修改response

house_price/house_price_predcition.py

@@ -5,15 +5,19 @@ import numpy as np
 from sklearn.model_selection import train_test_split
 from logzero import logger
 import os
+
 current_path = os.path.dirname(__file__)
 
+
 def load_data():
     logger.info(f"读取本地数据")
     logger.info(current_path)
     train_data = pd.read_csv(f'{current_path}/data/train.csv')
-    train_data.drop(train_data[(train_data["GrLivArea"]>4000)&(train_data["SalePrice"]<300000)].index,inplace=True)#pandas 里面的条件索引
+    train_data.drop(train_data[(train_data["GrLivArea"] > 4000) & (train_data["SalePrice"] < 300000)].index,
+                    inplace=True)  # pandas 里面的条件索引
     return train_data
 
+
 def load_model():
     logger.info(f"读取本地模型")
     model = xgb.XGBModel()
@@ -43,7 +47,8 @@ def preprocessing(local_train: pd.DataFrame, new_data: pd.DataFrame):
     all_data.loc[na_index, 'GarageYrBlt'] = None
     all_data.GarageYrBlt.fillna(all_data.YearBuilt, inplace=True)
 
-    cols1 = ["GarageQual", "GarageCond", "GarageFinish", "GarageType", "BsmtExposure", "BsmtCond", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "MasVnrType"]
+    cols1 = ["GarageQual", "GarageCond", "GarageFinish", "GarageType", "BsmtExposure", "BsmtCond", "BsmtQual",
+             "BsmtFinType2", "BsmtFinType1", "MasVnrType"]
     for col in cols1:
         all_data[col].fillna("None", inplace=True)
 
@@ -53,11 +58,13 @@ def preprocessing(local_train: pd.DataFrame, new_data: pd.DataFrame):
         all_data[col].fillna(0, inplace=True)
     all_data["LotFrontage"].fillna(np.mean(all_data["LotFrontage"]), inplace=True)
 
-    cols3 = ["MSZoning", "BsmtFullBath", "BsmtHalfBath", "Utilities", "Functional", "Electrical", "KitchenQual", "SaleType","Exterior1st", "Exterior2nd"]
+    cols3 = ["MSZoning", "BsmtFullBath", "BsmtHalfBath", "Utilities", "Functional", "Electrical", "KitchenQual",
+             "SaleType", "Exterior1st", "Exterior2nd"]
     for col in cols3:
         all_data[col].fillna(all_data[col].mode()[0], inplace=True)
 
-    numeric_cols = [x for x in all_data.select_dtypes(exclude=['object']).columns.tolist() if x != 'Id' and x != 'SalePrice']
+    numeric_cols = [x for x in all_data.select_dtypes(exclude=['object']).columns.tolist() if
+                    x != 'Id' and x != 'SalePrice']
     object_cols = [x for x in all_data.select_dtypes(include=['object']).columns.tolist()]
 
     for col in numeric_cols:
@@ -67,6 +74,7 @@ def preprocessing(local_train: pd.DataFrame, new_data: pd.DataFrame):
     dataset = pd.get_dummies(all_data, columns=object_cols)
     return dataset
 
+
 def build_dataset(dataset):
     dataset.SalePrice = np.log1p(dataset.SalePrice)
     train = dataset[~dataset.SalePrice.isna()].copy()
@@ -84,7 +92,8 @@ def build_dataset(dataset):
 
 def build_model(dtrain, dvalid, watchlist, num_iter=5000, early_stop=200, **params):
     logger.info('开始本地建模')
-    model = xgb.train(params, dtrain, evals=watchlist, num_boost_round=num_iter, early_stopping_rounds=early_stop, verbose_eval=True)
+    model = xgb.train(params, dtrain, evals=watchlist, num_boost_round=num_iter, early_stopping_rounds=early_stop,
+                      verbose_eval=True)
     return model
 
 

ocean_wave/wave_height_mlp.py

@@ -1,4 +1,5 @@
 import os
+
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 import tensorflow as tf
 import numpy as np
@@ -12,6 +13,7 @@ loss = []
 jiance = []
 current_path = os.path.dirname(__file__)
 
