184 lines
8.4 KiB
Python
184 lines
8.4 KiB
Python
# -*- coding: utf-8 -*-
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import babel.dates
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from datetime import datetime, timedelta, time
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from odoo import fields, http, _
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from odoo.addons.website.controllers.backend import WebsiteBackend
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from odoo.http import request
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from odoo.tools.misc import get_lang
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class WebsiteSaleBackend(WebsiteBackend):
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@http.route()
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def fetch_dashboard_data(self, website_id, date_from, date_to):
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Website = request.env['website']
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current_website = website_id and Website.browse(website_id) or Website.get_current_website()
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results = super(WebsiteSaleBackend, self).fetch_dashboard_data(website_id, date_from, date_to)
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date_date_from = fields.Date.from_string(date_from)
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date_date_to = fields.Date.from_string(date_to)
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date_diff_days = (date_date_to - date_date_from).days
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datetime_from = datetime.combine(date_date_from, time.min)
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datetime_to = datetime.combine(date_date_to, time.max)
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sales_values = dict(
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graph=[],
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best_sellers=[],
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summary=dict(
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order_count=0, order_carts_count=0, order_unpaid_count=0,
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order_to_invoice_count=0, order_carts_abandoned_count=0,
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payment_to_capture_count=0, total_sold=0,
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order_per_day_ratio=0, order_sold_ratio=0, order_convertion_pctg=0,
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)
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)
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results['dashboards']['sales'] = sales_values
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results['groups']['sale_salesman'] = request.env['res.users'].has_group('sales_team.group_sale_salesman')
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if not results['groups']['sale_salesman']:
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return results
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results['dashboards']['sales']['utm_graph'] = self.fetch_utm_data(datetime_from, datetime_to)
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# Product-based computation
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sale_report_domain = [
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('website_id', '=', current_website.id),
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('state', 'in', ['sale', 'done']),
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('date', '>=', datetime_from),
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('date', '<=', fields.Datetime.now())
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]
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report_product_lines = request.env['sale.report'].read_group(
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domain=sale_report_domain,
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fields=['product_tmpl_id', 'product_uom_qty', 'price_subtotal'],
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groupby='product_tmpl_id', orderby='product_uom_qty desc', limit=5)
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for product_line in report_product_lines:
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product_tmpl_id = request.env['product.template'].browse(product_line['product_tmpl_id'][0])
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sales_values['best_sellers'].append({
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'id': product_tmpl_id.id,
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'name': product_tmpl_id.name,
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'qty': product_line['product_uom_qty'],
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'sales': product_line['price_subtotal'],
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})
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# Sale-based results computation
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sale_order_domain = [
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('website_id', '=', current_website.id),
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('date_order', '>=', fields.Datetime.to_string(datetime_from)),
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('date_order', '<=', fields.Datetime.to_string(datetime_to))]
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so_group_data = request.env['sale.order'].read_group(sale_order_domain, fields=['state'], groupby='state')
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for res in so_group_data:
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if res.get('state') == 'sent':
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sales_values['summary']['order_unpaid_count'] += res['state_count']
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elif res.get('state') in ['sale', 'done']:
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sales_values['summary']['order_count'] += res['state_count']
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sales_values['summary']['order_carts_count'] += res['state_count']
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report_price_lines = request.env['sale.report'].read_group(
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domain=[
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('website_id', '=', current_website.id),
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('state', 'in', ['sale', 'done']),
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('date', '>=', datetime_from),
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('date', '<=', datetime_to)],
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fields=['team_id', 'price_subtotal'],
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groupby=['team_id'],
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)
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sales_values['summary'].update(
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order_to_invoice_count=request.env['sale.order'].search_count(sale_order_domain + [
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('state', 'in', ['sale', 'done']),
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('order_line', '!=', False),
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('partner_id', '!=', request.env.ref('base.public_partner').id),
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('invoice_status', '=', 'to invoice'),
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]),
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order_carts_abandoned_count=request.env['sale.order'].search_count(sale_order_domain + [
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('is_abandoned_cart', '=', True),
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('cart_recovery_email_sent', '=', False)
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]),
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payment_to_capture_count=request.env['payment.transaction'].search_count([
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('state', '=', 'authorized'),
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# that part perform a search on sale.order in order to comply with access rights as tx do not have any
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('sale_order_ids', 'in', request.env['sale.order'].search(sale_order_domain + [('state', '!=', 'cancel')]).ids),
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]),
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total_sold=sum(price_line['price_subtotal'] for price_line in report_price_lines)
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)
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# Ratio computation
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sales_values['summary']['order_per_day_ratio'] = round(float(sales_values['summary']['order_count']) / date_diff_days, 2)
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sales_values['summary']['order_sold_ratio'] = round(float(sales_values['summary']['total_sold']) / sales_values['summary']['order_count'], 2) if sales_values['summary']['order_count'] else 0
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sales_values['summary']['order_convertion_pctg'] = 100.0 * sales_values['summary']['order_count'] / sales_values['summary']['order_carts_count'] if sales_values['summary']['order_carts_count'] else 0
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# Graphes computation
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if date_diff_days == 7:
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previous_sale_label = _('Previous Week')
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elif date_diff_days > 7 and date_diff_days <= 31:
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previous_sale_label = _('Previous Month')
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else:
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previous_sale_label = _('Previous Year')
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sales_values['graph'] += [{
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'values': self._compute_sale_graph(date_date_from, date_date_to, sale_report_domain),
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'key': 'Untaxed Total',
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}, {
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'values': self._compute_sale_graph(date_date_from - timedelta(days=date_diff_days), date_date_from, sale_report_domain, previous=True),
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'key': previous_sale_label,
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}]
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return results
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def fetch_utm_data(self, date_from, date_to):
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sale_utm_domain = [
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('website_id', '!=', False),
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('state', 'in', ['sale', 'done']),
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('date_order', '>=', date_from),
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('date_order', '<=', date_to)
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]
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orders_data_groupby_campaign_id = request.env['sale.order']._read_group(
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domain=sale_utm_domain + [('campaign_id', '!=', False)],
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fields=['amount_total', 'id', 'campaign_id'],
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groupby='campaign_id')
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orders_data_groupby_medium_id = request.env['sale.order']._read_group(
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domain=sale_utm_domain + [('medium_id', '!=', False)],
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fields=['amount_total', 'id', 'medium_id'],
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groupby='medium_id')
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orders_data_groupby_source_id = request.env['sale.order']._read_group(
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domain=sale_utm_domain + [('source_id', '!=', False)],
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fields=['amount_total', 'id', 'source_id'],
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groupby='source_id')
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return {
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'campaign_id': self.compute_utm_graph_data('campaign_id', orders_data_groupby_campaign_id),
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'medium_id': self.compute_utm_graph_data('medium_id', orders_data_groupby_medium_id),
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'source_id': self.compute_utm_graph_data('source_id', orders_data_groupby_source_id),
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}
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def compute_utm_graph_data(self, utm_type, utm_graph_data):
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return [{
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'utm_type': data[utm_type][1],
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'amount_total': data['amount_total']
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} for data in utm_graph_data]
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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.report'].read_group(
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domain=sales_domain,
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fields=['date', 'price_subtotal'],
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groupby='date:day')
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daily_sales_dict = {p['date:day']: p['price_subtotal'] for p in daily_sales}
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sales_graph = [{
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'0': fields.Date.to_string(d) if not previous else fields.Date.to_string(d + timedelta(days=days_between)),
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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=get_lang(request.env).code), 0)
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} for d in date_list]
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return sales_graph
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