Janady13/AI_Brain / services/seeder_api/app.py

1#!/usr/bin/env python3 2import os 3import time 4from fastapi import FastAPI 5from pydantic import BaseModel 6 7from .intelligence import ( 8 AmazonProductRecommendation, 9 AWSRevenueRecommendation, 10 AutomationSprint, 11 AutomationTask, 12 BudgetPlan, 13 CampaignMetrics, 14 CampaignScore, 15 CJAffiliateRecommendation, 16 CryptoMarketSnapshot, 17 EbayRevenuePlay, 18 GrowthExperiment, 19 GoogleCampaignPerformance, 20 CompetitiveSignal, 21 InfluencerRecommendation, 22 LearningLoopRecommendation, 23 ExcellenceSummary, 24 ExecutivePlaybook, 25 StrategicDirective, 26 ScenarioAnalysis, 27 ScenarioProjection, 28 MarketingIntelligenceEngine, 29 MarketingCampaign, 30 PromptDiagnostics, 31 StripeMonetizationPlan, 32 PayPalMonetizationPlan, 33 default_marketing_engine, 34) 35 36APP_NAME = os.getenv("APP_NAME", "HiveSeedAPI") 37START_TS = time.time() 38 39app = FastAPI(title=APP_NAME) 40 41class Health(BaseModel): 42 status: str 43 uptime_seconds: float 44 45class Ready(BaseModel): 46 ready: bool 47 reason: str | None = None 48 49 50class MarketingQuery(BaseModel): 51 prompt: str 52 limit: int = 3 53 54 55class MarketingInsight(BaseModel): 56 channel: str 57 audience: str 58 offer: str 59 objective: str 60 rationale: str 61 62 63class MarketingResponse(BaseModel): 64 query: str 65 recommendations: list[MarketingInsight] 66 insight: str 67 68 69class StatisticalInsight(BaseModel): 70 channel: str 71 audience: str 72 offer: str 73 objective: str 74 expected_roi: float 75 expected_ctr: float 76 confidence: float 77 rationale: str 78 drivers: dict[str, float] 79 80 81class PortfolioInsight(BaseModel): 82 expected_roi: float 83 expected_ctr: float 84 confidence: float 85 rationale: str 86 strongest_drivers: dict[str, float] 87 88 89class StatisticalResponse(BaseModel): 90 query: str 91 recommendations: list[StatisticalInsight] 92 portfolio: PortfolioInsight 93 94 95class DiagnosticResponse(BaseModel): 96 query: str 97 stage: str 98 urgency: str 99 focus: str 100 confidence: float 101 recommended_kpis: list[str] 102 opportunities: list[str] 103 risk_signals: list[str] 104 keyword_summary: dict[str, list[str]] 105 106 107class ProcessRun(BaseModel): 108 process: str 109 recommendations: list[MarketingInsight] 110 summary: str 111 112 113class ExperimentInsight(BaseModel): 114 name: str 115 hypothesis: str 116 primary_metric: str 117 secondary_metrics: list[str] 118 playbook: list[str] 119 confidence: float 120 121 122class LearningLoopInsight(BaseModel): 123 play_name: str 124 stage: str 125 audience_focus: str 126 channel: str 127 learning: str 128 wins: list[str] 129 losses: list[str] 130 next_actions: list[str] 131 kpi_focus: list[str] 132 confidence: float 133 source: str 134 notes: str | None = None 135 136 137class CompetitiveLandscapeInsight(BaseModel): 138 competitor: str 139 channel: str 140 summary: str 141 strengths: list[str] 142 weaknesses: list[str] 143 recommended_play: str 144 share_of_voice: float 145 signal_intensity: float 146 threat_level: str 147 focus_alignment: float 148 source: str 149 notes: str | None = None 150 151 152class ExcellenceInsight(BaseModel): 153 excellence_index: float 154 maturity_level: str 155 headline: str 156 strengths: list[str] 157 watchouts: list[str] 158 process_coverage: dict[str, float] 159 notes: str | None = None 160 161 162class StrategicDirectiveInsight(BaseModel): 163 name: str 164 priority: int 165 objective: str 166 primary_channel: str 167 supporting_channels: list[str] 168 investment_usd: float | None 169 