修复代码

.gitignore

@@ -2,3 +2,5 @@ Qwen3-1.7B/**
 **/__pycache__/**
 
 .venv
+.env
+best_ckpt.pth

feature_extraction.py

@@ -0,0 +1,452 @@
+'''
+批量提取特征方法
+很花时间
+'''
+
+
+from openai import OpenAI
+import json
+import os
+from dotenv import load_dotenv
+import re
+
+# 加载环境变量
+load_dotenv(dotenv_path=".env")
+
+# 读取环境变量
+API_KEY = os.getenv("QWEN_API_KEY")
+MODEL = os.getenv("MODEL_NAME", "qwen3-next-80b-a3b-thinking")
+TEMPERATURE = float(os.getenv("TEMPERATURE", 0.1))  # 降低随机性，提升格式稳定性
+OUTPUT_DIR = os.getenv("OUTPUT_DIR", "time_12_1/data_ch_1")
+
+
+client = OpenAI(
+    api_key=API_KEY,
+    # base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
+    # https://sg1.proxy.yinlihupo.cc/proxy/
+    base_url="https://openrouter.ai/api/v1"
+)
+
+
+def build_extraction_prompt(dialogue_text):
+    prompt_template = """
+# Role
+你是一名拥有15年经验的“家庭教育销售通话审计专家”。你的核心能力是透过家长杂乱的表述，精准捕捉深层心理动机、家庭权力结构、隐形财富信号以及高危销售线索。
+
+# Core Protocols (核心审计协议 - 最高优先级)
+
+## 1. 宁缺毋滥原则 (The Principle of Precision)
+* **存在即输出，无证即沉默**：忽略任何关于“字段数量”的限制。如果原文中有20个维度的有效证据，就输出20个；如果只有3个，就输出3个。
+* **严禁凑数**：如果原文未提及某维度，或者证据模糊两可，**绝对不要**输出该 Key。不要为了追求“信息丰富”而强行填空。
+
+## 2. 证据阵列法则 (The Law of Evidence Arrays)
+* **数据结构变更**：`evidence` 字段必须是 **字符串数组 (List<String>)**，严禁使用单一字符串。
+* **颗粒度控制 (Granularity Control)**：
+    * 数组中的元素必须是 **具有独立语义的完整原句** 或 **包含主谓宾的完整意群**。
+    * **禁止碎片**：严禁提取如 "不合适"、"太贵"、"焦虑" 这样缺乏上下文的短语。
+    * **主体过滤**：**仅提取家长（客户）表达的原话**，严禁提取销售人员的引导语、复述语或共情语。
+* **纯净引用**：每一个元素必须是原文的 100% 完美复制。
+    * **严禁拼接**：严禁使用“+”、“和”、“以及”将两句不连贯的话拼在同一个字符串里。
+    * **严禁篡改**：禁止总结、禁止润色、禁止“原文+分析”。你的分析只能体现在 `value` 字段中。
+
+## 3. 结论极简法则 (The Law of Concise Conclusion)
+* **强制必输字段**：`Follow_up_Priority` 是 **核心必选字段**，无论任何情况都必须输出，不允许缺失。
+* **Follow_up_Priority 兜底规则**：
+  - 若文本完全无痛点/无财力/无意识，`value` 填“C级 (无痛点/无意识)”，`evidence` 填 ["文本未提及任何痛点、财力或意向相关内容"]
+  - 若仅部分信息缺失，按规则评级并在 `evidence` 中列出已有有效原句。
+* **Value 约束**：`value` 字段必须是 **客观、简练的定性结论**（必须限制在 **20个汉字以内**）。
+    * *正确示例*: "A级 (高痛点+强财力)"
+    * *错误示例*: "家长表现出对价格的犹豫，虽然她很有钱，但是因为..." (禁止小作文)
+
+## 4. 身份与财富的高敏嗅觉
+* 对于**高价值信号**（职业/多孩/私立学校/房产）和**生命红线**（自杀/不想活了/抑郁症确诊）保持极度敏感，一旦出现必须提取。
+
+# Task
+阅读提供的销售通话录音文本，从以下 23 个预设维度中筛选出**有效信息**，生成一份高精度的客户画像 JSON。
+
+# Field Definitions (字段定义与提取逻辑)
+### [第一组：心理动力与危机]
+1.  **Core_Fear_Source** (深层恐惧)
+    * *逻辑*: 驱动家长寻求帮助的终极噩梦。是怕孩子死（生命安全）？怕孩子阶级跌落？还是怕自己面子挂不住？
+    * *注意*: 必须提取具体的后果描述。
+2.  **Pain_Threshold** (痛苦阈值)
+    * *逻辑*: 家长当前的情绪状态。是“崩溃急救”（无法忍受，必须马上解决），还是“隐隐作痛”（还能凑合）？
+3.  **Time_Window_Pressure** (时间压力)
+    * *逻辑*: 客观的截止日期。如：距离中高考仅剩X月、休学复课最后期限、学校劝退通牒。
+4.  **Helplessness_Index** (无助指数)
+    * *逻辑*: 家长是否已经尝试过多种方法均失败（习得性无助），还是盲目自信觉得还能管。
+5.  **Social_Shame** (社交耻感)
+    * *逻辑*: 孩子问题是否影响了家长的社会形象（怕老师找、怕亲戚问、不敢出门）。
+6.  **Ultimatum_Event** (爆发事件)
+    * *逻辑*: 迫使家长此时此刻咨询的导火索。如：昨日发生的激烈争吵、离家出走、打架、学校停课通知。
+7.  **Emotional_Trigger** (情绪扳机)
+    * *逻辑*: 沟通中家长情绪最激动的点（哭泣、愤怒、颤抖）。
+
+### [第二组：阻力与障碍]
+8.  **Secret_Resistance** (隐性抗拒)
+    * *逻辑*: **阻碍成交**的心理障碍。特指：怕被家人知道买课、怕孩子知道家长在咨询、觉得课程是骗局。
+    * *排除*: 孩子的生活秘密（如抽烟/早恋）不属于此字段。
+9.  **Trust_Deficit** (信任赤字)
