app.py

import gradio as gr
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import time
import numpy as np
# 导入 AutoTokenizer 和 AutoModelForSeq2SeqLM
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM 
import torch
import json
import re

# 选择两个中文到英文的翻译模型
MODEL_CONFIGS = {
    "Chinese-to-English (Opus-MT)": {
        "model_name": "Helsinki-NLP/opus-mt-zh-en",
        "description": "中文到英文的机器翻译模型 (Helsinki-NLP OPUS-MT)",
        "max_length": 200, # 翻译输出的最大长度
        "color": "#FF6B6B"
    },
    "Chinese-to-English (mBART-Large-50)": { # 替换为mBART模型
        "model_name": "facebook/mbart-large-50-many-to-one-mmt",
        "description": "中文到英文的机器翻译模型 (Facebook mBART-Large-50)",
        "max_length": 200, # 翻译输出的最大长度
        "color": "#4ECDC4"
    }
    # 如果需要第三个模型，可以取消注释下面这个，或替换成您想要的
    # "Chinese-to-English (Another Model)": {
    #     "model_name": "facebook/mbart-large-50-one-to-many-mmt", # 另一个多语言模型，需要指定 src_lang/tgt_lang
    #     "description": "中文到英文的机器翻译模型 (Facebook mBART-Large-50)",
    #     "max_length": 200,
    #     "color": "#45B7D1"
    # }
}

class TranslationComparator:
    def __init__(self):
        self.models = {}
        self.load_models()

    def load_models(self):
        """加载所有翻译模型"""
        print("正在加载翻译模型...")
        for model_key, config in MODEL_CONFIGS.items():
            try:
                print(f"加载 {model_key} ({config['model_name']})...")
                
                # 对于翻译任务，使用 "translation" pipeline
                # 注意：mBART模型需要指定 source_lang 和 target_lang
                if "mbart-large-50" in config["model_name"]:
                    self.models[model_key] = pipeline(
                        "translation",
                        model=config["model_name"],
                        tokenizer=config["model_name"],
                        src_lang="zh_CN",  # 源语言为中文
                        tgt_lang="en_US",  # 目标语言为英文
                        device=-1, # 使用CPU，避免GPU内存不足问题
                        torch_dtype=torch.float32 # 保持一致，或根据模型精度调整
                    )
                else: # 对于Helsinki-NLP/opus-mt-zh-en等
                    self.models[model_key] = pipeline(
                        "translation", # 也可以用 "translation_zh_to_en" 如果 pipeline 支持
                        model=config["model_name"],
                        tokenizer=config["model_name"],
                        device=-1, # 使用CPU
                        torch_dtype=torch.float32
                    )
                print(f"✓ {model_key} 加载成功")
            except Exception as e:
                print(f"✗ {model_key} 加载失败: {e}")
                self.models[model_key] = None

    def translate_text(self, model_key, text_to_translate, max_length=200):
        """使用指定模型进行翻译"""
        model_entry = self.models.get(model_key)
        if model_entry is None:
            return {
                "translated_text": f"[Model {model_key} not loaded correctly, this is a simulated translation]",
                "inference_time": 0.5,
                "input_length": len(text_to_translate.split()),
                "output_length": 50, # 模拟输出长度
                "parameters": {
                    "max_length": max_length
                }
            }

        try:
            start_time = time.time()
            
            # 翻译文本
            # pipeline("translation") 的返回格式是 [{"translation_text": "..."}]
            result = model_entry( # 直接使用 model_entry，因为现在都是pipeline对象
                text_to_translate,
                max_length=max_length
            )
            
            end_time = time.time()
            
            translated_text = result[0]['translation_text']
            
            return {
                "translated_text": translated_text,
                "inference_time": round(end_time - start_time, 3),
                "input_length": len(text_to_translate.split()),
                "output_length": len(translated_text.split()),
                "parameters": {
                    "max_length": max_length
                }
            }
        
        except Exception as e:
            return {
                "error": f"翻译错误: {str(e)}",
                "inference_time": 0,
                "input_length": 0,
                "output_length": 0
            }

# 初始化比较器
comparator = TranslationComparator()

def run_translation_comparison(zh_prompt, max_length):
    """运行所有中文到英文模型的翻译对比"""
    
    if not zh_prompt.strip():
        # 返回与模型数量相匹配的错误消息
        return tuple([gr.Code.update(value=json.dumps({"错误信息": "请输入中文文本进行翻译"}, ensure_ascii=False)) for _ in MODEL_CONFIGS])

    results = {}
    outputs_list = []

    for model_key in MODEL_CONFIGS.keys():
        result = comparator.translate_text(
            model_key,
            zh_prompt,
            max_length=int(max_length)
        )
        results[model_key] = result
    
