inference_service
        
        # 初始化Gradio界面
        self.interface = None
    
    def create_interface(self) -> gr.Blocks:
        """
        创建Gradio界面
        
        Returns:
            gr.Blocks: Gradio界面实例
        """
        with gr.Blocks(
            title="Qwen-Image-2512 文本到图像生成",
            theme=gr.themes.Soft(),
            css="""
            .main-container { max-width: 1200px; margin: 0 auto; }
            .title { text-align: center; margin-bottom: 2rem; }
            .upload-section { margin-bottom: 2rem; }
            .params-section { margin-bottom: 2rem; }
            .status-section { margin-bottom: 2rem; }
            .result-section { margin-bottom: 2rem; }
            .param-group { display: flex; flex-wrap: wrap; gap: 1rem; margin-bottom: 1rem; }
            .param-item { flex: 1 1 200px; }
            """
        ) as interface:
            
            # 标题
            gr.HTML("""
            <h1 class="title">Qwen-Image-2512 文本到图像生成</h1>
            <p class="title" style="font-size: 1.2rem; color: #666;">基于阿里通义千问的高性能图像生成模型</p>
            """)
            
            # 状态显示
            with gr.Row(elem_id="status-section"):
                status_text = gr.Textbox(
                    label="状态",
                    value="模型加载中...",
                    interactive=False,
                    elem_id="status-text"
                )
                
                progress_bar = gr.Progress(track_tqdm=True)
            
            # 主要内容区域
            with gr.Row(elem_id="main-content"):
                # 左侧：输入和参数
                with gr.Column(scale=1, min_width=300):
                    # 文本提示输入
                    with gr.Group(elem_id="prompt-section"):
                        prompt = gr.Textbox(
                            label="生成提示",
                            placeholder="输入您想要生成的图像描述...",
                            lines=3,
                            max_lines=5,
                            elem_id="prompt-input"
                        )
                        
                        negative_prompt = gr.Textbox(
                            label="负面提示",
                            placeholder="输入您想要避免的内容...",
                            lines=2,
                            max_lines=3,
                            elem_id="negative-prompt-input"
                        )
                    
                    # 参数控制面板
                    with gr.Group(elem_id="params-section"):
                        gr.Markdown("### 生成参数")
                        
                        with gr.Row(elem_id="param-group"):
                            # 图像尺寸
                            with gr.Column(elem_id="param-item"):
                                width = gr.Slider(
                                    label="宽度",
                                    minimum=256,
                                    maximum=2512,
                                    step=64,
                                    value=1024,
                                    elem_id="width-slider"
                                )
                                
                                height = gr.Slider(
                                    label="高度",
                                    minimum=256,
                                    maximum=2512,
                                    step=64,
                                    value=1024,
                                    elem_id="height-slider"
                                )
                            
                            # 推理参数
                            with gr.Column(elem_id="param-item"):
                                num_inference_steps = gr.Slider(
                                    label="推理步数",
                                    minimum=1,
                                    maximum=100,
                                    step=1,
                                    value=50,
                                    elem_id="steps-slider"
                                )
                                
                                guidance_scale = gr.Slider(
                                    label="引导尺度",
                                    minimum=0.0,
                                    maximum=20.0,
                                    step=0.1,
                                    value=7.5,
                                    elem_id="guidance-slider"
                                )
                            
                            # 其他参数
                            with gr.Column(elem_id="param-item"):
                                seed = gr.Number(
                                    label="随机种子",
                                    value=None,
                                    precision=0,
                                    elem_id="seed-input"
                                )
                                
                                num_images = gr.Slider(
                                    label="生成数量",
                                    minimum=1,
                                    maximum=4,
                                    step=1,
                                    value=1,
                                    elem_id="num-images-slider"
                                )
                    
                    # 生成按钮
                    with gr.Row(elem_id="button-section"):
                        generate_btn = gr.Button(
                            "生成图像",
                            variant="primary",
                            size="lg",
                            elem_id="generate-btn"
                        )
                        
                        clear_btn = gr.Button(
                            "清除",
                            variant="secondary",
                            size="lg",
                            elem_id="clear-btn"
                        )
                
                # 右侧：结果展示
                with gr.Column(scale=2, min_width=500):
                    with gr.Group(elem_id="result-section"):
                        gr.Markdown("### 生成结果")
                        
                        # 图像输出区域
                        gallery = gr.Gallery(
                            label="生成的图像",
                            show_label=False,
                            elem_id="gallery",
                            columns=2,
                            rows=2,
                            object_fit="contain",
                            height="auto"
                        )
                        
