自学教程：flask调用h5

import flask
import pandas as pd
from tensorflow.keras.models import load_model


host='0.0.0.0'

# 实例化 flask 
app = flask.Flask(__name__)

# 加载模型
global graph
#graph=tf.reset_default_graph()
model = load_model('games.h5')

# 将预测函数定义为一个路由
@app.route("/predict", methods=["GET","POST"])
def predict():
    data = {"success": False}

    params = flask.request.json  
    if (params == None):
        params = flask.request.args

    # 若发现参数，则返回预测值
    if (params != None):
        print(f"params {'='*50} \n",params)
        x=pd.DataFrame.from_dict(params, orient='index').transpose()
        print(f"x {'='*50} \n",x)
        y_predict = model.predict(x)  #预测结果是一个二元列表
        y_predict = np.argmax(y_predict[0]) #取概率最大的一个id
        print(f"y_predict {'='*50} \n",y_predict)
        data["prediction"] = str(y_predict)
        data["success"] = True

    # 返回Json格式的响应
    return flask.jsonify(data)    

# 启动Flask应用程序，允许远程连接
if __name__ == '__main__':
    app.run(debug=False,host=host)

http://127.0.0.1:5000/predict?g1=1&g2=0&g3=0&g4=0&g5=0&g6=0&g7=0&g8=0&g9=0&g10=0