expected_revenue_usd: float | None 170 confidence: float 171 time_horizon_hours: int 172 north_star_metric: str 173 supporting_metrics: list[str] 174 action_steps: list[str] 175 dependencies: list[str] 176 risk_mitigations: list[str] 177 inspiration: str 178 source: str 179 notes: str | None = None 180 181 182class ExecutivePlaybookInsight(BaseModel): 183 headline: str 184 focus: str 185 urgency: str 186 north_star_metric: str 187 total_budget_usd: float | None 188 expected_revenue_usd: float | None 189 confidence: float 190 directives: list[StrategicDirectiveInsight] 191 summary: str 192 notes: str | None = None 193 194 195class ScenarioProjectionInsight(BaseModel): 196 scenario: str 197 expected_revenue: float 198 expected_roi: float 199 probability: float 200 confidence: float 201 accelerators: list[str] 202 stabilizers: list[str] 203 watchouts: list[str] 204 source: str 205 notes: str | None = None 206 207 208class ScenarioPlanningInsight(BaseModel): 209 headline: str 210 summary: str 211 projections: list[ScenarioProjectionInsight] 212 confidence: float 213 accelerators: list[str] 214 stabilizers: list[str] 215 source: str 216 notes: str | None = None 217 218 219class MarketingProcessesResponse(BaseModel): 220 query: str 221 rag: ProcessRun 222 egg: list[ExperimentInsight] 223 google_ads: list["GoogleAdsInsight"] 224 cj_affiliate: list["CJAffiliateInsight"] 225 crypto_markets: list["CryptoMarketInsight"] 226 stripe: list["StripePlanInsight"] 227 paypal: list["PayPalPlanInsight"] 228 influencer_collaborations: list["InfluencerInsight"] 229 amazon_marketplace: list["AmazonMarketplaceInsight"] 230 ebay_marketplace: list["EbayMarketplaceInsight"] 231 aws_cloud: list["AWSRevenueInsight"] 232 budget_optimizer: "BudgetPlanInsight" 233 automation: "AutomationSprintInsight" 234 learning_loop: list["LearningLoopInsight"] 235 competitive_landscape: list["CompetitiveLandscapeInsight"] 236 excellence: "ExcellenceInsight" 237 executive_playbook: "ExecutivePlaybookInsight" 238 scenario_planning: "ScenarioPlanningInsight" 239 240 241class GoogleAdsInsight(BaseModel): 242 campaign_name: str 243 clicks: int 244 impressions: int 245 conversions: float 246 cost_micros: int 247 ctr: float 248 cpc: float 249 roas: float 250 source: str 251 notes: str | None = None 252 253 254class CJAffiliateInsight(BaseModel): 255 program_name: str 256 advertiser_id: str 257 category: str 258 commission_rate: float 259 epc: float 260 seven_day_epc: float 261 projected_epc: float 262 projected_monthly_revenue: float 263 network_rank: int 264 payout_terms: str 265 landing_url: str 266 rationale: str 267 source: str 268 notes: str | None = None 269 270 271class CryptoMarketInsight(BaseModel): 272 asset: str 273 price_usd: float | None 274 change_24h: float | None 275 market_cap: float | None 276 volume_24h: float | None 277 source: str 278 notes: str | None = None 279 280 281class StripePlanInsight(BaseModel): 282 product_name: str 283 price_usd: float 284 billing_interval: str 285 currency: str 286 rationale: str 287 setup_steps: list[str] 288 source: str 289 notes: str | None = None 290 291 292class PayPalPlanInsight(BaseModel): 293 product_name: str 294 price_usd: float 295 checkout_flow: str 296 payout_schedule: str 297 rationale: str 298 setup_steps: list[str] 299 source: str 300 notes: str | None = None 301 302 303class InfluencerInsight(BaseModel): 304 name: str 305 platform: str 306 niche: str 307 collab_format: str 308 audience_size: int 309 engagement_rate: float 310 estimated_reach: int 311 projected_cpm: float 312 projected_leads: