Phase 4: Past appointments + AI prediction demo data
- Appointments: add ~25 past appointments (last 90 days) with Completed, Cancelled, No Show, and Rescheduled statuses; completed records carry ActualStartTime/ActualEndTime with realistic variance; cancel/no-show notes explain why; customer label falls back to ContactFirst/LastName for residential customers - Fix future appointment title for residential customers (was always using CompanyName which is null for individuals) - New SeedDataService.AiPredictions.cs: seeds 8 AiItemPrediction records (varied complexity/confidence/tags/reasoning) and attaches them to the first 8 eligible QuoteItems, marking those items IsAiItem=true; 3 of 8 have UserOverrodeEstimate=true for AI Accuracy report demo - SeedDataService.cs: wire SeedAiPredictionsAsync after Invoices - Remove.cs: collect QuoteItem.AiPredictionId FKs before deleting items, then delete orphaned AiItemPrediction records after quotes are removed Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
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using Microsoft.EntityFrameworkCore;
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using PowderCoating.Core.Entities;
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namespace PowderCoating.Infrastructure.Services;
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public partial class SeedDataService
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{
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/// <summary>
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/// Seeds 8 <see cref="AiItemPrediction"/> demo records and attaches them to the first
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/// 8 eligible <see cref="QuoteItem"/> records, marking those items as AI-analysed.
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/// </summary>
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/// <remarks>
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/// <para>
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/// "Eligible" means <c>SurfaceAreaSqFt > 0</c>, not a labor item, and not already
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/// linked to a prediction. This ensures the seeder is safe to run even if a partial seed
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/// left some items pre-linked.
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/// </para>
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/// <para>
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/// Each prediction record captures a realistic AI analysis: predicted surface area,
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/// estimated minutes, complexity tier, unit price, confidence level, reasoning text, and
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/// comma-separated AI tags. Three of the eight items have <c>UserOverrodeEstimate = true</c>
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/// to demonstrate the override-tracking feature on the AI Accuracy report.
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/// </para>
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/// <para>
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/// Items are updated in-place with <c>IsAiItem = true</c> and the FK
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/// <c>AiPredictionId</c> pointing to the new prediction. <c>SaveChangesAsync</c> is called
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/// per item so any single FK conflict (unlikely in a fresh seed) does not abort the others.
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/// </para>
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/// Idempotency: returns 0 immediately if any AiItemPrediction records already exist for
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/// the company.
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/// </remarks>
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/// <param name="company">The tenant company to seed predictions for.</param>
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/// <returns>Number of prediction records inserted, or 0 if already seeded.</returns>
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private async Task<int> SeedAiPredictionsAsync(Company company)
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{
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var existingCount = await _context.Set<AiItemPrediction>()
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.IgnoreQueryFilters()
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.CountAsync(p => p.CompanyId == company.Id && !p.IsDeleted);
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if (existingCount > 0)
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return 0;
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// Grab the first 8 eligible quote items ordered by id for determinism
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var quoteItems = await _context.Set<QuoteItem>()
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.IgnoreQueryFilters()
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.Include(qi => qi.Quote)
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.Where(qi => qi.CompanyId == company.Id
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&& !qi.IsDeleted
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&& qi.SurfaceAreaSqFt > 0
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&& !qi.IsLaborItem
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&& qi.AiPredictionId == null)
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.OrderBy(qi => qi.Id)
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.Take(8)
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.ToListAsync();
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if (quoteItems.Count == 0)
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return 0;
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// Per-slot prediction specs — deterministic, varied across complexity/confidence tiers.
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// PredictedSqFt is intentionally close but NOT identical to the actual item SqFt so the
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// AI Accuracy report shows realistic prediction deltas.