+    * *逻辑*: 对机构/销售/网课模式的直接质疑。如：“你们正规吗？”“之前被骗过”。
+10. **Family_Sabotage** (家庭阻力)
+    * *逻辑*: 家庭中明确的反对者或捣乱者（拆台的配偶、干涉的长辈、发病的家属）。
+    * *排除*: 客观的不幸（如家人生病/车祸）不属于此字段，除非该事件直接阻碍了家长听课。
+11. **Low_Self_Efficacy** (效能感低)
+    * *逻辑*: 家长担心**自己**学不会、坚持不下来、没时间听课。
+12. **Attribution_Barrier** (归因偏差)
+    * *逻辑*: 家长认为错在谁？（全是学校的错 / 全是手机的错 / 全是遗传的错 / 承认自己有错）。
+
+### [第三组：资源与决策]
+13. **Payer_Decision_Maker** (决策权)
+    * *逻辑*: 谁掌握财权？谁有一票否决权？是“妈妈独裁”还是“需商量”？
+14. **Hidden_Wealth_Proof** (隐形财力)
+    * *逻辑*: 寻找高消费证据。如：私立学校、出国计划、高昂学费、住别墅、高知职业（教授/医生）。
+15. **Price_Sensitivity** (价格敏感度)
+    * *逻辑*: 对价格的反应。是“只看效果不差钱”，还是“犹豫比价”、“哭穷”。
+16. **Sunk_Cost** (沉没成本)
+    * *逻辑*: 过往已投入的无效成本。如：之前报过xx辅导班、做过xx次心理咨询、花了xx万没效果。
+17. **Compensatory_Spending** (补偿心理)
+    * *逻辑*: 是否因亏欠感而通过花钱（买东西/报课）来弥补孩子。
+
+### [第四组：销售价值判断]
+18. **Expectation_Bonus** (期望范围)
+    * *逻辑*: 家长的底线（只要活着/不退学）与理想（考大学/变优秀）。
+19. **Competitor_Mindset** (竞品思维)
+    * *逻辑*: 家长是否在对比其他**解决方案**。如：特训学校、心理医生（针对孩子）、线下辅导班。
+    * *排除*: 家属的就医经历不属于此字段。
+20. **Cognitive_Stage** (认知阶段)
+    * *逻辑*: 愚昧期（修孩子） -> 觉醒期（修自己/找方法）。
+21. **Referral_Potential** (转介绍潜力)
+    * *逻辑*: 基于身份判断。重点捕捉：多孩家庭、教师/医生/教授/公务员身份（KOL潜质）、家长委员会成员。
+22. **Last_Interaction** (互动状态)
+    * *逻辑*: 通话结束时的温度。秒回/挂断/索要案例/已读不回。
+23. **Follow_up_Priority** (跟进优先级) - [重点监控字段]
+    * *逻辑*: 综合评级（S/A/B/C）。
+    * **Extraction Rule (必须使用数组逻辑)**:
+        * 如果评级为 **S/A**（通常需要痛点+财力/意向双重支撑），必须在 `evidence` 数组中分别列出这两方面（甚至三方面）的原话。
+        * **S级**: 涉及生命安全 OR (极高痛点 + 强支付能力 + 强意向)。
+        * **A级**: 有痛点 + 有支付能力。
+        * **B级**: 有痛点 + 无支付能力/犹豫。
+        * **C级**: 无痛点/无意识。
+
+# Output Format (输出格式指令)
+**强制要求1**：JSON 中必须包含 `Follow_up_Priority` 字段，否则视为无效输出。
+**强制要求2**：JSON 格式必须严格合法（逗号分隔、引号成对、括号匹配），可直接被JSON解析工具识别。
+**强制要求3**：直接输出 JSON 对象，无需 Markdown 代码块、解释性文字或额外内容。
+**强制要求4**：若文中未提及除了Follow_up_Priority字段的22个字段内容，就不要输出。
+**强制要求5**：不准将示例模板内容作为证据输出，必须从原文中找证据。
+
+JSON 结构要求：
+1.  **Key**: 仅使用上述定义中出现的英文 Key。
+2.  **Value**: 必须是 **<20字** 的短语结论。
+3.  **Evidence**: 必须是 **List<String>** (字符串数组)。
+4.  **Strict Validation (自我审查)**:
+    * 检查 `evidence` 是否包含“家长说”、“意思就是”？ -> 若有，**改为纯引用**。
+    * 检查 `evidence` 是否包含销售说的话？ -> 若有，**删除该元素**。
+    * 检查 JSON 语法是否正确？ -> 确保逗号不遗漏、括号成对。
+    * 不准将示例模板内容作为证据输出，必须从原文中找证据。
+
+**Example Output:**
+{
+  "Follow_up_Priority": {
+    "value": "A级 (痛点强+财力足)",
+    "evidence": [
+        "我是今年才确诊，他是焦虑的",       // 证据1：完整原句支撑痛点
+        "孩子现在在西工大附中上学",         // 证据2：完整原句支撑隐形财力
+        "留学基金我们已经准备好了"          // 证据3：完整原句支撑支付能力
+    ]
+  },
+  "Pain_Threshold": {
+    "value": "崩溃急救状态",
+    "evidence": [
+        "我不知道怎么来处理",
+        "一看见难了就崩溃啊就崩溃"
+    ]
+  },
+  "Trust_Deficit": {
+    "value": "质疑课程通用性",
+    "evidence": [
+        "你这些课不都是通用的吗？"
+    ]
+  }
+}"""
+    full_prompt = f"{prompt_template}\n\n### 原始通话文本\n{dialogue_text}\n\n### 请严格按照上述要求输出JSON（仅JSON，无其他内容）"
+    return full_prompt
+
+
+def clean_and_fix_json(json_str):
+    """清洗JSON格式，不补充兜底内容"""
+    try:
+        # 移除转义符、控制字符和多余空格
+        json_str = json_str.replace('\\"', '"').replace("\\'", "'")
