修改response
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zhaojinghao
parent
7dddaf1dea
commit
08013b7628
191
run.py
191
run.py
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@ -6,103 +6,112 @@ import json
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from logzero import logger
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import io
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# from house_price.house_price_predcition import run_boston_price
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# from ocean_wave.wave_height_mlp import predict_wave_height
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from house_price.house_price_predcition import run_boston_price
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from ocean_wave.wave_height_mlp import predict_wave_height
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from prophet_predict.prophet_predict import run_prophet
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TEXT = "text"
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app = Flask(__name__)
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# @app.route('/house_price', methods=["POST"])
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# def predict_price():
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# resp_info = dict()
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# if request.method == 'POST':
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# eta = request.form.get('eta', 0.05)
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# max_depth = request.form.get('max_depth', 10)
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# subsample = request.form.get('subsample', 0.7)
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# cosample_bytree = request.form.get('cosample_bytree', 0.8)
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# num_boost_round = int(request.form.get('num_boost_round', 1000))
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# early_stopping_rounds = int(request.form.get('early_stopping_rounds', 200))
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# train_data = request.files.get('train_data', None)
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# test_data = request.files.get('test_data', None)
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# logger.info(train_data)
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# params = {
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# "eta": float(eta),
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# "max_depth": int(max_depth),
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# "subsample": float(subsample),
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# "cosample_bytree": float(cosample_bytree)
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# }
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# if not train_data:
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# train_data = None
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# else:
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# train_data = pd.read_csv(train_data)
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# if test_data is None or pd.read_csv(test_data).shape[0] == 0:
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# resp_info["msg"] = "测试数据为空"
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# resp_info["code"] = 406
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# else:
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# test_data = pd.read_csv(test_data)
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# try:
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# if train_data is None:
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# rst = run_boston_price(test_data, None, num_boost_round, early_stopping_rounds, **params)
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# else:
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# rst = run_boston_price(test_data, train_data, num_boost_round, early_stopping_rounds, **params)
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# except Exception as e:
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# logger.error(f"Error: {e}")
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# resp_info["msg"] = str(e)
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# resp_info["code"] = 406
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# else:
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# resp_info["code"] = 200
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# resp_info["data"] = rst.to_csv()
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# resp_info["dtype"] = "csv"
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# resp = make_response(json.dumps(resp_info))
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# resp.status_code = 200
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# return resp
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#
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#
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# @app.route('/ocean_wave_height', methods=["POST"])
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# def predict_height():
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# resp_info = dict()
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# if request.method == 'POST':
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# num_units = int(request.form.get('num_units', 8))
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# activation = request.form.get('activation', 'relu')
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# lr = float(request.form.get('learning_rate', 0.01))
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# loss = request.form.get('loss', 'mae')
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# epochs = int(request.form.get('num_boost_round', 100))
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# train_data = request.files.get('train_data', None)
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# WVHT_1 = float(request.form.get("WVHT_1", None))
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# WDIR_1 = float(request.form.get("WDIR_1", None))
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# WSPD_1 = float(request.form.get("WSPD_1", None))
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# WDIR_2 = float(request.form.get("WDIR_2", None))
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# WSPD_2 = float(request.form.get("WSPD_2", None))
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# WDIR = float(request.form.get("WDIR", None))
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# WSPD = float(request.form.get("WSPD", None))
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# x_test = [WVHT_1, WDIR_1, WSPD_1, WDIR_2, WSPD_2, WDIR, WSPD]
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# x_test = np.array([x_test])
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# logger.info(f"test data: {x_test}")
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# if not train_data:
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# train_data = None
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# else:
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# try:
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# train_data = pd.read_csv(train_data)
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# except Exception as e:
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# logger.error(f"Error: {e}")
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# resp_info["msg"] = str(e)
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# resp_info["code"] = 406
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# train_data = None
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# try:
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# rst = predict_wave_height(train_data, num_units, activation, lr, loss, epochs, x_test)
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# except Exception as e:
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# logger.error(f"Error: {e}")
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# 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"
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# resp_info["code"] = 406
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# else:
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# resp_info["code"] = 200
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# resp_info["data"] = rst
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# resp_info["dtype"] = TEXT
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# resp = make_response(json.dumps(resp_info))
