Update app.py

app.py

@@ -1,230 +1,5 @@
-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
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import torch
-import json
-import re
-
-# 选择三个小型文本生成模型
-MODEL_CONFIGS = {
-    "GPT2-Small": {
-        "model_name": "gpt2",
-        "description": "OpenAI的GPT-2小型模型（1.24亿参数）",
-        "max_length": 100,
-        "color": "#FF6B6B"
-    },
-    "DistilGPT2": {
-        "model_name": "distilgpt2",
-        "description": "GPT-2的蒸馏版本（8200万参数）",
-        "max_length": 100,
-        "color": "#4ECDC4"
-    },
-    "GPT2-Medium": {
-        "model_name": "gpt2-medium",
-        "description": "GPT-2中型模型（3.55亿参数）",
-        "max_length": 100,
-        "color": "#45B7D1"
-    }
-}
-
-class TextGenerationComparator:
-    def __init__(self):
-        self.models = {}
-        self.tokenizers = {}
-        self.load_models()
-
-    def load_models(self):
-        """加载所有文本生成模型"""
-        print("正在加载模型...")
-        for model_key, config in MODEL_CONFIGS.items():
-            try:
-                print(f"加载 {model_key}...")
-                # 使用pipeline方式加载，更简单且内存友好
-                self.models[model_key] = pipeline(
-                    "text-generation",
-                    model=config["model_name"],
-                    tokenizer=config["model_name"],
-                    device=-1,  # 使用CPU，避免GPU内存问题
-                    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}")
-                # 创建一个mock模型用于演示
-                self.models[model_key] = None
-
-    def generate_text(self, model_key, prompt, max_length=50, temperature=0.7, top_p=0.9):
-        """使用指定模型生成文本"""
-        if self.models[model_key] is None:
-            return {
-                "generated_text": f"[Model {model_key} not loaded correctly, this is a simulated output] {prompt} and this is a sample continuation of the text...",
-                "inference_time": 0.5,
-                "input_length": len(prompt.split()),
-                "output_length": max_length,
-                "parameters": {
-                    "temperature": temperature,
-                    "top_p": top_p,
-                    "max_length": max_length
-                }
-            }
-
-        try:
-            start_time = time.time()
-
-            # 生成文本
-            result = self.models[model_key](
-                prompt,
-                max_length=len(prompt.split()) + max_length,
-                temperature=temperature,
-                top_p=top_p,
-                do_sample=True,
-                pad_token_id=50256,  # GPT-2的pad token
-                num_return_sequences=1,
-                truncation=True
-            )
-
-            end_time = time.time()
-
-            # 提取生成的文本（去除原始prompt）
-            generated_text = result[0]['generated_text']
-            if generated_text.startswith(prompt):
-                generated_text = generated_text[len(prompt):].strip()
-
-            return {
-                "generated_text": generated_text,
-                "full_text": result[0]['generated_text'],
-                "inference_time": round(end_time - start_time, 3),
-                "input_length": len(prompt.split()),
-                "output_length": len(generated_text.split()),
-                "parameters": {
-                    "temperature": temperature,
-                    "top_p": top_p,
-                    "max_length": max_length
-                }
-            }
-
-        except Exception as e:
-            return {
-                "error": f"生成错误: {str(e)}",
-                "inference_time": 0,
-                "input_length": 0,
-                "output_length": 0
-            }
-
-# 初始化比较器
-comparator = TextGenerationComparator()
-
-def run_text_generation_comparison(prompt, max_length, temperature, top_p):
-    """运行所有模型的文本生成对比"""
-    if not prompt.strip():
-        return "Please enter a prompt.", "Please enter a prompt.", "Please enter a prompt." # 提示文本为英文
-
-    results = {}
-
-    for model_key in MODEL_CONFIGS.keys():
-        result = comparator.generate_text(
-            model_key,
-            prompt,
-            max_length=int(max_length),
-            temperature=temperature,
-            top_p=top_p
-        )
-        results[model_key] = result
-
-    # 格式化输出
-    def format_result(result):
-        if "error" in result:
-            return json.dumps(result, indent=2, ensure_ascii=False)
-
-        # 这里的键名保留中文，值会是英文
-        formatted = {
-            "生成文本": result["generated_text"],
-            "推断时间": f"{result['inference_time']}s",
-            "生成Token数": result["output_length"],
-            "生成速度": f"{result['output_length']/max(result['inference_time'], 0.001):.1f} tokens/s"
-        }
-        return json.dumps(formatted, indent=2, ensure_ascii=False)
-
-    gpt2_result = format_result(results.get("GPT2-Small", {}))
-    distilgpt2_result = format_result(results.get("DistilGPT2", {}))
-    gpt2_medium_result = format_result(results.get("GPT2-Medium", {}))
-
-    return gpt2_result, distilgpt2_result, gpt2_medium_result
-
-def calculate_grace_scores_for_generation():
-    """为文本生成任务计算GRACE评估分数"""
-    # 基于文本生成任务特点的GRACE评分
-    grace_data = {
-        "GPT2-Small": {
-            "Generalization": 7.5,  # 中等泛化能力，适用多种文本类型
-            "Relevance": 8.2,       # 与输入提示相关性较好
-            "Artistry": 7.8,        # 创造性和表达力中等
-            "Consistency": 8.0,     # 输出一致性良好
-            "Efficiency": 9.2       # 小模型，效率很高
-        },
-        "DistilGPT2": {
-            "Generalization": 7.2,  # 蒸馏模型，泛化能力略低
-            "Relevance": 7.9,       # 相关性稍低于原模型
-            "Artistry": 7.5,        # 创造性受蒸馏影响
-            "Consistency": 7.8,     # 一致性略有损失
-            "Efficiency": 9.8       # 最小模型，效率最高
-        },
-        "GPT2-Medium": {
-            "Generalization": 8.8,  # 更大模型，更好的泛化
-            "Relevance": 9.1,       # 更好的上下文理解
-            "Artistry": 8.9,        # 更强的创造性表达
-            "Consistency": 8.7,     # 更一致的输出质量
-            "Efficiency": 6.5       # 较大模型，效率较低
-        }
-    }
-    return grace_data
-
-def create_generation_radar_chart():
-    """创建文本生成GRACE评估雷达图"""
-    grace_scores = calculate_grace_scores_for_generation()
-    # 类别名称翻译，但在图表中为了保持GRACE框架的名称一致性，这里保留英文，但在标题和描述中会使用中文
-    categories = ['Generalization', 'Relevance', 'Artistry', 'Consistency', 'Efficiency']
-
-    fig = go.Figure()
 
-    for i, (model_name, scores) in enumerate(grace_scores.items()):
-        values = [scores[cat] for cat in categories]
-        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：文本生成模型评估",
-            '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"""