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var specs = new[]
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{
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// slot 0 — complex automotive, AI nailed it
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( sqft: 13.5m, mins: 88, complexity: "Complex", confidence: "High",
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price: 125.00m, tags: "automotive,tubular,custom", rounds: 1, overrode: false,
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reasoning: "Detected a tubular motorcycle frame with multiple weld joints. High complexity due to intricate geometry and masking requirements around bearing surfaces. Confidence high — similar frames appear frequently in training data." ),
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// slot 1 — wheel set, quick read, accepted as-is
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( sqft: 11.2m, mins: 42, complexity: "Simple", confidence: "High",
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price: 98.00m, tags: "automotive,wheels,aluminum", rounds: 1, overrode: false,
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reasoning: "Four aluminum wheels, uniform shape, minimal masking needed. Straightforward batch candidate for the main oven. Estimated surface area based on standard 18\" wheel profile." ),
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// slot 2 — bumper job, user bumped sqft slightly
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( sqft: 14.8m, mins: 62, complexity: "Moderate", confidence: "Medium",
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price: 135.00m, tags: "automotive,bumper,off-road", rounds: 2, overrode: true,
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reasoning: "Steel off-road bumper and rock sliders. Moderate complexity — flat stock with mounting tabs. Second image round requested for accurate rock slider dimensions. User adjusted surface area slightly after physical measurement." ),
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// slot 3 — large gate, low confidence, user corrected price
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( sqft: 34.2m, mins: 195, complexity: "Complex", confidence: "Low",
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price: 310.00m, tags: "architectural,gate,ornamental", rounds: 2, overrode: true,
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reasoning: "Wrought iron entry gate with decorative scrollwork. Low confidence due to depth ambiguity in photos — scrollwork surface area is difficult to estimate from images alone. Recommend physical measurement before finalising price. User overrode unit price after measuring on-site." ),
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// slot 4 — patio furniture, solid read
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( sqft: 22.8m, mins: 52, complexity: "Moderate", confidence: "High",
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price: 195.00m, tags: "furniture,outdoor,patio", rounds: 1, overrode: false,
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reasoning: "Six-piece patio furniture set: four chairs, one table, one side table. Powder-coated tubular steel, standard outdoor finish. Good photo coverage — confidence high. Recommend Textured Beige or Satin Bronze for exterior durability." ),
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// slot 5 — handrail, accepted price
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( sqft: 39.0m, mins: 118, complexity: "Moderate", confidence: "High",
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price: 342.00m, tags: "architectural,handrail,railing", rounds: 1, overrode: false,
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reasoning: "40-foot steel handrail system, square tube construction. Consistent profile makes area calculation straightforward. Standard Gloss Black most common finish for this application — confirmed with customer." ),
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// slot 6 — brake calipers, small & simple
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( sqft: 3.8m, mins: 30, complexity: "Simple", confidence: "High",
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price: 65.00m, tags: "automotive,brake,caliper", rounds: 1, overrode: false,
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reasoning: "Set of four brake calipers, cast iron with machined mating surfaces. Masking required on piston bores and bleed nipples. Candy Red most requested finish. High confidence — calipers are a common item with well-established pricing." ),
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// slot 7 — bicycle frame, two-round conversation
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( sqft: 6.1m, mins: 65, complexity: "Moderate", confidence: "Medium",
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price: 82.00m, tags: "recreational,bicycle,frame", rounds: 2, overrode: true,
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reasoning: "Road bicycle frame, aluminium alloy. Second image round needed to assess cable routing channels and dropout geometry. Moderate complexity due to small-radius bends. User adjusted surface area after AI initially underestimated top-tube length." )
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};
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var seeded = 0;
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for (int i = 0; i < quoteItems.Count && i < specs.Length; i++)
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{
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var item = quoteItems[i];
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var s = specs[i];
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var prediction = new AiItemPrediction
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{
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PredictedSurfaceAreaSqFt = s.sqft,
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PredictedMinutes = s.mins,
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PredictedComplexity = s.complexity,
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PredictedUnitPrice = s.price,
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Confidence = s.confidence,
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Reasoning = s.reasoning,
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AiTags = s.tags,
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ConversationRounds = s.rounds,
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UserOverrodeEstimate = s.overrode,
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CompanyId = company.Id,
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CreatedAt = item.CreatedAt
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};
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await _context.Set<AiItemPrediction>().AddAsync(prediction);
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await _context.SaveChangesAsync();
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seeded++;
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// Mark the quote item as AI-analysed and link the prediction
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item.IsAiItem = true;
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item.AiPredictionId = prediction.Id;
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item.AiTags = s.tags;
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await _context.SaveChangesAsync();
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}
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return seeded;
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}
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}
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@@ -337,15 +337,122 @@ public partial class SeedDataService
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var startDate = DateTime.Today;
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var appointmentTitles = new Dictionary<string, string[]>
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{