        # 格式化输出
        if "error" in result:
            outputs_list.append(json.dumps({"错误信息": result["error"]}, indent=2, ensure_ascii=False))
        else:
            formatted = {
                "翻译文本": result["translated_text"],
                "推断时间": f"{result['inference_time']}s",
                "翻译Token数": result["output_length"],
                "翻译速度": f"{result['output_length']/max(result['inference_time'], 0.001):.1f} tokens/s"
            }
            outputs_list.append(json.dumps(formatted, indent=2, ensure_ascii=False))
    
    return tuple(outputs_list)


def calculate_grace_scores_for_translation():
    """为翻译任务计算GRACE评估分数"""
    # 模拟中文到英文翻译模型的GRACE分数
    grace_data = {
        "Chinese-to-English (Opus-MT)": {
            "Generalization": 7.8, # 处理不同领域中翻英能力
            "Relevance": 8.3,     # 翻译内容与原文语义相关性
            "Accuracy": 8.0,      # 翻译精确性
            "Consistency": 7.9,   # 翻译稳定性
            "Efficiency": 7.5     # 推理效率
        },
        "Chinese-to-English (mBART-Large-50)": { # **这里已修改！**
            "Generalization": 8.5, # 更大型多语言模型，泛化性通常更强
            "Relevance": 8.8,
            "Accuracy": 8.6,
            "Consistency": 8.5,
            "Efficiency": 6.0     # 模型较大，效率可能略低
        }
    }
    return grace_data


def create_translation_radar_chart():
    """创建翻译GRACE评估雷达图"""
    grace_scores = calculate_grace_scores_for_translation()
    categories = ['Generalization', 'Relevance', 'Accuracy', 'Consistency', 'Efficiency'] # 更改为翻译维度

    fig = go.Figure()

    for i, (model_name, scores) in enumerate(grace_scores.items()):
        values = [scores[cat] for cat in categories]
        # **这里使用 MODEL_CONFIGS[model_name]["color"] 依赖于 MODEL_CONFIGS 和 grace_scores 的键名一致**
        # 这是导致之前 KeyError 的地方，现在应该已修复，因为 calculate_grace_scores_for_translation 的键名已更新
        color = MODEL_CONFIGS[model_name]["color"] 

        fig.add_trace(go.Scatterpolar(
            r=values,
            theta=categories,
            fill='toself',
            name=model_name,
            line_color=color,
            fillcolor=color,
            opacity=0.6
        ))

    fig.update_layout(
        polar=dict(
            radialaxis=dict(
                visible=True,
                range=[0, 10],
                tickfont=dict(size=10)
            )
        ),
        showlegend=True,
        title={
            'text': "GRACE框架：中文到英文翻译模型评估",
            'x': 0.5,
            'font': {'size': 16}
        },
        width=600,
        height=500
    )
    return fig

def create_performance_bar_chart():
    """创建性能对比柱状图"""
    grace_scores = calculate_grace_scores_for_translation()
    models = list(grace_scores.keys())
    categories = ['Generalization', 'Relevance', 'Accuracy', 'Consistency', 'Efficiency'] # 更改为翻译维度

    fig = go.Figure()
    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#F7DC6F', '#BB8FCE']

    for i, category in enumerate(categories):
        values = [grace_scores[model][category] for model in models]
        fig.add_trace(go.Bar(
            name=category,
            x=models,
            y=values,
            marker_color=colors[i % len(colors)],
            opacity=0.8
        ))

    fig.update_layout(
        title='GRACE框架详细对比 - 中文到英文翻译',
        xaxis_title='模型',
        yaxis_title='分数 (0-10)',
        barmode='group',
        width=700,
        height=400
    )
    return fig

def create_model_info_table():
    """创建模型信息对比表"""
    model_info = []
    for model_key, config in MODEL_CONFIGS.items():
        # 模拟参数信息
        if "opus-mt-zh-en" in config["model_name"]:
            params = "~3亿"
            size = "~1.2GB"
        elif "mbart-large-50" in config["model_name"]: # 修改为mBART的参数
            params = "~6.1亿" # mBART-Large-50 的实际参数量
            size = "~2.4GB" # mBART-Large-50 的实际模型大小
        else: # 默认值
            params = "未知"
            size = "未知"
        
        model_info.append({
            "模型": model_key,
            "参数量": params,
            "模型大小": size,
            "描述": config["description"],
            "最大输出长度": config["max_length"]
        })
    return pd.DataFrame(model_info)

def create_summary_scores_table():
    """创建评分摘要表"""
    grace_scores = calculate_grace_scores_for_translation()
    summary_data = []
    for model_name, scores in grace_scores.items():
        avg_score = np.mean(list(scores.values()))
        summary_data.append({
            "模型": model_name,
            "泛化性": scores["Generalization"],
            "相关性": scores["Relevance"],
            "准确性": scores["Accuracy"], # 更改为准确性
            "一致性": scores["Consistency"],
            "效率性": scores["Efficiency"],
            "平均分": round(avg_score, 2)
        })
    df = pd.DataFrame(summary_data)
    return df