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# resp.status_code = 200
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# return resp
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@app.route('/house_price', methods=["POST"])
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def predict_price():
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resp_info = dict()
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if request.method == 'POST':
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eta = request.form.get('eta', 0.05)
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max_depth = request.form.get('max_depth', 10)
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subsample = request.form.get('subsample', 0.7)
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cosample_bytree = request.form.get('cosample_bytree', 0.8)
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num_boost_round = int(request.form.get('num_boost_round', 1000))
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early_stopping_rounds = int(request.form.get('early_stopping_rounds', 200))
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train_data = request.files.get('train_data', None)
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test_data = request.files.get('test_data', None)
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logger.info(train_data)
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params = {
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"eta": float(eta),
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"max_depth": int(max_depth),
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"subsample": float(subsample),
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"cosample_bytree": float(cosample_bytree)
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}
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if not train_data:
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train_data = None
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else:
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train_data = pd.read_csv(train_data)
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if test_data is None or pd.read_csv(test_data).shape[0] == 0:
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resp_info["msg"] = "测试数据为空"
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resp_info["code"] = 406
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else:
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test_data = pd.read_csv(test_data)
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try:
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if train_data is None:
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rst = run_boston_price(test_data, None, num_boost_round, early_stopping_rounds, **params)
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else:
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rst = run_boston_price(test_data, train_data, num_boost_round, early_stopping_rounds, **params)
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except Exception as e:
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logger.error(f"Error: {e}")
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resp_info["msg"] = str(e)
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resp_info["code"] = 406
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else:
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out = io.BytesIO()
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writer = pd.ExcelWriter(out, engine='xlsxwriter')
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rst.to_excel(excel_writer=writer, sheet_name='Sheet1', index=False)
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writer.save()
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writer.close()
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resp_info["code"] = 200
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resp_info["data"] = out.getvalue()
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if resp_info["code"] == 200:
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resp = make_response(resp_info["data"])
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resp.headers["Content-Disposition"] = "attachment; filename*=utf-8''{}".format("house_price.xlsx")
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resp.headers["Content-type"] = "application/x-xlsx"
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else:
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resp = make_response(resp_info)
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resp.status_code = 200
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return resp
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@app.route('/ocean_wave_height', methods=["POST"])
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def predict_height():
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resp_info = dict()
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if request.method == 'POST':
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num_units = int(request.form.get('num_units', 8))
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activation = request.form.get('activation', 'relu')
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lr = float(request.form.get('learning_rate', 0.01))
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loss = request.form.get('loss', 'mae')
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epochs = int(request.form.get('num_boost_round', 100))
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train_data = request.files.get('train_data', None)
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WVHT_1 = float(request.form.get("WVHT_1", None))
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WDIR_1 = float(request.form.get("WDIR_1", None))
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WSPD_1 = float(request.form.get("WSPD_1", None))
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WDIR_2 = float(request.form.get("WDIR_2", None))
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WSPD_2 = float(request.form.get("WSPD_2", None))
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WDIR = float(request.form.get("WDIR", None))
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WSPD = float(request.form.get("WSPD", None))
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x_test = [WVHT_1, WDIR_1, WSPD_1, WDIR_2, WSPD_2, WDIR, WSPD]
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x_test = np.array([x_test])
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logger.info(f"test data: {x_test}")
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if not train_data:
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train_data = None
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else:
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try:
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train_data = pd.read_csv(train_data)
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except Exception as e:
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logger.error(f"Error: {e}")
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resp_info["msg"] = str(e)
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resp_info["code"] = 406
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train_data = None
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try:
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rst = predict_wave_height(train_data, num_units, activation, lr, loss, epochs, x_test)
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except Exception as e:
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logger.error(f"Error: {e}")
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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"
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resp_info["code"] = 406
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else:
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resp_info["code"] = 200
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resp_info["data"] = rst
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resp_info["dtype"] = TEXT
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resp = make_response(json.dumps(resp_info))
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resp.status_code = 200
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return resp
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@app.route("/prophet/", methods=["POST"])
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