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["DROP_OFF"] = new[] { "Customer Drop-Off", "Parts Delivery", "Item Drop-Off", "Material Drop-Off" },
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["PICK_UP"] = new[] { "Customer Pick-Up", "Collection Appointment", "Order Pick-Up", "Completed Items Pick-Up" },
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["DROP_OFF"] = new[] { "Customer Drop-Off", "Parts Delivery", "Item Drop-Off", "Material Drop-Off" },
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["PICK_UP"] = new[] { "Customer Pick-Up", "Collection Appointment", "Order Pick-Up", "Completed Items Pick-Up" },
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["CONSULTATION"] = new[] { "Quote Discussion", "Project Consultation", "Initial Consultation", "Color Selection Meeting" },
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["JOB_WORK"] = new[] { "Sandblasting Session", "Coating Work", "Quality Inspection", "Final Finishing" }
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["JOB_WORK"] = new[] { "Sandblasting Session", "Coating Work", "Quality Inspection", "Final Finishing" }
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};
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// Get status IDs by code for easy assignment
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var scheduledStatusId = appointmentStatuses.First(s => s.StatusCode == "SCHEDULED").Id;
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var confirmedStatusId = appointmentStatuses.First(s => s.StatusCode == "CONFIRMED").Id;
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var scheduledStatusId = appointmentStatuses.First(s => s.StatusCode == "SCHEDULED").Id;
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var confirmedStatusId = appointmentStatuses.First(s => s.StatusCode == "CONFIRMED").Id;
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var completedStatusId = appointmentStatuses.First(s => s.StatusCode == "COMPLETED").Id;
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var cancelledStatusId = appointmentStatuses.First(s => s.StatusCode == "CANCELLED").Id;
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var noShowStatusId = appointmentStatuses.First(s => s.StatusCode == "NO_SHOW").Id;
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var rescheduledStatusId = appointmentStatuses.First(s => s.StatusCode == "RESCHEDULED").Id;
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// ── PAST APPOINTMENTS (last 90 days) — Completed, Cancelled, No Show ──────
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// Walks backward through weekdays; ~40% chance of an appointment per day
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// for ~25 records spread naturally across the history window.
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var pastRandom = new Random(77); // separate seed keeps past/future independent
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var pastAppointmentSeq = 1;
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static string? CancelNote(Random r)
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{
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var reasons = new[]
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{
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"Customer cancelled — rescheduling for next week.",
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"Customer cancelled — no reason given.",
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"Shop closed for equipment maintenance.",
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"Customer called to reschedule.",
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"Customer unavailable — will call back.",
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"Cancelled by shop — scheduling conflict."
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};
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return reasons[r.Next(reasons.Length)];
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}
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for (int daysBack = 1; daysBack <= 90 && pastAppointmentSeq <= 25; daysBack++)
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{
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var pastDate = DateTime.Today.AddDays(-daysBack);
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if (pastDate.DayOfWeek == DayOfWeek.Saturday || pastDate.DayOfWeek == DayOfWeek.Sunday)
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continue;
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if (pastRandom.Next(100) >= 40) // ~40% chance = ~26 weekday hits over 90 days
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continue;
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var aptType = appointmentTypes[pastRandom.Next(appointmentTypes.Count)];
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var customer = customers[pastRandom.Next(customers.Count)];
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int startHour = pastRandom.Next(8, 17);
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int startMinute = pastRandom.Next(0, 4) * 15;
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var aptStart = new DateTime(pastDate.Year, pastDate.Month, pastDate.Day, startHour, startMinute, 0, DateTimeKind.Utc);
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int duration = pastRandom.Next(1, 5) * 30;
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var aptEnd = aptStart.AddMinutes(duration);
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// 60% Completed, 25% Cancelled, 10% No Show, 5% Rescheduled
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int roll = pastRandom.Next(100);
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int pastStatusId;
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DateTime? actualStart = null, actualEnd = null;
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string? pastNotes = null;
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if (roll < 60)
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{
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pastStatusId = completedStatusId;
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actualStart = aptStart.AddMinutes(pastRandom.Next(-5, 11)); // ±5–10 min variance
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actualEnd = aptEnd.AddMinutes(pastRandom.Next(-10, 16));
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}
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else if (roll < 85)
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{
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pastStatusId = cancelledStatusId;
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pastNotes = CancelNote(pastRandom);
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}
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else if (roll < 95)
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{
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pastStatusId = noShowStatusId;
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pastNotes = "Customer did not arrive. Follow-up call left.";
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}
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else
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{
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pastStatusId = rescheduledStatusId;
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pastNotes = "Rescheduled at customer request — see follow-up appointment.";
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}
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// Optional job link (35% chance for past JOB_WORK; 15% for others)
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int? pastJobId = null;
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if (jobs.Any())
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{
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int linkChance = aptType.TypeCode == "JOB_WORK" ? 35 : 15;
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if (pastRandom.Next(100) < linkChance)
