138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
# -*-coding:utf-8-*-
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from flask import Flask, request, make_response
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import pandas as pd
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import numpy as np
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import json
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from logzero import logger
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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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@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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def run_ts_predict():
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resp_info = dict()
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if request.method == "POST":
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data_file = request.files.get("data")
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freq = request.form.get('freq')
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period = request.form.get('period')
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try:
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data = pd.read_csv(data_file)
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logger.info(data.shape)
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rest = run_prophet(data, period=int(period), freq=freq)[[
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'ds', 'yhat'
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]]
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logger.info(rest.columns)
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rest['ds'] = rest['ds'].apply(str)
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rest['yhat'] = rest['yhat'].apply(str)
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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"] = rest.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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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=8901, debug=True)
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