docker 前最后一版，还没有解决日志和文件过多的问题

api_send_model.py

@@ -0,0 +1,15 @@
+import requests
+
+# 定义上传文件的URL
+url = "http://localhost:3397/upload_model/"
+
+# 打开要上传的模型文件
+file_path = "train_api/train_model_20240605_172031.pth"  # 替换为你要上传的模型文件路径
+files = {'file': open(file_path, 'rb')}
+
+# 发送POST请求上传文件
+response = requests.post(url, files=files)
+
+# 打印响应结果
+print(response.status_code)
+print(response.json())

config/config.yaml

@@ -1,6 +1,6 @@
 #---设备配置---#
-# device: cpu 
-device: cuda
+device: cpu 
+# device: cuda
 
 #---训练配置---#
 n_epochs: 150
@@ -12,7 +12,9 @@ data_train: train_val
 early_stop_patience: 50
 gamma: 0.98
 step_size: 10
-experiments_count: 1
+experimental_mode: false
+experiments_count: 50
+replace_model: true  # 是否替换现有模型
 
 #---检测和推理配置---#
 # 检测和推理使用模型路径

psy_api.py

@@ -2,10 +2,11 @@ import os
 import time
 import datetime
 import logging
+import shutil
 import uvicorn
 import yaml
 import numpy as np
-from fastapi import FastAPI, Request
+from fastapi import FastAPI, Request, File, UploadFile
 from pydantic import BaseModel
 from typing import List
 import atexit
@@ -19,6 +20,13 @@ app = FastAPI()
 # 控制是否打印的宏定义
 PRINT_LOG = True
 
+# 初始化配置文件
+config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "config/config.yaml"))
+
+# 初始化日志配置
+log_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "logfile.log"))
+logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+
 def log_print(message):
     logging.info(message)
     if PRINT_LOG:
@@ -49,8 +57,17 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# 初始化配置文件
-config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "config/config.yaml"))
+# 定义一个函数来动态更新日志处理器
+def update_log_handler(log_path):
+    logger = logging.getLogger()
+    # 移除所有现有的处理器
+    for handler in logger.handlers[:]:
+        logger.removeHandler(handler)
+    # 添加新的文件处理器
+    file_handler = logging.FileHandler(log_path)
+    file_handler.setLevel(logging.INFO)
+    file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
+    logger.addHandler(file_handler)
 
 # 定义训练接口
 @app.post("/train/")
@@ -81,11 +98,11 @@ async def train_model(request: Request, features_list: List[Features]):
 
     # 添加模型保存路径
     model_path = os.path.abspath(os.path.join(static_dir, f"train_model_{now}.pth"))
-    config['model_path'] = model_path
+    config['train_model_path'] = model_path
 
     # 配置日志
     log_path = os.path.abspath(os.path.join(static_dir, f"train_log_{now}.log"))
-    logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+    update_log_handler(log_path)
 
     # 配置训练和验证结果图片路径
     train_process_path = os.path.abspath(os.path.join(static_dir, f"train_progress_img_{now}.png"))
@@ -103,7 +120,7 @@ async def train_model(request: Request, features_list: List[Features]):
     list_precision = []
     list_recall = []
     list_f1 = []
-    train_times = 1 if config['data_train'] == 'all' else config["experiments_count"]
+    train_times = 1 if config['experimental_mode'] == False else config["experiments_count"]
     for _ in range(train_times):
         avg_f1, wrong_percentage, precision, recall, f1 = ml_model.train_detect()
         list_avg_f1.append(avg_f1)
@@ -120,6 +137,11 @@ async def train_model(request: Request, features_list: List[Features]):
     end_time = time.time()  # 记录结束时间
     log_print("预测耗时: " + str(end_time - start_time) + " 秒")  # 打印执行时间
 
+    # 替换现有检测模型
+    if(config["replace_model"] == True):
+        shutil.copyfile(config["train_model_path"], config["model_path"])
+        log_print(f"Model file has been copied from {config['train_model_path']} to {config['model_path']}")
+
     # 保证日志写到文件
     atexit.register(flush_log)
 
@@ -127,6 +149,8 @@ async def train_model(request: Request, features_list: List[Features]):
     model_file_url = f"{request.base_url}train_api/train_model_{now}.pth"
     log_file_url = f"{request.base_url}train_api/train_log_{now}.log"
     data_file_url = f"{request.base_url}train_api/train_feature_label_weighted_{now}.xlsx"
+    train_process_img_url = f"{request.base_url}train_api/train_progress_img_{now}.png"
+    evaluate_result_img_url = f"{request.base_url}train_api/evaluate_result_img_{now}.png"
 
     # 返回分类结果和模型文件
     return {
@@ -139,7 +163,9 @@ async def train_model(request: Request, features_list: List[Features]):
         "data_file": {
             "model_file_url": model_file_url,
             "log_file_url": log_file_url,
-            "data_file_url": data_file_url
+            "data_file_url": data_file_url,
+            "train_process_img_url": train_process_img_url,
+            "evaluate_result_img_url": evaluate_result_img_url
         }
     }
 
@@ -176,7 +202,7 @@ async def evaluate_model(request: Request, features_list: List[Features]):
 