int 313 projected_revenue: float 314 projected_roi: float 315 rationale: str 316 source: str 317 notes: str | None = None 318 319 320class AmazonMarketplaceInsight(BaseModel): 321 asin: str 322 title: str 323 category: str 324 price_usd: float 325 commission_rate: float 326 rating: float 327 reviews: int 328 estimated_monthly_sales: int 329 buy_box_share: float 330 prime_eligible: bool 331 fulfillment_model: str 332 projected_monthly_revenue: float 333 rationale: str 334 url: str 335 source: str 336 notes: str | None = None 337 338 339class EbayMarketplaceInsight(BaseModel): 340 item_id: str 341 title: str 342 category: str 343 price_usd: float 344 affiliate_rate: float 345 seller_level: str 346 seller_rating: float 347 conversion_rate: float 348 sold_per_month: int 349 projected_monthly_revenue: float 350 projected_profit_margin: float 351 shipping_days: int 352 rationale: str 353 url: str 354 source: str 355 notes: str | None = None 356 357 358class AWSRevenueInsight(BaseModel): 359 program_name: str 360 focus: str 361 description: str 362 revenue_motion: str 363 primary_service: str 364 secondary_services: list[str] 365 automation_play: str 366 projected_roi: float 367 source: str 368 notes: str | None = None 369 370 371class BudgetAllocationInsight(BaseModel): 372 channel: str 373 audience_focus: str 374 offer: str 375 objective: str 376 allocation_usd: float 377 allocation_weight: float 378 expected_leads: float 379 expected_revenue: float 380 rationale: str 381 source: str 382 notes: str | None = None 383 384 385class BudgetPlanInsight(BaseModel): 386 total_budget: float 387 expected_revenue: float 388 expected_roi: float 389 payback_period_weeks: float 390 summary: str 391 allocations: list[BudgetAllocationInsight] 392 393 394class AutomationTaskInsight(BaseModel): 395 start_minute: int 396 end_minute: int 397 activity: str 398 outcome: str 399 tooling: list[str] 400 kpis: list[str] 401 automation_level: str 402 source: str 403 404 405class AutomationSprintInsight(BaseModel): 406 total_minutes: int 407 stage: str 408 focus: str 409 summary: str 410 notes: str | None = None 411 tasks: list[AutomationTaskInsight] 412 source: str 413 414 415try: # Pydantic v2 416 GoogleAdsInsight.model_rebuild() 417 CJAffiliateInsight.model_rebuild() 418 CryptoMarketInsight.model_rebuild() 419 StripePlanInsight.model_rebuild() 420 PayPalPlanInsight.model_rebuild() 421 InfluencerInsight.model_rebuild() 422 AmazonMarketplaceInsight.model_rebuild() 423 EbayMarketplaceInsight.model_rebuild() 424 AWSRevenueInsight.model_rebuild() 425 BudgetAllocationInsight.model_rebuild() 426 BudgetPlanInsight.model_rebuild() 427 AutomationTaskInsight.model_rebuild() 428 AutomationSprintInsight.model_rebuild() 429 LearningLoopInsight.model_rebuild() 430 CompetitiveLandscapeInsight.model_rebuild() 431 ExcellenceInsight.model_rebuild() 432 StrategicDirectiveInsight.model_rebuild() 433 ExecutivePlaybookInsight.model_rebuild() 434 ScenarioProjectionInsight.model_rebuild() 435 ScenarioPlanningInsight.model_rebuild() 436 MarketingProcessesResponse.model_rebuild() 437except AttributeError: # pragma: no cover - pydantic v1 fallback 438 MarketingProcessesResponse.update_forward_refs( 439 GoogleAdsInsight=GoogleAdsInsight, 440 CJAffiliateInsight=CJAffiliateInsight, 441 CryptoMarketInsight=CryptoMarketInsight, 442 StripePlanInsight=StripePlanInsight, 443 PayPalPlanInsight=PayPalPlanInsight, 444 InfluencerInsight=InfluencerInsight, 445 AmazonMarketplaceInsight=AmazonMarketplaceInsight, 446 EbayMarketplaceInsight=EbayMarketplaceInsight, 