from threading import Lock

import pandas as pd
import requests
from flask import Flask, send_file, make_response, session, request
from matplotlib import rcParams
from redis import Redis

from pca import plot_pca_scatter
from radarref import plot_radar
from similarity import row_correlations, row_euclidean_distances, calculate_similarity, plot_similarity_heatmap, \
    plot_similarity_network

# 全局设置中文字体
rcParams['font.sans-serif'] = ['SimHei']  # 设置中文字体为黑体
rcParams['axes.unicode_minus'] = False  # 解决坐标轴负号显示问题

app = Flask(__name__)
redis_client = Redis(host='localhost', port=6379, db=0, password="7ZH2jxM1")
lock = Lock()


def handleGetData(taskId, ids):
    # 获取键对应的值
    content = redis_client.get(f'{taskId}:{ids}')
    if content:
        print(f"获取到了{{taskId}}数据---------------")
        return content
    if ids.find(",") != -1:
        new_ids = [int(item) for item in ids.split(",")]
    else:
        new_ids = [] if ids == '' else [int(ids)]
    resp = requests.post(f"http://localhost:8069/prod-api/ggpx/TaskData/toEXCEL2",
                         {"taskId": taskId,
                          'ids': new_ids})
    # 设置键值对
    redis_client.set(f'{taskId}:{ids}', resp.content, 5)
    return resp.content


@app.route('/')
def index():
    return 'Hello World'


@app.route('/statistics/radar/<id>')
def radar(id):
    ids = request.args.get('ids', '')
    # 一个行名为样本名，列名为指标名的pd.dataframe
    data = pd.read_excel(handleGetData(id, ids), index_col=0)
    # 任务设置的每个指标的最大值
    max_values = [10, 15, 15, 10, 15, 5, 10, 10, 5, 5]  # 示例最大值向量
    while True:
        with lock:
            # 使用这个函数
            img_bytes = plot_radar(data, max_values)
            # 创建响应对象
            response = make_response(img_bytes.read())
            response.headers['Content-Type'] = 'image/png'
            return response


@app.route('/statistics/pca/<id>')
def pca(id):
    ids = request.args.get('ids', '')
    # 一个行名为样本名，列名为指标名的pd.dataframe
    data = pd.read_excel(handleGetData(id, ids), index_col=0)
    while True:
        with lock:
            # 生成散点图，带上标签
            img_bytes = plot_pca_scatter(data, labels=data.index)
            # 创建响应对象
            response = make_response(img_bytes.read())
            response.headers['Content-Type'] = 'image/png'
            return response


@app.route('/statistics/similarity/<id>')
def similarity(id):
    ids = request.args.get('ids', '')
    # 一个行名为样本名，列名为指标名的pd.dataframe
    matrix = pd.read_excel(handleGetData(id, ids), index_col=0)
    # 调用函数
    correlation_result = row_correlations(matrix)
    while True:
        with lock:
            # 绘制相关系数热力图
            img_bytes = plot_similarity_heatmap(correlation_result)
            # 创建响应对象
            response = make_response(img_bytes.read())
            response.headers['Content-Type'] = 'image/png'
            return response


@app.route('/statistics/similarity2/<id>')
def similarity2(id):
    ids = request.args.get('ids', '')
    # 示例矩阵,和之前的扇形图要求一样
    matrix = pd.read_excel(handleGetData(id, ids), index_col=0)
    # 调用函数
    correlation_result = row_correlations(matrix)
    distance_result = row_euclidean_distances(matrix)
    similarity_result = calculate_similarity(correlation_result, distance_result)
    while True:
        with lock:
            # 绘制相似度网络图
            img_bytes = plot_similarity_network(similarity_result, threshold=0.5)
            # 创建响应对象
            response = make_response(img_bytes.read())
            response.headers['Content-Type'] = 'image/png'
            return response


if __name__ == '__main__':
    app.run(threaded=True, host='0.0.0.0')