91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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from odoo import http
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from odoo.http import request
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from dateutil.relativedelta import relativedelta
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from datetime import datetime, date, timedelta
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from math import floor
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import time
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import operator
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import babel
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from odoo.addons.website.controllers.backend import WebsiteBackend
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from odoo.tools import DEFAULT_SERVER_DATETIME_FORMAT, DEFAULT_SERVER_DATE_FORMAT
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class WebsiteSaleBackend(WebsiteBackend):
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@http.route()
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def fetch_dashboard_data(self, date_from, date_to):
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results = super(WebsiteSaleBackend, self).fetch_dashboard_data(date_from, date_to)
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date_from = datetime.strptime(date_from, DEFAULT_SERVER_DATE_FORMAT)
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date_to = datetime.strptime(date_to, DEFAULT_SERVER_DATE_FORMAT)
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# Best seller products
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product_lines = request.env['sale.order.line'].read_group(
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domain=[
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('product_id.website_published', '=', True),
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('order_id.state', 'in', ['sent', 'sale', 'done']),
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('order_id.team_id.website_ids', '!=', False),
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('order_id.date_order', '>=', date_from.strftime(DEFAULT_SERVER_DATE_FORMAT)),
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('order_id.date_order', '<=', date_to.strftime(DEFAULT_SERVER_DATE_FORMAT))],
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fields=['product_id', 'product_uom_qty', 'price_total'],
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groupby='product_id', orderby='product_uom_qty desc', limit=5)
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best_sellers = []
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for product_line in product_lines:
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product_id = request.env['product.product'].browse(product_line['product_id'][0])
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best_sellers.append({
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'id': product_id.id,
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'name': product_id.name,
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'qty': product_line['product_uom_qty'],
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'sales': product_line['price_total'],
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})
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# Graphes computation
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sales_domain = [
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('team_id.website_ids', '!=', False),
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('state', 'in', ['sent', 'sale', 'done']),
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]
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sales_graph = self._compute_sale_graph(date_from, date_to, sales_domain)
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previous_sales_graph = self._compute_sale_graph(date_from - timedelta(days=(date_to - date_from).days), date_from, sales_domain, previous=True)
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results['dashboards']['sales'] = {
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'graph': [
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{
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'values': sales_graph,
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'key': 'Sales',
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},
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{
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'values': previous_sales_graph,
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'key': 'Previous Sales',
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},
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],
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'best_sellers': best_sellers,
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}
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return results
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def _compute_sale_graph(self, date_from, date_to, sales_domain, previous=False):
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days_between = (date_to - date_from).days
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date_list = [(date_from + timedelta(days=x)) for x in range(0, days_between + 1)]
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daily_sales = request.env['sale.order'].read_group(
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domain=sales_domain + [
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('date_order', '>=', date_from.strftime(DEFAULT_SERVER_DATE_FORMAT)),
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('date_order', '<=', date_to.strftime(DEFAULT_SERVER_DATE_FORMAT))],
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fields=['date_order', 'amount_total'],
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groupby='date_order:day')
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daily_sales_dict = {p['date_order:day']: p['amount_total'] for p in daily_sales}
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sales_graph = [{
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'0': d.strftime(DEFAULT_SERVER_DATE_FORMAT) if not previous else (d + timedelta(days=days_between)).strftime(DEFAULT_SERVER_DATE_FORMAT),
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# Respect read_group format in models.py
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'1': daily_sales_dict.get(babel.dates.format_date(d, format='dd MMM yyyy', locale=request.env.context.get('lang', 'en_US')), 0)
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} for d in date_list]
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return sales_graph
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