+        json_str = re.sub(r'[\n\r\t\f\v]', '', json_str)
+        json_str = re.sub(r'\s+', ' ', json_str).strip()
+        # 修复末尾多余逗号
+        json_str = re.sub(r",\s*}", "}", json_str)
+        json_str = re.sub(r",\s*]", "]", json_str)
+        return json_str
+    except Exception as e:
+        raise RuntimeError(f"JSON清洗失败: {str(e)}") from e
+
+
+def extract_features_with_qwen(dialogue_text, file_name, output_dir="qwen_new_123"):
+    """
+    调用API提取特征并保存为JSON文件
+    :param dialogue_text: 预处理后的对话文本（字符串）
+    :param file_name: 原文件名称（用于生成输出文件名）
+    :param output_dir: 结果保存目录
+    :return: 提取的特征字典（失败则返回None）
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    prompt = build_extraction_prompt(dialogue_text)
+    
+    try:
+        response = client.chat.completions.create(
+            model=MODEL,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=TEMPERATURE,
+            max_tokens=8000
+        )
+        
+        feature_json_str = response.choices[0].message.content.strip()
+        # 提取JSON片段
+        json_match = re.search(r"\{[\s\S]*\}", feature_json_str)
+        if json_match:
+            feature_json_str = json_match.group()
+        else:
+            raise ValueError("返回内容中未找到有效JSON数据")
+        
+        # 移除代码块标记
+        if feature_json_str.startswith("```json"):
+            feature_json_str = feature_json_str[7:-3].strip()
+        elif feature_json_str.startswith("```"):
+            feature_json_str = feature_json_str[3:-3].strip()
+        
+        feature_dict = json.loads(feature_json_str)
+        
+        # 验证核心字段
+        if "Follow_up_Priority" not in feature_dict:
+            raise ValueError("返回结果缺失核心必选字段：Follow_up_Priority")
+        
+        # 生成输出文件名
+        file_base = os.path.splitext(file_name)[0]
+        json_filename = f"{file_base}.json"
+        output_path = os.path.join(output_dir, json_filename)
+        
+        # 保存文件
+        with open(output_path, "w", encoding="utf-8") as f:
+            json.dump(feature_dict, f, ensure_ascii=False, indent=2)
+        
+        print(f"处理完成：{file_name} -> {json_filename}")
+        return feature_dict
+    
+    except json.JSONDecodeError as e:
+        print(f"JSON解析失败 {file_name}：{str(e)} | 原始内容：{feature_json_str[:200]}...")
+        return None
+    except ValueError as e:
+        print(f"数据验证失败 {file_name}：{str(e)}")
+        return None
+    except Exception as e:
+        print(f"特征提取失败 {file_name}：{str(e)}")
+        return None
+
+# 批量处理函数
+def batch_process_first_200_txt(folder_path, output_dir):
+    """
+    仅处理指定文件夹下的前200个txt文件
+    :param folder_path: 待处理文件夹路径
+    :param output_dir: 结果输出目录
+    """
+    # 检查文件夹是否存在
+    if not os.path.isdir(folder_path):
+        print(f"文件夹不存在：{folder_path}")
+        return
+    
+    # 筛选出文件夹中的txt文件并按名称排序（保证处理顺序稳定）
+    txt_file_list = [
+        f for f in os.listdir(folder_path)
+        if os.path.isfile(os.path.join(folder_path, f)) and f.lower().endswith(".txt")
+    ]
+    # 按文件名排序（可选，保证每次处理顺序一致）
+    txt_file_list.sort()
+    
