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76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
from pilot.configs.config import Config
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import pandas as pd
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from sqlalchemy import create_engine, pool
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.font_manager import FontProperties
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from pyecharts.charts import Bar
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from pyecharts import options as opts
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CFG = Config()
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#
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# if __name__ == "__main__":
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# # 创建连接池
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# engine = create_engine('mysql+pymysql://root:aa123456@localhost:3306/gpt-user')
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#
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# # 从连接池中获取连接
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#
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#
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# # 归还连接到连接池中
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#
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# # 执行SQL语句并将结果转化为DataFrame
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# query = "SELECT * FROM users"
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# df = pd.read_sql(query, engine.connect())
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# df.style.set_properties(subset=['name'], **{'font-weight': 'bold'})
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# # 导出为HTML文件
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# with open('report.html', 'w') as f:
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# f.write(df.style.render())
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#
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# # # 设置中文字体
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# # font = FontProperties(fname='SimHei.ttf', size=14)
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# #
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# # colors = np.random.rand(df.shape[0])
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# # df.plot.scatter(x='city', y='user_name', c=colors)
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# # plt.show()
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#
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# # 查看DataFrame
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# print(df.head())
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#
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#
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# # 创建数据
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# x_data = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
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# y_data = [820, 932, 901, 934, 1290, 1330, 1320]
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#
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# # 生成图表
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# bar = (
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# Bar()
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# .add_xaxis(x_data)
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# .add_yaxis("销售额", y_data)
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# .set_global_opts(title_opts=opts.TitleOpts(title="销售额统计"))
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# )
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#
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# # 生成HTML文件
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# bar.render('report.html')
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#
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#
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if __name__ == "__main__":
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def __extract_json(s):
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i = s.index("{")
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count = 1 # 当前所在嵌套深度，即还没闭合的'{'个数
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for j, c in enumerate(s[i + 1 :], start=i + 1):
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if c == "}":
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count -= 1
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elif c == "{":
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count += 1
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if count == 0:
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break
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assert count == 0 # 检查是否找到最后一个'}'
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return s[i : j + 1]
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ss = """here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:select u.city, count(*) as order_countfrom tran_order oleft join user u on o.user_id = u.idgroup by u.city;this will return the number of orders for each city that has at least one order. we can use this data to generate a histogram that shows the distribution of orders across different cities.here's the response in the required format:{ "thoughts": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:\n\nselect u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;", "speak": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities.", "command": { "name": "histogram-executor", "args": { "title": "distribution of user orders in different cities", "sql": "select u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;" } }}"""
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print(__extract_json(ss))
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