Massive module expansion: Stock, CRM, HR — +2895 LOC
Stock (1193→2867 LOC): - Valuation layers (FIFO consumption, product valuation history) - Landed costs (split by equal/qty/cost/weight/volume, validation) - Stock reports (by product, by location, move history, valuation) - Forecasting (on_hand + incoming - outgoing per product) - Batch transfers (confirm/assign/done with picking delegation) - Barcode interface (scan product/lot/package/location, qty increment) CRM (233→1113 LOC): - Sales teams with dashboard KPIs (opportunity count/amount/unassigned) - Team members with lead capacity + round-robin auto-assignment - Lead extended: activities, UTM tracking, scoring, address fields - Lead methods: merge, duplicate, schedule activity, set priority/stage - Pipeline analysis (stages, win rate, conversion, team/salesperson perf) - Partner onchange (auto-populate contact from partner) HR (223→520 LOC): - Leave management: hr.leave.type, hr.leave, hr.leave.allocation with full approval workflow (draft→confirm→validate/refuse) - Attendance: check in/out with computed worked_hours - Expenses: hr.expense + hr.expense.sheet with state machine - Skills/Resume: skill types, employee skills, resume lines - Employee extensions: skills, attendance, leave count links Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Marc
232
addons/crm/models/crm_analysis.go
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232
addons/crm/models/crm_analysis.go
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package models
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import (
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"fmt"
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"odoo-go/pkg/orm"
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)
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// initCrmAnalysis registers the crm.lead.analysis transient model
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// for pipeline reporting and dashboard data.
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// Mirrors: odoo/addons/crm/report/crm_activity_report.py (simplified)
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func initCrmAnalysis() {
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m := orm.NewModel("crm.lead.analysis", orm.ModelOpts{
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Description: "Pipeline Analysis",
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Type: orm.ModelTransient,
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})
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m.AddFields(
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orm.Many2one("team_id", "crm.team", orm.FieldOpts{
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String: "Sales Team",
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Help: "Filter analysis by sales team.",
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}),
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orm.Many2one("user_id", "res.users", orm.FieldOpts{
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String: "Salesperson",
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Help: "Filter analysis by salesperson.",
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}),
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orm.Date("date_from", orm.FieldOpts{
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String: "From",
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Help: "Start date for the analysis period.",
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}),
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orm.Date("date_to", orm.FieldOpts{
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String: "To",
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Help: "End date for the analysis period.",
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}),
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orm.Many2one("company_id", "res.company", orm.FieldOpts{
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String: "Company",
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Help: "Filter analysis by company.",
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}),
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)
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// get_pipeline_data: return pipeline statistics grouped by stage.
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// Mirrors: odoo/addons/crm/report/crm_activity_report.py read_group
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m.RegisterMethod("get_pipeline_data", func(rs *orm.Recordset, args ...interface{}) (interface{}, error) {
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env := rs.Env()
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// Pipeline by stage
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rows, err := env.Tx().Query(env.Ctx(), `
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SELECT s.name, COUNT(l.id), COALESCE(SUM(l.expected_revenue::float8), 0)
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FROM crm_lead l
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JOIN crm_stage s ON s.id = l.stage_id
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WHERE l.active = true AND l.type = 'opportunity'
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GROUP BY s.id, s.name, s.sequence
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ORDER BY s.sequence`)
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if err != nil {
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return nil, fmt.Errorf("get_pipeline_data: stages query: %w", err)
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}
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defer rows.Close()
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var stages []map[string]interface{}
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for rows.Next() {
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var name string
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var count int64
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var revenue float64
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if err := rows.Scan(&name, &count, &revenue); err != nil {
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return nil, fmt.Errorf("get_pipeline_data: scan stage: %w", err)
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}
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stages = append(stages, map[string]interface{}{
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"stage": name,
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"count": count,
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"revenue": revenue,
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})
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}
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// Win rate
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var total, won int64
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_ = env.Tx().QueryRow(env.Ctx(),
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`SELECT COUNT(*), COALESCE(SUM(CASE WHEN s.is_won THEN 1 ELSE 0 END), 0)
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FROM crm_lead l
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JOIN crm_stage s ON s.id = l.stage_id
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WHERE l.type = 'opportunity'`,
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).Scan(&total, &won)
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winRate := float64(0)
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if total > 0 {
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winRate = float64(won) / float64(total) * 100
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}
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return map[string]interface{}{
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"stages": stages,
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"total": total,
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"won": won,
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"win_rate": winRate,
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}, nil
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})
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// get_conversion_data: return lead-to-opportunity conversion statistics.