+    # 取前200个txt文件
+    target_files = txt_file_list[:200]
+    
+    if not target_files:
+        print(f"文件夹 {folder_path} 中无txt文件可处理")
+        return
+    
+    print(f"始处理前 {len(target_files)} 个txt文件")
+    
+    processed_count = 0
+    failed_count = 0
+    
+    for file_name in target_files:
+        file_path = os.path.join(folder_path, file_name)
+        
+        # 读取文件内容
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                dialogue_content = f.read().strip()
+            if not dialogue_content:
+                print(f"文件内容为空，跳过：{file_name}")
+                failed_count += 1
+                continue
+        except Exception as e:
+            print(f"读取文件失败 {file_name}：{str(e)}")
+            failed_count += 1
+            continue
+        
+        # 调用特征提取函数
+        result = extract_features_with_qwen(dialogue_content, file_name, output_dir)
+        if result:
+            processed_count += 1
+        else:
+            failed_count += 1
+    
+    # 输出批量处理统计结果
+    print("\n批量处理完成")
+    print(f"成功处理：{processed_count} 个文件")
+    print(f"处理失败：{failed_count} 个文件")
+    print(f"结果保存至：{os.path.abspath(output_dir)}")
+
+
+def process_single_txt(file_path, output_dir=OUTPUT_DIR):
+    """
+    处理单个TXT文件，提取特征并保存JSON
+    :param file_path: 单个TXT文件的完整路径
+    :param output_dir: JSON结果保存目录
+    """
+    # 1. 验证文件是否存在且是TXT文件
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"文件不存在：{file_path}")
+    if not file_path.lower().endswith(".txt"):
+        raise ValueError(f"不是TXT文件：{file_path}")
+    if not os.path.isfile(file_path):
+        raise IsADirectoryError(f"这是文件夹，不是文件：{file_path}")
+
+    # 2. 读取TXT文件内容
+    print(f"正在读取文件：{file_path}")
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            dialogue_content = f.read().strip()
+        if not dialogue_content:
+            raise ValueError("文件内容为空")
+        print(f"成功读取文件（字符数：{len(dialogue_content)}）")
+    except Exception as e:
+        raise RuntimeError(f"读取文件失败：{str(e)}") from e
+
+    # 3. 构建提示词并调用API
+    prompt = build_extraction_prompt(dialogue_content)
+    try:
+        print("正在调用API提取特征...")
+        response = client.chat.completions.create(
+            model=MODEL,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=TEMPERATURE,
+            max_tokens=8000,
+            timeout=30
+        )
+    except Exception as e:
+        raise RuntimeError(f"API调用失败：{str(e)}") from e
+
+    # 4. 提取并清洗JSON
+    feature_json_str = response.choices[0].message.content.strip()
+    json_match = re.search(r"\{[\s\S]*\}", feature_json_str)
+    if not json_match:
+        raise RuntimeError(f"API返回无有效JSON：{feature_json_str[:200]}...")
+    cleaned_json = clean_and_fix_json(json_match.group())
+
+    # 5. 解析并验证JSON
+    try:
+        parsed_dict = json.loads(cleaned_json)
+    except json.JSONDecodeError as e:
+        raise RuntimeError(f"JSON解析失败：{str(e)} | 清洗后内容：{cleaned_json[:500]}") from e
+
+    # 验证核心字段
+    if "Follow_up_Priority" not in parsed_dict:
