layer2 commit

layer2/process/step1and2.py

@@ -0,0 +1,132 @@
+"""
+对821道英文问题进行处理
+1. 判断是否包含多个子问题，将问题拆分为完整子问题(去掉推理过程，只保留最后结果)
+2. 判断题目类型
+3. 将题目做成选择题
+   对计算题，在数值附近随机生成三个相似答案作为错误选项
+   对简答题，与标准答案最相近的其他问题的答案作为三个错误选项
+4. 将正确和错误选项随机打乱，生成ABCD选择题的模型
+5. 添加prompt，并将选择题包裹在[ANSWER]<answer>[/ANSWER]标签中
+6. 模型打分
+"""
+import json
+import time
+from openai import OpenAI
+import re
+from prompts import SINGLE_QUESTION_PROMPTS, QA_TYPE_PROMPTS, ONLY_ANSWER_PROMPTS
+
+API_KEY="sk-oYh3Xrhg8oDY2gW02c966f31C84449Ad86F9Cd9dF6E64a8d"
+BASE_URL="https://vip.apiyi.com/v1"
+MODEL_DEEPSEEK_V3="deepseek-chat"
+
+def load_data(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    return data
+
+def process_response(response):
+    """Extract and parse JSON from a response."""
+    json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', response)
+    json_str = json_match.group(1) if json_match else response.strip()
+    json_str = re.sub(r'(\$[^\$]*\$)', lambda m: m.group(1).replace('\\', '\\\\'), json_str)
+    json_str = json_str.replace('\\"', '"').replace("\\'", "'")
+    return json_str
+
+def save_data(data, output_file):
+    with open(output_file, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+def split_complex_question(question, answer):
+    client = OpenAI(api_key = API_KEY,base_url = BASE_URL)
+    try:
+        response = client.chat.completions.create(
+            model= MODEL_DEEPSEEK_V3,
+            messages=[
+                {"role": "system", "content": "You are an expert in decomposing complex technical questions into independent sub-questions and providing corresponding complete answers with preserved context, precision, and technical terminology. "},
+                {"role": "user", "content": SINGLE_QUESTION_PROMPTS.replace("{question}",question).replace("{answer}",answer)}
+            ],
+            stream = False,
+            temperature = 0
+        )
+        result = response.choices[0].message.content.strip()
+        # print(result)
+        return 1 if "It's a single issue." in result else json.loads(process_response(result))
+    except Exception as e:
+        print(f"API调用错误: {e}")
+        return [{"question": question, "answer": answer}]
+
+def single_question_process(data):
+    single_question_data = []
+    total = len(data)
+    for i, item in enumerate(data):
+        print(f"处理第 {i+1}/{total} 条数据...")
+        question = item["question"]
+        answer = item["answer"]
+        split_data = split_complex_question(question, answer)
+
+        if isinstance(split_data, list):
+            for q_data in split_data:
+                single_question_data.append({
+                    "idx":item["idx"],
+                    "question": q_data["question"],
+                    "answer": q_data["answer"]
+                })
+        else:
+            single_question_data.append({
+                "idx":item["idx"],
+                "question": question,
+                "answer": answer
+            })
+
+        if (i+1) % 10 == 0:
+            time.sleep(2)
+    return single_question_data
+
+def classify_qa_type(question, answer):
+    client = OpenAI(api_key = API_KEY,base_url = BASE_URL)
+    try:
+        response = client.chat.completions.create(
+            model = MODEL_DEEPSEEK_V3,
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant"},
+                {"role": "user", "content": QA_TYPE_PROMPTS.replace("{question}",question).replace("{answer}",answer)}
+            ],
+            stream=False
+        )
+        result = response.choices[0].message.content.strip().lower()
+        # print(result)
+        return {"1": "Calculation", "2": "Multiple choice", "3": "True/False"}.get(result, "Other")
+    except Exception as e:
+        print(f"API调用错误: {e}")
+        return "Other"
+
+def qa_type_process(data):
+    total = len(data)
+    for i, item in enumerate(data):
+        print(f"处理第 {i+1}/{total} 条数据...")
+        question = item["question"]
+        answer = item["answer"]
+        label = classify_qa_type(question, answer)
+        item["type"] = label
+
+        if (i+1) % 10 == 0:
+            time.sleep(2)
+    return data
+
+def main():
+    input_file = "/home/ubuntu/50T/fsy/layer2/QA/code/821.json"
+    output_file = "/home/ubuntu/50T/fsy/layer2/QA/code/processed_data.json"
+    data = load_data(input_file)
+
+    # step:1
+    single_question_data = single_question_process(data)
+    # step:2
+    qa_type_data = qa_type_process(single_question_data)
+    # step:3
+    
+
+    # save_data(processed_data, output_file)
+    print(f"处理完成，结果已保存到 {output_file}")
+
+if __name__ == "__main__":
+    main()