447 AWSRevenueInsight=AWSRevenueInsight, 448 BudgetAllocationInsight=BudgetAllocationInsight, 449 BudgetPlanInsight=BudgetPlanInsight, 450 AutomationTaskInsight=AutomationTaskInsight, 451 AutomationSprintInsight=AutomationSprintInsight, 452 LearningLoopInsight=LearningLoopInsight, 453 CompetitiveLandscapeInsight=CompetitiveLandscapeInsight, 454 ExcellenceInsight=ExcellenceInsight, 455 StrategicDirectiveInsight=StrategicDirectiveInsight, 456 ExecutivePlaybookInsight=ExecutivePlaybookInsight, 457 ScenarioProjectionInsight=ScenarioProjectionInsight, 458 ScenarioPlanningInsight=ScenarioPlanningInsight, 459 ) 460 461 462@app.get("/healthz", response_model=Health) 463def healthz() -> Health: 464 return Health(status="ok", uptime_seconds=time.time() - START_TS) 465 466@app.get("/readyz", response_model=Ready) 467def readyz() -> Ready: 468 # Minimal readiness: enumerations exist or DRY_RUN enabled 469 base = os.path.join(os.path.dirname(__file__), "..", "..", "daemon", "enumerations") 470 files = [ 471 os.path.join(base, "math_model_500.txt"), 472 os.path.join(base, "agent_500.txt"), 473 os.path.join(base, "language_500.txt"), 474 os.path.join(base, "prophecy_500.txt"), 475 os.path.join(base, "marketing_500.txt"), 476 ] 477 if all(os.path.isfile(p) for p in files): 478 return Ready(ready=True) 479 if os.getenv("DRY_RUN", "0") in {"1", "true", "TRUE", "yes", "on"}: 480 return Ready(ready=True, reason="DRY_RUN enabled") 481 return Ready(ready=False, reason="Enumerations missing; run make enumerations") 482 483 484def _campaign_to_insight(engine: MarketingIntelligenceEngine, query: str, campaign: MarketingCampaign) -> MarketingInsight: 485 tokens = engine.keywords(query) 486 matches: list[str] = [] 487 normalized = { 488 "channel": campaign.channel.lower(), 489 "audience": campaign.audience.lower(), 490 "offer": campaign.offer.lower(), 491 "objective": campaign.objective.lower(), 492 } 493 for token in tokens: 494 for feature, text in normalized.items(): 495 if token in text and feature not in matches: 496 matches.append(feature) 497 rationale = "Matched: " + ", ".join(matches) if matches else "General fit from curated library" 498 return MarketingInsight( 499 channel=campaign.channel, 500 audience=campaign.audience, 501 offer=campaign.offer, 502 objective=campaign.objective, 503 rationale=rationale, 504 ) 505 506 507def _campaign_to_statistical_insight( 508 engine: MarketingIntelligenceEngine, 509 query: str, 510 scored: CampaignScore, 511) -> StatisticalInsight: 512 matches: list[str] = [] 513 for feature, count in scored.keyword_coverage.items(): 514 if count: 515 matches.append(feature) 516 517 if matches: 518 rationale = f"Matched {', '.join(sorted(matches))} intent with statistical lift." 519 else: 520 rationale = "Benchmark-driven pick from marketing corpus." 521 522 strongest = sorted(scored.metrics.drivers.items(), key=lambda item: item[1], reverse=True) 523 if strongest: 524 rationale += f" Top driver: {strongest[0][0]} ROI {strongest[0][1]:.2f}." 525 526 return StatisticalInsight( 527 channel=scored.campaign.channel, 528 audience=scored.campaign.audience, 529 offer=scored.campaign.offer, 530 objective=scored.campaign.objective, 531 expected_roi=round(scored.metrics.expected_roi, 2), 532 expected_ctr=round(scored.metrics.expected_ctr, 4), 533 confidence=round(scored.metrics.confidence, 2), 534 rationale=rationale, 535 drivers={key: round(value, 2) for key, value in scored.metrics.drivers.items()}, 536 ) 537 538 539def _portfolio_to_insight(query: str, portfolio: CampaignMetrics, tokens: list[str]) -> PortfolioInsight: 540 highlights = ", ".join(sorted(set(tokens))) or "broad" 541 strongest = sorted(portfolio.drivers.items(), key=lambda item: item[1], reverse=True) 542 driver_text = strongest[0][0] if strongest else "channel" 543 rationale = ( 544 f"Statistical blend for '{query}' optimized for {highlights} focus; strongest lift from {driver_text}." 