+        raise RuntimeError("核心字段Follow_up_Priority缺失")
+    fu_prio = parsed_dict["Follow_up_Priority"]
+    if not isinstance(fu_prio, dict) or "value" not in fu_prio or "evidence" not in fu_prio:
+        raise RuntimeError("Follow_up_Priority格式错误（需包含value和evidence）")
+    if not isinstance(fu_prio["evidence"], list):
+        raise RuntimeError("evidence必须是数组类型")
+    if len(str(fu_prio["value"])) >= 20:
+        raise RuntimeError(f"value超20字限制：{fu_prio['value']}")
+
+    # 6. 保存JSON结果
+    os.makedirs(output_dir, exist_ok=True)
+    file_name = os.path.basename(file_path)
+    json_file_name = f"{os.path.splitext(file_name)[0]}"
+    json_save_path = os.path.join(output_dir, json_file_name)
+
+    try:
+        with open(json_save_path, "w", encoding="utf-8") as f:
+            json.dump(parsed_dict, f, ensure_ascii=False, indent=2)
+        print(f"处理完成！JSON保存至：{json_save_path}")
+        return parsed_dict
+    except Exception as e:
+        raise RuntimeError(f"保存JSON失败：{str(e)}") from e
+
+
+async def process_single(content : str):
+    # 3. 构建提示词并调用API
+    prompt = build_extraction_prompt(content)
+    try:
+        print("正在调用API提取特征...")
+        response = client.chat.completions.create(
+            model=MODEL,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=TEMPERATURE,
+            max_tokens=8000,
+            timeout=30
+        )
+    except Exception as e:
+        raise RuntimeError(f"API调用失败：{str(e)}") from e
+
+    # 4. 提取并清洗JSON
+    feature_json_str = response.choices[0].message.content.strip()
+    json_match = re.search(r"\{[\s\S]*\}", feature_json_str)
+    if not json_match:
+        raise RuntimeError(f"API返回无有效JSON：{feature_json_str[:200]}...")
+    cleaned_json = clean_and_fix_json(json_match.group())
+
+    # 5. 解析并验证JSON
+    try:
+        parsed_dict = json.loads(cleaned_json)
+    except json.JSONDecodeError as e:
+        raise RuntimeError(f"JSON解析失败：{str(e)} | 清洗后内容：{cleaned_json[:500]}") from e
+
+    # 验证核心字段
+    if "Follow_up_Priority" not in parsed_dict:
+        raise RuntimeError("核心字段Follow_up_Priority缺失")
+    fu_prio = parsed_dict["Follow_up_Priority"]
+    if not isinstance(fu_prio, dict) or "value" not in fu_prio or "evidence" not in fu_prio:
+        raise RuntimeError("Follow_up_Priority格式错误（需包含value和evidence）")
+    if not isinstance(fu_prio["evidence"], list):
+        raise RuntimeError("evidence必须是数组类型")
+    if len(str(fu_prio["value"])) >= 20:
+        raise RuntimeError(f"value超20字限制：{fu_prio['value']}")
+    
+    return parsed_dict
+
+if __name__ == "__main__":
+    # 要处理的源文件夹路径
+    target_folder = "./time_12_1/data_200" 
+    
+    # 执行批量处理：仅处理前200个txt文件，输出到
+    batch_process_first_200_txt(
+        folder_path=target_folder,
+        output_dir=OUTPUT_DIR
+    )
+    # SINGLE_TXT_PATH = "./qwen/cdb7d561-975a-431e-86d4-9b3ddc714f73.txt"
+    
+    # try:
+    #     # 执行单个文件处理
+    #     process_single_txt(file_path=SINGLE_TXT_PATH)
+    # except Exception as e:
+    #     print(f"\n处理失败：{str(e)}")
+