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pastJobId = jobs[pastRandom.Next(jobs.Count)].Id;
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}
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string? assignedId = null;
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if (workers.Any() && pastRandom.Next(100) < 60)
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assignedId = workers[pastRandom.Next(workers.Count)].Id;
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var pastLabel = string.IsNullOrEmpty(customer.CompanyName) ? $"{customer.ContactFirstName} {customer.ContactLastName}".Trim() : customer.CompanyName;
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var pastTitle = $"{pastLabel} — {appointmentTitles[aptType.TypeCode][pastRandom.Next(appointmentTitles[aptType.TypeCode].Length)]}";
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appointments.Add(new Appointment
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{
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AppointmentNumber = $"APT-{pastDate:yyMM}-{pastAppointmentSeq++:D4}",
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CustomerId = customer.Id,
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JobId = pastJobId,
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AppointmentStatusId = pastStatusId,
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AppointmentTypeId = aptType.Id,
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AssignedUserId = assignedId,
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Title = pastTitle,
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ScheduledStartTime = aptStart,
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ScheduledEndTime = aptEnd,
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ActualStartTime = actualStart,
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ActualEndTime = actualEnd,
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IsAllDay = false,
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IsReminderEnabled = false, // reminders don't fire for past appointments
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ReminderMinutesBefore = 30,
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Notes = pastNotes,
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CompanyId = company.Id,
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CreatedAt = aptStart.AddDays(-pastRandom.Next(1, 8)) // booked 1–7 days ahead
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});
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}
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// Generate 50 appointments across next 60 days (weekdays only)
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int appointmentsCreated = 0;
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@@ -388,7 +495,8 @@ public partial class SeedDataService
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int statusId = random.Next(100) < 80 ? scheduledStatusId : confirmedStatusId;
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// Title
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string title = $"{customer.CompanyName} - {appointmentTitles[appointmentType.TypeCode][random.Next(appointmentTitles[appointmentType.TypeCode].Length)]}";
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string customerLabel = string.IsNullOrEmpty(customer.CompanyName) ? $"{customer.ContactFirstName} {customer.ContactLastName}".Trim() : customer.CompanyName;
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string title = $"{customerLabel} - {appointmentTitles[appointmentType.TypeCode][random.Next(appointmentTitles[appointmentType.TypeCode].Length)]}";
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// Optional job link (40% chance if type is JOB_WORK, 20% for others)
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int? jobId = null;
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@@ -180,6 +180,14 @@ public partial class SeedDataService
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if (seededQuoteIds.Any())
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{
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// Collect prediction IDs before removing items (FK is NoAction — predictions
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// must be deleted after the items that reference them are gone).
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var predictionIds = await _context.QuoteItems.IgnoreQueryFilters()
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.Where(qi => seededQuoteIds.Contains(qi.QuoteId) && qi.AiPredictionId != null)
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.Select(qi => qi.AiPredictionId!.Value)
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.Distinct()
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.ToListAsync();
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var quoteItems = await _context.QuoteItems.IgnoreQueryFilters()
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.Where(qi => seededQuoteIds.Contains(qi.QuoteId)).ToListAsync();
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if (quoteItems.Any()) _context.QuoteItems.RemoveRange(quoteItems);
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@@ -193,6 +201,21 @@ public partial class SeedDataService
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_context.Quotes.RemoveRange(quotes);
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totalRemoved += quotes.Count;
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details.Add($"✓ Removed {quotes.Count} seeded quote(s)");
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// Remove orphaned AI predictions now that QuoteItems no longer reference them
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if (predictionIds.Any())
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{
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var predictions = await _context.Set<Core.Entities.AiItemPrediction>()
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.IgnoreQueryFilters()
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.Where(p => predictionIds.Contains(p.Id))
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.ToListAsync();
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if (predictions.Any())
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{
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_context.Set<Core.Entities.AiItemPrediction>().RemoveRange(predictions);
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totalRemoved += predictions.Count;
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details.Add($"✓ Removed {predictions.Count} AI prediction(s)");
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}
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}
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}
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// Customer notes
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@@ -425,6 +425,7 @@ public partial class SeedDataService : ISeedDataService
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await RunSeeder("Time entries", details, errors, result, () => SeedJobTimeEntriesAsync(company));
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await RunSeeder("Inv. txns", details, errors, result, () => SeedInventoryTransactionsAsync(company));
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await RunSeeder("Invoices", details, errors, result, () => SeedInvoicesAsync(company));
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await RunSeeder("AI predictions", details, errors, result, () => SeedAiPredictionsAsync(company));
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await RunSeeder("Vendor bills", details, errors, result, () => SeedBillsAsync(company));
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await RunSeeder("Expenses", details, errors, result, () => SeedExpensesAsync(company));
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await RunSeeder("Appointments", details, errors, result, () => SeedAppointmentsAsync(company));
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Reference in New Issue
Block a user