545 ) 546 return PortfolioInsight( 547 expected_roi=round(portfolio.expected_roi, 2), 548 expected_ctr=round(portfolio.expected_ctr, 4), 549 confidence=round(portfolio.confidence, 2), 550 rationale=rationale, 551 strongest_drivers={key: round(value, 2) for key, value in portfolio.drivers.items()}, 552 ) 553 554 555@app.post("/intelligence/marketing", response_model=MarketingResponse) 556def marketing_intelligence(query: MarketingQuery) -> MarketingResponse: 557 engine = default_marketing_engine() 558 campaigns = engine.recommend(query.prompt, query.limit) 559 insights = [_campaign_to_insight(engine, query.prompt, campaign) for campaign in campaigns] 560 narrative = engine.summarize(query.prompt, campaigns) 561 return MarketingResponse(query=query.prompt, recommendations=insights, insight=narrative) 562 563 564@app.post("/intelligence/marketing/statistical", response_model=StatisticalResponse) 565def marketing_statistical_intelligence(query: MarketingQuery) -> StatisticalResponse: 566 engine = default_marketing_engine() 567 ranked = engine.recommend_statistical(query.prompt, query.limit) 568 insights = [ 569 _campaign_to_statistical_insight(engine, query.prompt, scored) for scored in ranked 570 ] 571 tokens = engine.keywords(query.prompt) 572 portfolio_metrics = engine.portfolio(query.prompt, ranked) 573 portfolio = _portfolio_to_insight(query.prompt, portfolio_metrics, tokens) 574 return StatisticalResponse(query=query.prompt, recommendations=insights, portfolio=portfolio) 575 576 577def _diagnostics_payload(query: str, diagnostics: PromptDiagnostics) -> DiagnosticResponse: 578 return DiagnosticResponse( 579 query=query, 580 stage=diagnostics.stage, 581 urgency=diagnostics.urgency, 582 focus=diagnostics.focus, 583 confidence=diagnostics.confidence, 584 recommended_kpis=diagnostics.recommended_kpis, 585 opportunities=diagnostics.opportunities, 586 risk_signals=diagnostics.risk_signals, 587 keyword_summary=diagnostics.keyword_summary, 588 ) 589 590 591def _experiment_payload(experiment: GrowthExperiment) -> ExperimentInsight: 592 return ExperimentInsight( 593 name=experiment.name, 594 hypothesis=experiment.hypothesis, 595 primary_metric=experiment.primary_metric, 596 secondary_metrics=experiment.secondary_metrics, 597 playbook=experiment.playbook, 598 confidence=experiment.confidence, 599 ) 600 601 602def _google_ads_payload(performance: GoogleCampaignPerformance) -> "GoogleAdsInsight": 603 return GoogleAdsInsight( 604 campaign_name=performance.campaign_name, 605 clicks=performance.clicks, 606 impressions=performance.impressions, 607 conversions=performance.conversions, 608 cost_micros=performance.cost_micros, 609 ctr=performance.ctr, 610 cpc=performance.cpc, 611 roas=performance.roas, 612 source=performance.source, 613 notes=performance.notes, 614 ) 615 616 617def _cj_affiliate_payload(recommendation: CJAffiliateRecommendation) -> "CJAffiliateInsight": 618 return CJAffiliateInsight( 619 program_name=recommendation.program_name, 620 advertiser_id=recommendation.advertiser_id, 621 category=recommendation.category, 622 commission_rate=recommendation.commission_rate, 623 epc=recommendation.epc, 624 seven_day_epc=recommendation.seven_day_epc, 625 