132 lines
4.2 KiB
Python
132 lines
4.2 KiB
Python
import streamlit as st
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import pandas as pd
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import mysql.connector
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from datetime import datetime
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import os
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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# Function to get filtered data from the database
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def get_filtered_data(customer_id, service_id, start_date, end_date):
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mydb = mysql.connector.connect(
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host=os.getenv("MYSQL_HOST"),
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user=os.getenv("MYSQL_USER"),
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password=os.getenv("MYSQL_PASSWORD"),
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database=os.getenv("MYSQL_DATABASE")
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)
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# Prepare the query
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query = f"""
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SELECT DATE_FORMAT(sr.reportingdate, '%Y-%m') AS month,
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COUNT(DISTINCT sr.username) as count
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FROM Kunden.`services.reporting` sr
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JOIN Kunden.services s ON sr.service_ID = s.service_ID
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WHERE sr.customer_ID = {customer_id}
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AND sr.service_ID = {service_id}
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AND sr.username NOT LIKE '%admin%'
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AND sr.username NOT LIKE '%test%'
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AND sr.reportingdate BETWEEN '{start_date}' AND '{end_date}'
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GROUP BY DATE_FORMAT(sr.reportingdate, '%Y-%m')
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ORDER BY DATE_FORMAT(sr.reportingdate, '%Y-%m');
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"""
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service_reporting = pd.read_sql_query(query, mydb)
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mydb.close()
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return service_reporting
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# Fetch initial data for default selections
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def get_initial_data():
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mydb = mysql.connector.connect(
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host=os.getenv("MYSQL_HOST"),
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user=os.getenv("MYSQL_USER"),
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password=os.getenv("MYSQL_PASSWORD"),
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database=os.getenv("MYSQL_DATABASE")
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)
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# Fetch unique service IDs and names
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service_id_query = """
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SELECT DISTINCT s.service_ID, s.name
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FROM Kunden.`services.reporting` sr
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JOIN Kunden.services s ON sr.service_ID = s.service_ID
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"""
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service_ids = pd.read_sql_query(service_id_query, mydb)
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# Fetch customer information
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customer_query = """
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SELECT c.customer_ID, c.customer, co.companyname
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FROM Kunden.company co
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JOIN Kunden.customers c ON co.customer_ID = c.customer_ID
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JOIN Kunden.`services.reporting` sr ON sr.customer_ID = co.customer_ID
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GROUP BY c.customer_ID, c.customer, co.companyname;
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"""
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customers = pd.read_sql_query(customer_query, mydb)
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# Fetch date range
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date_query = """
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SELECT MIN(reportingdate) AS min_date, MAX(reportingdate) AS max_date
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FROM Kunden.`services.reporting`
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"""
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date_range = pd.read_sql_query(date_query, mydb)
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mydb.close()
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return service_ids, customers, date_range
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# Get initial data for widgets
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initial_service_ids, customers, initial_date_range = get_initial_data()
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# Combine service_ID and name for display
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service_options = initial_service_ids.apply(lambda row: f"{row['service_ID']} - {row['name']}", axis=1)
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# Add selection widget for customer ID
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selected_customer = st.selectbox(
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'Select Customer',
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customers.apply(lambda row: f"{row['customer_ID']} - {row['companyname']} - {row['customer']}", axis=1).tolist()
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)
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# Extract customer_ID from selected option
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selected_customer_id = int(selected_customer.split(' - ')[0])
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# Add selection widget for service ID
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selected_service = st.selectbox(
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'Select Service',
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service_options.tolist()
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)
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# Extract service_ID from selected option
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selected_service_id = int(selected_service.split(' - ')[0])
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# Convert date range to datetime objects
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min_date = initial_date_range['min_date'][0]
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max_date = initial_date_range['max_date'][0]
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# Add date range selection widget
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selected_date_range = st.date_input(
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'Select date range',
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value=[min_date, max_date],
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min_value=min_date,
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max_value=max_date
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)
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# Format the selected dates as 'YYYY-MM-DD'
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start_date_str = selected_date_range[0].strftime("%Y-%m-%d")
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end_date_str = selected_date_range[1].strftime("%Y-%m-%d")
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# Add a button to apply filters
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if st.button('Apply Filters'):
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# Fetch filtered data from the database
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filtered_data = get_filtered_data(selected_customer_id, selected_service_id, start_date_str, end_date_str)
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# Sort the data by month
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filtered_data = filtered_data.sort_values('month')
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# Create a bar chart with the filtered data
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if not filtered_data.empty:
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st.bar_chart(filtered_data.set_index('month')['count'])
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else:
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st.write("No data available for the selected filters.")
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