inference.py

@@ -59,8 +59,11 @@ class InferenceEngine:
         批量推理函数，输入为 JSON 字符串列表，输出为包含转换概率的字典列表。为防止OOM，列表最大长度为8。
         请注意Json文件中的词条数必须大于等于10.
         """
-        assert len(json_list) <= 8, "单次输入json文件数量不可超过8。"
+        # print(111111)
-        id2feature = extract_json_data(json_list)     # id2feature
+        assert len(json_list) <= 10, "单次输入json文件数量不可超过8。"
+        id2feature = extract_json_data(json_list)  
+        print(json.dumps(id2feature ,indent=2 ,ensure_ascii=False))
+        # id2feature
 
         message_list = []
         for id, feature in id2feature.items():
@@ -93,9 +96,12 @@ class InferenceEngine:
         probs = torch.softmax(outputs_float, dim=1)  # probs: [B, 2]
         # 转换为CPU的numpy数组，再转列表（每个样本对应2个类别的概率）
         probs = probs.cpu().numpy().tolist()
+        probs = [p[1] for p in probs]  # 只保留类别1的概率
 
-        # 返回格式：labels是每个样本的分类标签列表，probs是每个样本的类别概率列表
+        # 3. 计算置信度
-        return {"labels": preds, "probs": probs}
+        confidence = [abs(p - 0.5) * 2 for p in probs]
+        # 返回格式：labels是每个样本的分类标签列表，probs是每个样本的类别概率列表，confidence是每个样本的置信度列表
+        return {"labels": preds, "probs": probs, "confidence": confidence}
 
     def inference_sample(self, json_path: str) -> dict:
         """
@@ -104,6 +110,50 @@ class InferenceEngine:
         """
         return self.inference_batch([json_path])
     
+    def inference(
+        self,
+        featurs : dict[str ,dict]
+    ):
+        assert len(featurs) <= 10, "单次输入json文件数量不可超过8。"
+        message_list = []
+        for id, feature in featurs.items():
+            messages = self.formatter.get_llm_prompt(feature)
+            message_list.append(messages)
+
+        inputs = self.tokenizer.apply_chat_template(
+            message_list, 
+            tokenize=False, 
+            add_generation_prompt=True, 
+            enable_thinking=False
+        )
+        
+        model_inputs = self.tokenizer(
+            inputs,
+            padding=True,
+            truncation=True,
+            max_length=2048,
+            return_tensors="pt"
+        ).to(self.device)
+
+        with torch.inference_mode():
+            with torch.amp.autocast(device_type=self.device, dtype=torch.bfloat16):
+                outputs = self.model(model_inputs)
+
+        # 1. 计算分类标签（argmax）
+        preds = torch.argmax(outputs, dim=1).cpu().numpy().tolist()
+
+        # 2. 计算softmax概率（核心修正：转CPU、转numpy、转列表，解决Tensor序列化问题）
+        outputs_float = outputs.float()  # 转换为 float32 避免精度问题
+        probs = torch.softmax(outputs_float, dim=1)  # probs: [B, 2]
+        # 转换为CPU的numpy数组，再转列表（每个样本对应2个类别的概率）
+        probs = probs.cpu().numpy().tolist()
+        probs = [p[1] for p in probs]  # 只保留类别1的概率
+
+        # 3. 计算置信度
+        confidence = [abs(p - 0.5) * 2 for p in probs]
+        # 返回格式：labels是每个样本的分类标签列表，probs是每个样本的类别概率列表，confidence是每个样本的置信度列表
+        return {"labels": preds, "probs": probs, "confidence": confidence}
+
 if __name__ == "__main__":
     # 配置参数
     backbone_dir = "Qwen3-1.7B"
@@ -111,91 +161,3 @@ if __name__ == "__main__":
     device = "cuda"
     
