#!/usr/bin/env python3 """ Gemini 3 Pro 数学试卷解析示例 展示如何使用 Gemini 模型进行数学试卷解析 """ import os import json from dotenv import load_dotenv from claude_exam_parser import ClaudeExamParser # 加载环境变量 load_dotenv() def example_1_basic_usage(): """示例1: 基本使用 - 使用 Gemini 解析数学试卷""" print("=" * 80) print("示例1: 使用 Gemini 3 Pro 解析数学试卷") print("=" * 80) # 创建解析器，明确指定使用 Gemini 模型 parser = ClaudeExamParser( model="google/gemini-3-pro-preview" ) # 解析数学试卷 # 注意：请替换为实际的试卷文件路径 result = parser.parse_exam_file( file_path="sample_math_exam.pdf", # 替换为实际文件 subject="数学", grade="高一" ) if result["success"]: data = result["result"] print(f"\n✅ 解析成功！") print(f"学科: {data['subject']}") print(f"年级: {data.get('grade', 'N/A')}") print(f"题目总数: {data['total_questions']}") print(f"\n前3道题目:") for q in data["questions"][:3]: print(f"\n 题目 {q['number']} ({q['type']})") print(f" 题干: {q['question'][:100]}...") if q.get("formulas"): print(f" 公式: {q['formulas'][:2]}") print(f" 难度: {q.get('difficulty', 'N/A')}") else: print(f"\n❌ 解析失败: {result['error']}") def example_2_compare_models(): """示例2: 对比不同模型的解析效果""" print("\n\n" + "=" * 80) print("示例2: 对比 Claude 和 Gemini 的解析效果") print("=" * 80) file_path = "sample_math_exam.pdf" # 替换为实际文件 models = [ "anthropic/claude-sonnet-4.5", "google/gemini-3-pro-preview" ] results = {} for model in models: print(f"\n正在使用 {model} 解析...") parser = ClaudeExamParser(model=model) result = parser.parse_exam_file(file_path, subject="数学") if result["success"]: data = result["result"] # 统计公式数量 formula_count = sum( len(q.get("formulas", [])) for q in data.get("questions", []) ) results[model] = { "total_questions": data.get("total_questions", 0), "formula_count": formula_count } print(f" ✅ 识别题目: {results[model]['total_questions']}") print(f" ✅ 提取公式: {results[model]['formula_count']}") else: print(f" ❌ 解析失败: {result['error']}") print(f"\n对比结果:") print(json.dumps(results, indent=2, ensure_ascii=False)) def example_3_extract_formulas(): """示例3: 提取并分析数学公式""" print("\n\n" + "=" * 80) print("示例3: 提取并分析数学公式") print("=" * 80) parser = ClaudeExamParser(model="google/gemini-3-pro-preview") result = parser.parse_exam_file( file_path="sample_math_exam.pdf", # 替换为实际文件 subject="数学" ) if result["success"]: data = result["result"] all_formulas = [] # 收集所有公式 for q in data["questions"]: formulas = q.get("formulas", []) if formulas: all_formulas.extend([ { "question_number": q["number"], "formula": f } for f in formulas ]) print(f"\n找到 {len(all_formulas)} 个公式:") for i, item in enumerate(all_formulas[:10], 1): # 显示前10个 print(f"{i}. 题目 {item['question_number']}: {item['formula']}") # 分析公式类型 formula_types = { "分数": 0, "根式": 0, "积分": 0, "求和": 0, "向量": 0 } for item in all_formulas: formula = item["formula"] if "\\frac" in formula: formula_types["分数"] += 1 if "\\sqrt" in formula: formula_types["根式"] += 1 if "\\int" in formula: formula_types["积分"] += 1 if "\\sum" in formula: formula_types["求和"] += 1 if "\\vec" in formula or "\\overrightarrow" in formula: formula_types["向量"] += 1 print(f"\n公式类型分布:") for ftype, count in formula_types.items(): if count > 0: print(f" {ftype}: {count}") def example_4_analyze_difficulty(): """示例4: 分析题目难度分布""" print("\n\n" + "=" * 80) print("示例4: 分析题目难度分布") print("=" * 80) parser = ClaudeExamParser(model="google/gemini-3-pro-preview") result = parser.parse_exam_file( file_path="sample_math_exam.pdf", # 替换为实际文件 subject="数学" ) if result["success"]: data = result["result"] # 难度分布 difficulty_dist = data.get("stats", {}).get("difficulty_distribution", {}) if difficulty_dist: print(f"\n难度分布:") total = sum(difficulty_dist.values()) for level in sorted(difficulty_dist.keys()): count = difficulty_dist[level] percentage = (count / total) * 100 bar = "█" * int(percentage / 5) print(f" 难度 {level}: {count:2d} 题 ({percentage:5.1f}%) {bar}") # 按难度分组题目 by_difficulty = {} for q in data["questions"]: diff = q.get("difficulty", 0) if diff not in by_difficulty: by_difficulty[diff] = [] by_difficulty[diff].append(q["number"]) print(f"\n各难度题目编号:") for level in sorted(by_difficulty.keys()): questions = by_difficulty[level] print(f" 难度 {level}: {', '.join(map(str, questions))}") def example_5_knowledge_points(): """示例5: 分析知识点覆盖""" print("\n\n" + "=" * 80) print("示例5: 分析知识点覆盖") print("=" * 80) parser = ClaudeExamParser(model="google/gemini-3-pro-preview") result = parser.parse_exam_file( file_path="sample_math_exam.pdf", # 替换为实际文件 subject="数学" ) if result["success"]: data = result["result"] # 统计知识点 knowledge_point_count = {} for q in data["questions"]: points = q.get("knowledge_points", []) for point in points: knowledge_point_count[point] = knowledge_point_count.get(point, 0) + 1 # 按出现频率排序 sorted_points = sorted( knowledge_point_count.items(), key=lambda x: x[1], reverse=True ) print(f"\n知识点覆盖 (共 {len(sorted_points)} 个知识点):") for point, count in sorted_points[:15]: # 显示前15个 bar = "●" * count print(f" {point:20s}: {count:2d} 题 {bar}") def main(): """主函数 - 运行所有示例""" print("\n") print("╔" + "=" * 78 + "╗") print("║" + " " * 20 + "Gemini 3 Pro 数学试卷解析示例" + " " * 28 + "║") print("╚" + "=" * 78 + "╝") # 检查 API Key if not os.getenv("OPENROUTER_API_KEY"): print("\n❌ 错误: 未设置 OPENROUTER_API_KEY") print("请在 .env 文件中设置，或运行: export OPENROUTER_API_KEY='your-key'") return # 运行示例（注意：需要实际的PDF文件） print("\n⚠️ 注意: 这些示例需要实际的PDF试卷文件") print("请将 'sample_math_exam.pdf' 替换为实际的文件路径\n") # 可以取消注释运行各个示例 # example_1_basic_usage() # example_2_compare_models() # example_3_extract_formulas() # example_4_analyze_difficulty() # example_5_knowledge_points() print("\n使用说明:") print("1. 准备一份数学试卷 PDF 文件") print("2. 编辑此文件，将 'sample_math_exam.pdf' 替换为实际文件路径") print("3. 取消注释要运行的示例") print("4. 运行: python example_gemini_usage.py") print("\n或者直接使用测试脚本:") print(" ./quick_test_gemini.sh your_math_exam.pdf 数学 高一") if __name__ == "__main__": main()