     # 配置日志
     log_path = os.path.abspath(os.path.join(static_dir, f"evaluate_log_{now}.log"))
-    logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+    update_log_handler(log_path)
 
     # 特征和标签
     X = feature_label_weighted[config['feature_names']].values
@@ -243,7 +269,7 @@ async def inference_model(request: Request, features_list: List[Features]):
 
     # 配置日志
     log_path = os.path.abspath(os.path.join(static_dir, f"inference_log_{now}.log"))
-    logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+    update_log_handler(log_path)
 
     # 特征和标签
     X = feature_label_weighted[config['feature_names']].values
@@ -267,9 +293,28 @@ async def inference_model(request: Request, features_list: List[Features]):
     # 返回预测结果
     return PredictionResult(predictions=predictions)
 
+# 定义模型上传接口
+@app.post("/upload_model/")
+async def upload_model(file: UploadFile = File(...)):
+    # 创建模型存放文件夹
+    models_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "models"))
+    os.makedirs(models_dir, exist_ok=True)
+
+    # 保存模型文件
+    file_path = os.path.join(models_dir, "psy.pth")
+    with open(file_path, "wb") as buffer:
+        buffer.write(await file.read())
+
+    return {"message": "模型上传成功", "file_path": file_path}
+
 
 # 以下是fastapi启动配置
 if __name__ == "__main__":
+    # 获取当前时间并格式化为字符串
+    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    # 打印程序启动时间
+    log_print(f"Program started at {current_time}")
+
     name_app = os.path.basename(__file__)[0:-3]  # Get the name of the script
     log_config = {
         "version": 1,
@@ -289,11 +334,14 @@ if __name__ == "__main__":
     static_dir_train = os.path.abspath(os.path.join(os.path.dirname(__file__), "train_api"))  # 设置模型文件和配置文件的存放目录，和本py同级
     static_dir_evaluate = os.path.abspath(os.path.join(os.path.dirname(__file__), "evaluate_api"))
     static_dir_inference = os.path.abspath(os.path.join(os.path.dirname(__file__), "inference_api"))
+    static_dir_models = os.path.abspath(os.path.join(os.path.dirname(__file__), "models"))
     os.makedirs(static_dir_train, exist_ok=True)
     os.makedirs(static_dir_evaluate, exist_ok=True)
     os.makedirs(static_dir_inference, exist_ok=True)
+    os.makedirs(static_dir_models, exist_ok=True)
     # 同级目录下的static文件夹
     app.mount("/train_api", StaticFiles(directory=static_dir_train), name="static_dir_train")
     app.mount("/evaluate_api", StaticFiles(directory=static_dir_evaluate), name="static_dir_evaluate")
     app.mount("/inference_api", StaticFiles(directory=static_dir_inference), name="static_dir_inference")
+    app.mount("/models", StaticFiles(directory=static_dir_models), name="static_dir_models")
     uvicorn.run(app, host="0.0.0.0", port=3397, reload=False)

train_api.yaml

@@ -1,61 +0,0 @@
-#---设备配置---#
-device: cpu 
-#device: cuda
-
-#---训练配置---#
-n_epochs: 150
-batch_size: 16
-learning_rate: 0.001
-nc: 4
-use_infer_as_val: true
-#data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
-data_train: all
-early_stop_patience: 50
-gamma: 0.98
-step_size: 10
-experiments_count: 50
-
-#---训练结果---#
-# 日志路径
-log_path: results/training.log
-# 训练过程统计图路径
-train_process_path: results/training_progress.png
-# 训练结果统计图路径
-train_result_path: results/training_result.png
-# 训练模型路径
-model_path: results/psychology.pth
-# 用于测试的部分数据路径
-infer_path: results/infer.xlsx
-
-#---训练原始数据---#
-# 训练样本数据路径配置
-data_path: data_processed/feature_label_weighted.xlsx
-
-#---样本特征---#
-# 标签名称
-label_name: 类别
-# 特征名称
-feature_names:
-  - "强迫症状数字化"
-  - "人际关系敏感数字化" 
-  - "抑郁数字化"
-  - "多因子症状"
-  - "母亲教养方式数字化"
-  - "父亲教养方式数字化"
-  - "自评家庭经济条件数字化"
-  - "有无心理治疗（咨询）史数字化"
-  - "学业情况数字化"
-  - "出勤情况数字化"
-# 定义特征权重列表
-feature_weights:
-  - 0.135
-  - 0.085
-  - 0.08
-  - 0.2
-  - 0.09
-  - 0.09
-  - 0.06
-  - 0.06
-  - 0.08
-  - 0.12
-

utils/common.py

@@ -247,7 +247,7 @@ class MLModel:
         best_val_f1, best_val_recall, best_val_precision, best_epoch, best_model = self.train_model(train_loader, val_loader, criterion, optimizer, scheduler)
 
         # Save the best model
-        self.save_model(self.config['model_path'])
+        self.save_model(self.config['train_model_path'])
 
         log_print(f"Best Validation F1 Score (Macro): {best_val_f1:.4f}")
         log_print(f"Best Validation Recall (Macro): {best_val_recall:.4f}")