     engine = InferenceEngine(backbone_dir, ckpt_path, device)
-
-    from data_process import extract_json_files
-    import random
-    
-    # 获取成交和未成交的json文件路径
-    deal_files = extract_json_files("deal")
-    not_deal_files = extract_json_files("not_deal")
-    
-    def filter_json_files_by_key_count(files: List[str], min_keys: int = 10) -> List[str]:
-        """
-        过滤出JSON文件中字典键数量大于等于指定数量的文件
-        
-        Args:
-            files: JSON文件路径列表
-            min_keys: 最小键数量要求，默认为10
-        
-        Returns:
-            符合条件的文件路径列表
-        """
-        valid_files = []
-        
-        for file_path in files:
-            try:
-                with open(file_path, 'r', encoding='utf-8') as f:
-                    data = json.load(f)
-                
-                # 检查是否为字典且键数量是否符合要求
-                if isinstance(data, dict) and len(data) >= min_keys:
-                    valid_files.append(file_path)
-                else:
-                    print(f"跳过文件 {os.path.basename(file_path)}: 键数量不足 ({len(data)} < {min_keys})")
-            except Exception as e:
-                print(f"读取文件 {file_path} 时出错: {e}")
-        
-        return valid_files
-
-    deal_files_filtered = filter_json_files_by_key_count(deal_files, min_keys=10)
-    not_deal_files_filtered = filter_json_files_by_key_count(not_deal_files, min_keys=10)
-
-    num_samples = 8
-    
-    # 计算每类需要选取的数量
-    num_deal_needed = min(4, len(deal_files_filtered))  # 最多选4个成交文件
-    num_not_deal_needed = min(4, len(not_deal_files_filtered))  # 最多选4个未成交文件
-    
-    # 如果某类文件不足，从另一类补足
-    if num_deal_needed + num_not_deal_needed < num_samples:
-        if len(deal_files_filtered) > num_deal_needed:
-            num_deal_needed = min(num_samples, len(deal_files_filtered))
-        elif len(not_deal_files_filtered) > num_not_deal_needed:
-            num_not_deal_needed = min(num_samples, len(not_deal_files_filtered))
-    
-    # 随机选取文件
-    selected_deal_files = random.sample(deal_files_filtered, min(num_deal_needed, len(deal_files_filtered))) if deal_files_filtered else []
-    selected_not_deal_files = random.sample(not_deal_files_filtered, min(num_not_deal_needed, len(not_deal_files_filtered))) if not_deal_files_filtered else []
-    
-    # 合并选中的文件
-    selected_files = selected_deal_files + selected_not_deal_files
-    
-    # 如果总数不足8个，尝试从原始文件中随机选取补足
-    if len(selected_files) < num_samples:
-        all_files = deal_files + not_deal_files
-        # 排除已选的文件
-        remaining_files = [f for f in all_files if f not in selected_files]
-        additional_needed = num_samples - len(selected_files)
-        if remaining_files:
-            additional_files = random.sample(remaining_files, min(additional_needed, len(remaining_files)))
-            selected_files.extend(additional_files)
-    
-    true_labels = []
-    for i, file_path in enumerate(selected_files):
-        folder_type = "未成交" if "not_deal" in file_path else "成交"
-        true_labels.append(folder_type)
-    
-    # 使用inference_batch接口进行批量推理
-    if selected_files:
-        print("\n开始批量推理...")
-        try:
-            batch_result = engine.inference_batch(selected_files)
-            print(batch_result)
-            print(true_labels)
-            
-        except Exception as e:
-            print(f"推理过程中出错: {e}")
-    else:
-        print("未找到符合条件的文件进行推理")
-    
-    print("\n推理端口测试完成!")

main.py

@@ -1,6 +1,65 @@
-def main():
+from feature_extraction import process_single
-    print("Hello from deal-classification!")
+from inference import InferenceEngine
+from services.mongo import voice_collection
+import json,uuid
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+backbone_dir = "Qwen3-1.7B"
+ckpt_path = "best_ckpt.pth"
+device = "cuda"
+
+engine = InferenceEngine(backbone_dir, ckpt_path, device)
+
+
+
+async def get_customer_record():
+    cursor = voice_collection.find({
+        "tag": "20分钟通话",
+        "matched_contacts": {
+            "$elemMatch": {
+                "wecom_id": {"$exists": True, "$ne": ""}
+            }
+        }
+    }).sort([('_id', -1)]).limit(24)
+    return await cursor.to_list(length=1)
+
+
+async def main():
+     records = await get_customer_record()     
+     for record in records:
+        # print(len(record["text_content"]))
+        data = await process_single(record["text_content"][:10000])
+        # print(json.dumps(data, indent=2 , ensure_ascii=False))
+        temp = {}
+        res = {}
+        for key ,value in data.items():
+            temp[key] = value.get("value") or ""
+        res[uuid.uuid4().hex] = temp
+        print(engine.inference(res))
+    
+    
+    
+app = FastAPI()
+
+class Predictbody(BaseModel):
+    content : str
+    
+@app.post("/predict")
+async def endpoint(body : Predictbody):  
+    data = await process_single(body.content[:10000])
+    temp = {}
+    res = {}
+    for key ,value in data.items():
+        temp[key] = value.get("value") or ""
+    res[uuid.uuid4().hex] = temp
+    return {
+        "feature" : data,
+        "predict" : engine.inference(res)
+    } 
+
         