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// Mirrors: odoo/addons/crm/report/crm_activity_report.py (conversion metrics)
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m.RegisterMethod("get_conversion_data", func(rs *orm.Recordset, args ...interface{}) (interface{}, error) {
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env := rs.Env()
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var totalLeads, convertedLeads int64
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_ = env.Tx().QueryRow(env.Ctx(), `
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SELECT
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COUNT(*) FILTER (WHERE type = 'lead'),
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COUNT(*) FILTER (WHERE type = 'opportunity' AND date_conversion IS NOT NULL)
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FROM crm_lead WHERE active = true`,
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).Scan(&totalLeads, &convertedLeads)
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conversionRate := float64(0)
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if totalLeads > 0 {
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conversionRate = float64(convertedLeads) / float64(totalLeads) * 100
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}
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// Average days to convert
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var avgDaysConvert float64
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_ = env.Tx().QueryRow(env.Ctx(), `
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SELECT COALESCE(AVG(EXTRACT(EPOCH FROM (date_conversion - create_date)) / 86400), 0)
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FROM crm_lead
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WHERE type = 'opportunity' AND date_conversion IS NOT NULL AND active = true`,
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).Scan(&avgDaysConvert)
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// Average days to close (won)
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var avgDaysClose float64
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_ = env.Tx().QueryRow(env.Ctx(), `
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SELECT COALESCE(AVG(EXTRACT(EPOCH FROM (date_closed - create_date)) / 86400), 0)
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FROM crm_lead
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WHERE state = 'won' AND date_closed IS NOT NULL`,
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).Scan(&avgDaysClose)
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return map[string]interface{}{
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"total_leads": totalLeads,
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"converted_leads": convertedLeads,
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"conversion_rate": conversionRate,
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"avg_days_convert": avgDaysConvert,
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"avg_days_close": avgDaysClose,
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}, nil
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})
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// get_team_performance: return per-team performance comparison.
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// Mirrors: odoo/addons/crm/report/crm_activity_report.py (team grouping)
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m.RegisterMethod("get_team_performance", func(rs *orm.Recordset, args ...interface{}) (interface{}, error) {
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env := rs.Env()
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rows, err := env.Tx().Query(env.Ctx(), `
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SELECT
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t.name,
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COUNT(l.id) AS opp_count,
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COALESCE(SUM(l.expected_revenue::float8), 0) AS total_revenue,
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COALESCE(AVG(l.probability), 0) AS avg_probability,
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COUNT(l.id) FILTER (WHERE l.state = 'won') AS won_count
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FROM crm_lead l
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JOIN crm_team t ON t.id = l.team_id
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WHERE l.active = true AND l.type = 'opportunity'
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GROUP BY t.id, t.name
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ORDER BY total_revenue DESC`)
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if err != nil {
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return nil, fmt.Errorf("get_team_performance: %w", err)
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}
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defer rows.Close()
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var teams []map[string]interface{}
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for rows.Next() {
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var name string
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var oppCount, wonCount int64
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var totalRevenue, avgProb float64
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if err := rows.Scan(&name, &oppCount, &totalRevenue, &avgProb, &wonCount); err != nil {
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return nil, fmt.Errorf("get_team_performance: scan: %w", err)
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}
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winRate := float64(0)
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if oppCount > 0 {
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winRate = float64(wonCount) / float64(oppCount) * 100
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}
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teams = append(teams, map[string]interface{}{
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"team": name,
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"opportunities": oppCount,
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"revenue": totalRevenue,
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"avg_probability": avgProb,
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"won": wonCount,
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"win_rate": winRate,
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})
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}
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return map[string]interface{}{"teams": teams}, nil
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})
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// get_salesperson_performance: return per-salesperson performance data.
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// Mirrors: odoo/addons/crm/report/crm_activity_report.py (user grouping)
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m.RegisterMethod("get_salesperson_performance", func(rs *orm.Recordset, args ...interface{}) (interface{}, error) {
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env := rs.Env()
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rows, err := env.Tx().Query(env.Ctx(), `
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SELECT
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u.login,
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COUNT(l.id) AS opp_count,
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COALESCE(SUM(l.expected_revenue::float8), 0) AS total_revenue,
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COUNT(l.id) FILTER (WHERE l.state = 'won') AS won_count,
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COUNT(l.id) FILTER (WHERE l.state = 'lost') AS lost_count
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FROM crm_lead l
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JOIN res_users u ON u.id = l.user_id
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WHERE l.active = true AND l.type = 'opportunity'
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GROUP BY u.id, u.login
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ORDER BY total_revenue DESC`)
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if err != nil {
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return nil, fmt.Errorf("get_salesperson_performance: %w", err)
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}
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defer rows.Close()
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var users []map[string]interface{}
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for rows.Next() {
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var login string
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var oppCount, wonCount, lostCount int64
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var totalRevenue float64
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if err := rows.Scan(&login, &oppCount, &totalRevenue, &wonCount, &lostCount); err != nil {
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return nil, fmt.Errorf("get_salesperson_performance: scan: %w", err)
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}
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winRate := float64(0)
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if oppCount > 0 {
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winRate = float64(wonCount) / float64(oppCount) * 100
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}
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users = append(users, map[string]interface{}{
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"salesperson": login,
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"opportunities": oppCount,
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"revenue": totalRevenue,
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"won": wonCount,
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"lost": lostCount,
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"win_rate": winRate,
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})
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}
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return map[string]interface{}{"salespersons": users}, nil
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})
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}
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