projected_epc=recommendation.projected_epc, 626 projected_monthly_revenue=recommendation.projected_monthly_revenue, 627 network_rank=recommendation.network_rank, 628 payout_terms=recommendation.payout_terms, 629 landing_url=recommendation.landing_url, 630 rationale=recommendation.rationale, 631 source=recommendation.source, 632 notes=recommendation.notes, 633 ) 634 635 636def _crypto_market_payload(snapshot: CryptoMarketSnapshot) -> "CryptoMarketInsight": 637 return CryptoMarketInsight( 638 asset=snapshot.asset, 639 price_usd=snapshot.price_usd, 640 change_24h=snapshot.change_24h, 641 market_cap=snapshot.market_cap, 642 volume_24h=snapshot.volume_24h, 643 source=snapshot.source, 644 notes=snapshot.notes, 645 ) 646 647 648def _stripe_plan_payload(plan: StripeMonetizationPlan) -> "StripePlanInsight": 649 return StripePlanInsight( 650 product_name=plan.product_name, 651 price_usd=plan.price_usd, 652 billing_interval=plan.billing_interval, 653 currency=plan.currency, 654 rationale=plan.rationale, 655 setup_steps=plan.setup_steps, 656 source=plan.source, 657 notes=plan.notes, 658 ) 659 660 661def _paypal_plan_payload(plan: PayPalMonetizationPlan) -> "PayPalPlanInsight": 662 return PayPalPlanInsight( 663 product_name=plan.product_name, 664 price_usd=plan.price_usd, 665 checkout_flow=plan.checkout_flow, 666 payout_schedule=plan.payout_schedule, 667 rationale=plan.rationale, 668 setup_steps=plan.setup_steps, 669 source=plan.source, 670 notes=plan.notes, 671 ) 672 673 674def _influencer_payload( 675 recommendation: InfluencerRecommendation, 676) -> "InfluencerInsight": 677 return InfluencerInsight( 678 name=recommendation.name, 679 platform=recommendation.platform, 680 niche=recommendation.niche, 681 collab_format=recommendation.collab_format, 682 audience_size=recommendation.audience_size, 683 engagement_rate=recommendation.engagement_rate, 684 estimated_reach=recommendation.estimated_reach, 685 projected_cpm=recommendation.projected_cpm, 686 projected_leads=recommendation.projected_leads, 687 projected_revenue=recommendation.projected_revenue, 688 projected_roi=recommendation.projected_roi, 689 rationale=recommendation.rationale, 690 source=recommendation.source, 691 notes=recommendation.notes, 692 ) 693 694 695def _amazon_listing_payload( 696 recommendation: AmazonProductRecommendation, 697) -> "AmazonMarketplaceInsight": 698 return AmazonMarketplaceInsight( 699 asin=recommendation.asin, 700 title=recommendation.title, 701 category=recommendation.category, 702 price_usd=recommendation.price_usd, 703 commission_rate=recommendation.commission_rate, 704 rating=recommendation.rating, 705 reviews=recommendation.reviews, 706 estimated_monthly_sales=recommendation.estimated_monthly_sales, 707 buy_box_share=recommendation.buy_box_share, 708 prime_eligible=recommendation.prime_eligible, 709 fulfillment_model=recommendation.fulfillment_model, 710 projected_monthly_revenue=recommendation.projected_monthly_revenue, 711 rationale=recommendation.rationale, 712 url=recommendation.url, 713 source=recommendation.source, 714 notes=recommendation.notes, 715 ) 716 717 718def _ebay_listing_payload(recommendation: EbayRevenuePlay) -> "EbayMarketplaceInsight": 719 return EbayMarketplaceInsight( 720 item_id=recommendation.item_id, 721 title=recommendation.title, 722 category=recommendation.category, 723 price_usd=recommendation.price_usd, 724 affiliate_rate=recommendation.affiliate_rate, 725 seller_level=recommendation.seller_level, 726 seller_rating=recommendation.seller_rating, 727 conversion_rate=recommendation.conversion_rate, 