+
 def readData(filePath):
     data = pd.read_csv(filePath, header=0)
     x = data.iloc[:len(data), 5:12].values
@@ -19,11 +21,13 @@ def readData(filePath):
 
     return x, y
 
+
 # 标准化函数
 def Z_ScoreNormalization(x, mean, sigma):
     x = (x - mean) / sigma
     return x
 
+
 # 特征标准化
 def featureScore(x):
     for i in range(7):
@@ -34,6 +38,7 @@ def featureScore(x):
 
     return x
 
+
 # 划分数据集
 def dataDivision(x, y, train_scale, val_scale):
     train_volumn = int(len(x) * train_scale)
@@ -52,6 +57,7 @@ def dataDivision(x, y, train_scale, val_scale):
 
     return x_train, x_val, x_test, y_train, y_val, y_test
 
+
 # 创建模型
 def createModel(neure, activation, learning_rate, loss):
     model = tf.keras.models.Sequential([
@@ -63,21 +69,25 @@ def createModel(neure, activation, learning_rate, loss):
 
     return model
 
+
 # 训练模型
 def trainModel(model, x_train, y_train, x_val, y_val, epochs):
     history = LossHistory()
 
-    model.fit(x_train, y_train, batch_size=32, epochs=epochs, validation_data=(x_val, y_val), validation_freq=1, callbacks=[history])
+    model.fit(x_train, y_train, batch_size=32, epochs=epochs, validation_data=(x_val, y_val), validation_freq=1,
+              callbacks=[history])
 
     model.summary()
 
     return model
 
+
 # 模型预测
 def predictModel(x_test, model):
     predicted_data = model.predict(x_test)
     return predicted_data
 
+
 # 训练
 def train(csv_file, neure, activation, learning_rate, loss, epochs):
     x, y = readData(csv_file)
@@ -98,21 +108,25 @@ def train(csv_file, neure, activation, learning_rate, loss, epochs):
     # model.save(os.path.abspath("./appweb/self_model/ocean_wave_mlp.h5"))
     return model, mse, mae, r
 
+
 def drawLoss(logs):
     loss.append(logs['loss'])
     print(loss[-1])
 
+
 def load_model():
     logger.info(f"{current_path}/pretrain_models/ocean_wave_mlp.h5")
     return keras_load_model(f"{current_path}/pretrain_models/ocean_wave_mlp.h5")
 
+
 class LossHistory(tf.keras.callbacks.Callback):
     def on_epoch_end(self, epoch, logs={}):
         # print(logs['loss'])
         drawLoss(logs)
         # return logs['loss'], logs['mae']
 
-def predict_wave_height(csv_file, num_units:int, activation:str, lr:float, loss:str, epochs:int, x_test:list):
+
+def predict_wave_height(csv_file, num_units: int, activation: str, lr: float, loss: str, epochs: int, x_test: [np.ndarray, list]):
     """_summary_
 
     Args:

prophet_predict/prophet_predict.py

@@ -4,6 +4,7 @@ import datetime as dt
 from get_holiday_cn.client import getHoliday
 from logzero import logger
 
+
 def run_prophet(data: pd.DataFrame, period: int = 1, freq: str = 'D'):
     """_summary_
 

run.py

@@ -9,10 +9,12 @@ from house_price.house_price_predcition import run_boston_price
 from ocean_wave.wave_height_mlp import predict_wave_height
 from prophet_predict.prophet_predict import run_prophet
 
+TEXT = "text"
 app = Flask(__name__)
 
