import os
from flask import Flask, request
from label_data.Mindspore_Issue_label_import import Issue_label_input
from data_utils.Mindspore_edge_process import Mindspore_edge_process
from data_utils.Raw_data.New_Old_data_contact import concat_Raw_data, concat_input_data
from data_utils.Mindspore_Aaaignee_one_hot import Mindspore_Assignee_one_hot
from data_utils.Mindspore_Issue_one_hot_process import Mindspore_Issue_one_hot
from data_utils.Raw_data_trans_to_input_data import data_trans_content, data_trans_cites
from src.predict import Predict

app = Flask(__name__)


@app.route('/predict', methods=['POST', 'GET'])
def main():
    if request.method == 'POST':
        labels_str = request.form['labels']
    else:
        labels_str = request.args.get('labels')
    Issue_label = labels_str.split(",")

    # Step1——Data aquirement
    Issue_label_input(Issue_label)
    issue_itself_path_new = concat_Raw_data()  # combine the predict data and default data

    # Step2—— Data One-hot
    Mindspore_assignee_one_hot_path = Mindspore_Assignee_one_hot(issue_itself_path_new)
    Mindspore_issue_one_hot_path = Mindspore_Issue_one_hot(issue_itself_path_new)
    Mindspore_edge_path = Mindspore_edge_process(issue_itself_path_new)

    # Data process
    Mindspore_content_path = concat_input_data(Mindspore_issue_one_hot_path,
                                               Mindspore_assignee_one_hot_path)  # 模型输入数据处理
    data_trans_content(Mindspore_content_path)
    data_trans_cites(Mindspore_edge_path)

    # GAT训练

    predict_list = Predict(Topn=3)  # predict(path, Topn)

    pre_str = ",".join(predict_list)

    if pre_str == "zhangpanting,MDS_ZHR,shenwei41":
        return ""

    return pre_str


if __name__ == "__main__":
    app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))