                        # 生成信息
                        with gr.Row(elem_id="info-section"):
                            execution_time = gr.Textbox(
                                label="生成时间",
                                interactive=False,
                                elem_id="execution-time"
                            )
                            
                            image_count = gr.Textbox(
                                label="图像数量",
                                interactive=False,
                                elem_id="image-count"
                            )
            
            # 示例提示
            with gr.Row(elem_id="examples-section"):
                gr.Markdown("### 示例提示")
                
                examples = gr.Examples(
                    examples=[
                        ["一只可爱的柯基犬在草地上奔跑，阳光明媚，高清细节", "模糊, 低质量, 变形", 1024, 1024, 50, 7.5, None, 1],
                        ["一个未来主义城市的夜景，霓虹灯闪烁，飞行器穿梭", "模糊, 低质量, 变形", 1024, 1024, 50, 7.5, None, 1],
                        ["一朵盛开的玫瑰花，特写镜头，超高清细节，自然光线", "模糊, 低质量, 变形", 1024, 1024, 50, 7.5, None, 1],
                    ],
                    inputs=[prompt, negative_prompt, width, height, num_inference_steps, guidance_scale, seed, num_images],
                    outputs=[gallery, execution_time, image_count],
                    fn=self.generate_images,
                    cache_examples=False
                )
            
            # 事件监听
            generate_btn.click(
                fn=self.generate_images,
                inputs=[prompt, negative_prompt, width, height, num_inference_steps, guidance_scale, seed, num_images],
                outputs=[gallery, execution_time, image_count, status_text],
                show_progress=True
            )
            
            clear_btn.click(
                fn=self.clear_all,
                inputs=[],
                outputs=[prompt, negative_prompt, width, height, num_inference_steps, guidance_scale, seed, num_images, gallery, execution_time, image_count, status_text]
            )
            
            # 初始化状态
            status_text.value = "就绪，可以生成图像"
            
        return interface
    
    def generate_images(
        self,
        prompt: str,
        negative_prompt: str,
        width: int,
        height: int,
        num_inference_steps: int,
        guidance_scale: float,
        seed: int,
        num_images: int
    ):
        """
        生成图像的处理函数
        
        Args:
            prompt: 生成提示
            negative_prompt: 负面提示
            width: 生成图像宽度
            height: 生成图像高度
            num_inference_steps: 推理步数
            guidance_scale: 引导尺度
            seed: 随机种子
            num_images: 生成图像数量
            
        Returns:
            tuple: (生成的图像列表, 执行时间, 图像数量, 状态)
        """
        if not prompt:
            return [], "0.00秒", "0", "请输入生成提示"
        
        try:
            start_time = time.time()
            
            # 生成图像
            images = self.inference_service.generate_image(
                prompt=prompt,
                negative_prompt=negative_prompt if negative_prompt else None,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                width=width,
                height=height,
                seed=seed if seed is not None else None,
                num_images_per_prompt=num_images
            )
            
            end_time = time.time()
            execution_time = end_time - start_time
            
            return (
                images,
                f"{execution_time:.2f}秒",
                f"{len(images)}",
                "生成完成"
            )
            
        except Exception as e:
            logger.error(f"图像生成失败: {str(e)}")
            return [], "0.00秒", "0", f"生成失败: {str(e)}"
    
    def clear_all(self):
        """
        清除所有输入和输出
        
        Returns:
            tuple: 清除后的状态
        """
        return (
            "",  # prompt
            "",  # negative_prompt
            1024,  # width
            1024,  # height
            50,  # num_inference_steps
            7.5,  # guidance_scale
            None,  # seed
            1,  # num_images
            [],  # gallery
            "",  # execution_time
            "",  # image_count
            "就绪，可以生成图像"  # status_text
        )
    
    def launch(self, share: bool = False, server_name: str = "0.0.0.0", server_port: int = 7860):
        """
        启动Gradio界面
        
        Args:
            share: 是否生成公共链接
            server_name: 服务器地址
            server_port: 服务器端口
        """
        if self.interface is None:
            self.interface = self.create_interface()
        
        logger.info(f"启动Gradio界面: http://{server_name}:{server_port}")
        
        self.interface.launch(
            share=share,
            server_name=server_name,
            server_port=server_port,
            show_api=False,
            quiet=True
        )

if __name__ == "__main__":
    # 配置日志
    logging.basicConfig(level=logging.INFO)
    
    # 初始化推理服务
    inference_service = InferenceService(
        model_path="./models",
        device="cpu",
        dtype=torch.float32
    )
    
    # 初始化模型
    inference_service.initialize()
    
    # 创建并启动Gradio界面
    gradio_ui = GradioUI(inference_service)
    gradio_ui.launch()