 @app.route('/house_price', methods=["POST"])
 def predict_price():
+    resp_info = dict()
     if request.method == 'POST':
         eta = request.form.get('eta', 0.05)
         max_depth = request.form.get('max_depth', 10)
@@ -22,7 +24,6 @@ def predict_price():
         early_stopping_rounds = int(request.form.get('early_stopping_rounds', 200))
         train_data = request.files.get('train_data', None)
         test_data = request.files.get('test_data', None)
-        resp = make_response()
         logger.info(train_data)
         params = {
             "eta": float(eta),
@@ -34,9 +35,10 @@ def predict_price():
             train_data = None
         else:
             train_data = pd.read_csv(train_data)
-        if test_data is None:
+        if test_data is None or pd.read_csv(test_data).shape[0] == 0:
-            resp.status_code = '406'
+            resp_info["msg"] = "测试数据为空"
-            resp.response = json.dumps({"text": "test data is None"})
+            resp_info["code"] = 406
+        else:
             test_data = pd.read_csv(test_data)
             try:
                 if train_data is None:
@@ -45,19 +47,20 @@ def predict_price():
                     rst = run_boston_price(test_data, train_data, num_boost_round, early_stopping_rounds, **params)
             except Exception as e:
                 logger.error(f"Error: {e}")
-            resp.status_code = '406'
+                resp_info["msg"] = str(e)
-            resp.response = json.dumps({'text': str(e)})
+                resp_info["code"] = 406
-            return resp
-        resp.status_code=200
-        resp.response = json.dumps({"Id": rst["Id"].values.tolist(), "price": rst["SalePrice"].values.tolist()})
-        return resp
             else:
-        resp.status_code=405
+                resp_info["code"] = 200
+                resp_info["data"] = rst.to_csv()
+                resp_info["dtype"] = "csv"
+    resp = make_response(json.dumps(resp_info))
+    resp.status_code = 200
     return resp
 
 
 @app.route('/ocean_wave_height', methods=["POST"])
 def predict_height():
+    resp_info = dict()
     if request.method == 'POST':
         num_units = int(request.form.get('num_units', 8))
         activation = request.form.get('activation', 'relu')
@@ -75,7 +78,6 @@ def predict_height():
         x_test = [WVHT_1, WDIR_1, WSPD_1, WDIR_2, WSPD_2, WDIR, WSPD]
         x_test = np.array([x_test])
         logger.info(f"test data: {x_test}")
-        resp = make_response()
         if not train_data:
             train_data = None
         else:
@@ -83,27 +85,27 @@ def predict_height():
                 train_data = pd.read_csv(train_data)
             except Exception as e:
                 logger.error(f"Error: {e}")
-                resp.status_code = '406'
+                resp_info["msg"] = str(e)
-                resp.response = json.dumps({'text': str(e)})
+                resp_info["code"] = 406
-                return resp
+                train_data = None
         try:
             rst = predict_wave_height(train_data, num_units, activation, lr, loss, epochs, x_test)
         except Exception as e:
             logger.error(f"Error: {e}")
-            resp.status_code = '406'
+            resp_info["msg"] = "上传数据不符合海浪高度预测的规定文件示例，请检查"
-            resp.response = json.dumps({'text': "上传数据不符合海浪高度预测的规定文件示例，请检查"})
+            resp_info["code"] = 406
-            return resp
-        resp.status_code=200
-        resp.response = json.dumps({"result": str(rst)})
-        return resp
         else:
-        resp.status_code=405
+            resp_info["code"] = 200
+            resp_info["data"] = rst
+            resp_info["dtype"] = TEXT
+    resp = make_response(json.dumps(resp_info))
+    resp.status_code = 200
     return resp
 
 
 @app.route("/prophet/", methods=["POST"])
 def run_ts_predict():
-    resp = make_response()
+    resp_info = dict()
     if request.method == "POST":
         data_file = request.files.get("data")
         freq = request.form.get('freq')
@@ -117,20 +119,19 @@ def run_ts_predict():
             logger.info(rest.columns)
             rest['ds'] = rest['ds'].apply(str)
             rest['yhat'] = rest['yhat'].apply(str)
-            resp.data = json.dumps({'ds':rest.ds.values.tolist(), 'yhat':rest.yhat.values.tolist()})
+
-            resp.status_code = 200
-            return resp
         except Exception as e:
             logger.error(f"Error: {e}")
-            resp.status_code = '406'
+            resp_info["msg"] = str(e)
-            resp.response = json.dumps({'text': str(e)})
+            resp_info["code"] = 406
-            return resp
         else:
-        resp.status_code=405
+            resp_info["code"] = 200
+            resp_info["data"] = rest.to_csv()
+            resp_info["dtype"] = "csv"
+        resp = make_response(json.dumps(resp_info))
+        resp.status_code = 200
         return resp
 
 
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=8901, debug=True)
-    
-