# 预设的示例中文提示
EXAMPLE_ZH_PROMPTS = [
    "你好，今天过得怎么样？",
    "敏捷的棕色狐狸跳过懒惰的狗。",
    "人工智能正在改变许多行业。",
    "今天天气真好，我们去公园散步吧。"
]

def create_app():
    with gr.Blocks(title="中文到英文翻译模型对比", theme=gr.themes.Soft()) as app:
        gr.Markdown("# 🌐 中文到英文翻译模型对比竞技场")
        gr.Markdown("### 使用GRACE框架对比不同中文到英文翻译模型在翻译任务中的表现")
        
        with gr.Tabs():
            # Arena选项卡
            with gr.TabItem("️ 翻译竞技场"):
                gr.Markdown("## 翻译竞技场")
                gr.Markdown("请在下方输入需要翻译的**中文**文本，查看不同模型翻译成**英文**的效果。")
                
                with gr.Row():
                    with gr.Column(scale=2): # 增加输入框的比例
                        input_zh_prompt = gr.Textbox(
                            label="输入中文文本",
                            placeholder="在此输入您的中文文本...",
                            lines=4, # 增加行数
                            value=EXAMPLE_ZH_PROMPTS[0]
                        )
                        # 预设中文示例按钮
                        with gr.Row():
                            for i, example in enumerate(EXAMPLE_ZH_PROMPTS):
                                gr.Button(f"示例 {i+1}", size="sm").click(
                                    fn=lambda x=example: x,
                                    outputs=[input_zh_prompt]
                                )
                    
                    with gr.Column(scale=1): # 调整参数控制列的比例
                        max_length = gr.Slider(
                            minimum=50,
                            maximum=500,
                            value=200,
                            step=10,
                            label="最大输出Token数"
                        )
                        
                        submit_btn = gr.Button(" 开始翻译", variant="primary", size="lg")
                
                # 动态创建输出框
                output_boxes = []
                for model_key, config in MODEL_CONFIGS.items():
                    output_boxes.append(gr.Code(
                        label=f"{model_key} 翻译结果", # 明确翻译方向
                        language="json",
                        value="点击“开始翻译”查看结果"
                    ))

                submit_btn.click(
                    fn=run_translation_comparison,
                    inputs=[input_zh_prompt, max_length],
                    outputs=output_boxes
                )
            
            # Benchmark选项卡
            with gr.TabItem(" GRACE 基准测试"):
                gr.Markdown("## GRACE框架对翻译的评估")
                gr.Markdown("""
                **GRACE框架在翻译中的维度定义：**
                - **G**eneralization (泛化性): 模型处理不同领域、风格和复杂度的文本并进行准确翻译的能力。
                - **R**elevance (相关性): 翻译内容在语义和上下文上与原文的匹配程度。
                - **A**ccuracy (准确性): 翻译的精确性和无误性，包括语法、词汇和句法结构的正确性。
                - **C**onsistency (一致性): 对相同或类似输入文本在不同时间或不同上下文中的翻译稳定性。
                - **E**fficiency (效率性): 翻译速度和所需的计算资源（如内存和CPU/GPU使用）。
                """)
                
                with gr.Row():
                    radar_plot = gr.Plot(
                        value=create_translation_radar_chart(),
                        label="GRACE 雷达图"
                    )
                
                with gr.Row():
                    bar_plot = gr.Plot(
                        value=create_performance_bar_chart(),
                        label="详细性能对比"
                    )
                
                with gr.Row():
                    with gr.Column():
                        model_info_df = create_model_info_table()
                        model_info_table = gr.Dataframe(
                            value=model_info_df,
                            label="模型信息",
                            interactive=False
                        )
                    
                    with gr.Column():
                        summary_df = create_summary_scores_table()
                        summary_table = gr.Dataframe(
                            value=summary_df,
                            label="GRACE 评分摘要",
                            interactive=False
                        )
        
    return app

# 创建并启动 Gradio 应用
if __name__ == "__main__":
    app = create_app()
    app.launch()