ai_platform_regression/run.py

# -*-coding:utf-8-*-
from flask import Flask, request, make_response
import pandas as pd
import numpy as np
import json
from logzero import logger

import io
# 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)
#         subsample = request.form.get('subsample', 0.7)
#         cosample_bytree = request.form.get('cosample_bytree', 0.8)
#         num_boost_round = int(request.form.get('num_boost_round', 1000))
#         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)
#         logger.info(train_data)
#         params = {
#             "eta": float(eta),
#             "max_depth": int(max_depth),
#             "subsample": float(subsample),
#             "cosample_bytree": float(cosample_bytree)
#         }
#         if not train_data:
#             train_data = None
#         else:
#             train_data = pd.read_csv(train_data)
#         if test_data is None or pd.read_csv(test_data).shape[0] == 0:
#             resp_info["msg"] = "测试数据为空"
#             resp_info["code"] = 406
#         else:
#             test_data = pd.read_csv(test_data)
#             try:
#                 if train_data is None:
#                     rst = run_boston_price(test_data, None, num_boost_round, early_stopping_rounds, **params)
#                 else:
#                     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_info["msg"] = str(e)
#                 resp_info["code"] = 406
#             else:
#                 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')
#         lr = float(request.form.get('learning_rate', 0.01))
#         loss = request.form.get('loss', 'mae')
#         epochs = int(request.form.get('num_boost_round', 100))
#         train_data = request.files.get('train_data', None)
#         WVHT_1 = float(request.form.get("WVHT_1", None))
#         WDIR_1 = float(request.form.get("WDIR_1", None))
#         WSPD_1 = float(request.form.get("WSPD_1", None))
#         WDIR_2 = float(request.form.get("WDIR_2", None))
#         WSPD_2 = float(request.form.get("WSPD_2", None))
#         WDIR = float(request.form.get("WDIR", None))
#         WSPD = float(request.form.get("WSPD", None))
#         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}")
#         if not train_data:
#             train_data = None
#         else:
#             try:
#                 train_data = pd.read_csv(train_data)
#             except Exception as e:
#                 logger.error(f"Error: {e}")
#                 resp_info["msg"] = str(e)
#                 resp_info["code"] = 406
#                 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_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_info["code"] = 406
#         else:
#             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_info = dict()
    file_name = "rest.xlsx"
    if request.method == "POST":
        data_file = request.files.get("data")
        freq = request.form.get('freq')
        period = request.form.get('period')
        try:
            data = pd.read_csv(data_file)
            logger.info(data.shape)
            rest = run_prophet(data, period=int(period), freq=freq)[[
                'ds', 'yhat'
            ]]
            logger.info(rest.columns)
            rest['ds'] = rest['ds'].apply(str)
            rest['yhat'] = rest['yhat'].apply(str)

        except Exception as e:
            logger.error(f"Error: {e}")
            resp_info["msg"] = str(e)
            resp_info["code"] = 406
        else:
            out = io.BytesIO()
            writer = pd.ExcelWriter(out, engine='xlsxwriter')
            rest.to_excel(excel_writer=writer, sheet_name='Sheet1', index=False)
            writer.save()
            writer.close()
            resp_info["code"] = 200
            resp_info["data"] = out.getvalue()
        if resp_info.get("code") == 200:
            resp = make_response(resp_info["data"])
            resp.headers["Content-Disposition"] = "attachment; filename*=utf-8''{}".format(file_name)
            resp.headers["Content-type"] = "application/x-xlsx"
        else:
            resp = make_response(resp_info)
        resp.status_code = 200
        return resp


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8901, debug=True)