 
 if __name__ == "__main__":
-    main()
+    import asyncio
+    asyncio.run(main())

pyproject.toml

@@ -5,8 +5,13 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
+    "fastapi>=0.128.0",
     "matplotlib>=3.10.8",
+    "motor>=3.7.1",
+    "openai>=2.16.0",
     "pandas>=3.0.0",
+    "python-dotenv>=1.2.1",
     "torch>=2.10.0",
     "transformers>=5.0.0",
+    "uvicorn>=0.40.0",
 ]

services/mongo.py

@@ -0,0 +1,32 @@
+import datetime
+import os
+from typing import Dict, List, Optional
+
+from bson import ObjectId
+from motor.motor_asyncio import (
+    AsyncIOMotorClient,
+    AsyncIOMotorCollection,
+    AsyncIOMotorDatabase,
+)
+
+MONGO_URI = os.getenv("MONGO_URI")
+
+MongoQueryParam = int | str | None | bool | datetime.datetime
+
+MongoQueryListParam = (
+    List[int] | List[str] | List[datetime.datetime] | List[bool] | List[None]
+)
+
+MongoQueryDictParam = Dict[
+    str, MongoQueryListParam | MongoQueryParam | "MongoQueryDictParam"
+]
+
+MongoQuery = Dict[
+    str, MongoQueryDictParam | MongoQueryParam | MongoQueryListParam | "MongoQuery"
+]
+
+# 明确的类型注解
+client: AsyncIOMotorClient = AsyncIOMotorClient(host=MONGO_URI)
+db: AsyncIOMotorDatabase = client["TriCore"]
+voice_collection: AsyncIOMotorCollection = db["voice_insight"]
+

uv.lock

@@ -10,6 +10,24 @@ resolution-markers = [
     "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.12.1"
@@ -143,18 +161,61 @@ name = "deal-classification"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "fastapi" },
     { name = "matplotlib" },
+    { name = "motor" },
+    { name = "openai" },
     { name = "pandas" },
+    { name = "python-dotenv" },
     { name = "torch" },
     { name = "transformers" },
+    { name = "uvicorn" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "fastapi", specifier = ">=0.128.0" },
     { name = "matplotlib", specifier = ">=3.10.8" },
+    { name = "motor", specifier = ">=3.7.1" },
+    { name = "openai", specifier = ">=2.16.0" },
     { name = "pandas", specifier = ">=3.0.0" },
+    { name = "python-dotenv", specifier = ">=1.2.1" },
     { name = "torch", specifier = ">=2.10.0" },
     { name = "transformers", specifier = ">=5.0.0" },
+    { name = "uvicorn", specifier = ">=0.40.0" },
+]
+
+[[package]]
+name = "distro"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
+]
+
+[[package]]
+name = "dnspython"
+version = "2.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8c/8b/57666417c0f90f08bcafa776861060426765fdb422eb10212086fb811d26/dnspython-2.8.0.tar.gz", hash = "sha256:181d3c6996452cb1189c4046c61599b84a5a86e099562ffde77d26984ff26d0f", size = 368251, upload-time = "2025-09-07T18:58:00.022Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/5a/18ad964b0086c6e62e2e7500f7edc89e3faa45033c71c1893d34eed2b2de/dnspython-2.8.0-py3-none-any.whl", hash = "sha256:01d9bbc4a2d76bf0db7c1f729812ded6d912bd318d3b1cf81d30c0f845dbf3af", size = 331094, upload-time = "2025-09-07T18:57:58.071Z" },
+]
+
+[[package]]
+name = "fastapi"
+version = "0.128.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-doc" },
+    { name = "pydantic" },
+    { name = "starlette" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/52/08/8c8508db6c7b9aae8f7175046af41baad690771c9bcde676419965e338c7/fastapi-0.128.0.tar.gz", hash = "sha256:1cc179e1cef10a6be60ffe429f79b829dce99d8de32d7acb7e6c8dfdf7f2645a", size = 365682, upload-time = "2025-12-27T15:21:13.714Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/05/5cbb59154b093548acd0f4c7c474a118eda06da25aa75c616b72d8fcd92a/fastapi-0.128.0-py3-none-any.whl", hash = "sha256:aebd93f9716ee3b4f4fcfe13ffb7cf308d99c9f3ab5622d8877441072561582d", size = 103094, upload-time = "2025-12-27T15:21:12.154Z" },
 ]
 
 [[package]]
@@ -316,6 +377,57 @@ wheels = [
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 ]
 
+[[package]]
+name = "jiter"
+version = "0.12.0"
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