728 sold_per_month=recommendation.sold_per_month, 729 projected_monthly_revenue=recommendation.projected_monthly_revenue, 730 projected_profit_margin=recommendation.projected_profit_margin, 731 shipping_days=recommendation.shipping_days, 732 rationale=recommendation.rationale, 733 url=recommendation.url, 734 source=recommendation.source, 735 notes=recommendation.notes, 736 ) 737 738 739def _aws_revenue_payload(recommendation: AWSRevenueRecommendation) -> "AWSRevenueInsight": 740 return AWSRevenueInsight( 741 program_name=recommendation.program_name, 742 focus=recommendation.focus, 743 description=recommendation.description, 744 revenue_motion=recommendation.revenue_motion, 745 primary_service=recommendation.primary_service, 746 secondary_services=recommendation.secondary_services, 747 automation_play=recommendation.automation_play, 748 projected_roi=recommendation.projected_roi, 749 source=recommendation.source, 750 notes=recommendation.notes, 751 ) 752 753 754def _budget_plan_payload(plan: BudgetPlan) -> "BudgetPlanInsight": 755 allocations = [ 756 BudgetAllocationInsight( 757 channel=allocation.channel, 758 audience_focus=allocation.audience_focus, 759 offer=allocation.offer, 760 objective=allocation.objective, 761 allocation_usd=allocation.allocation_usd, 762 allocation_weight=allocation.allocation_weight, 763 expected_leads=allocation.expected_leads, 764 expected_revenue=allocation.expected_revenue, 765 rationale=allocation.rationale, 766 source=allocation.source, 767 notes=allocation.notes, 768 ) 769 for allocation in plan.allocations 770 ] 771 772 return BudgetPlanInsight( 773 total_budget=plan.total_budget, 774 expected_revenue=plan.expected_revenue, 775 expected_roi=plan.expected_roi, 776 payback_period_weeks=plan.payback_period_weeks, 777 summary=plan.summary, 778 allocations=allocations, 779 ) 780 781 782def _automation_task_payload(task: AutomationTask) -> AutomationTaskInsight: 783 return AutomationTaskInsight( 784 start_minute=task.start_minute, 785 end_minute=task.end_minute, 786 activity=task.activity, 787 outcome=task.outcome, 788 tooling=task.tooling, 789 kpis=task.kpis, 790 automation_level=task.automation_level, 791 source=task.source, 792 ) 793 794 795def _automation_sprint_payload(sprint: AutomationSprint) -> AutomationSprintInsight: 796 return AutomationSprintInsight( 797 total_minutes=sprint.total_minutes, 798 stage=sprint.stage, 799 focus=sprint.focus, 800 summary=sprint.summary, 801 notes=sprint.notes, 802 tasks=[_automation_task_payload(task) for task in sprint.tasks], 803 source=sprint.source, 804 ) 805 806 807def _learning_loop_payload(recommendation: LearningLoopRecommendation) -> "LearningLoopInsight": 808 return LearningLoopInsight( 809 play_name=recommendation.play_name, 810 stage=recommendation.stage, 811 audience_focus=recommendation.audience_focus, 812 channel=recommendation.channel, 813 learning=recommendation.learning, 814 wins=recommendation.wins, 815 losses=recommendation.losses, 816 next_actions=recommendation.next_actions, 817 kpi_focus=recommendation.kpi_focus, 818 confidence=recommendation.confidence, 819 source=recommendation.source, 820 notes=recommendation.notes, 821 ) 822 823 824def _competitive_signal_payload(signal: CompetitiveSignal) -> "CompetitiveLandscapeInsight": 825 return CompetitiveLandscapeInsight( 826 competitor=signal.competitor, 827 channel=signal.channel, 828 summary=signal.summary, 829 strengths=signal.strengths, 830 weaknesses=signal.weaknesses, 831 recommended_play=signal.recommended_play, 832 share_of_voice=signal.share_of_voice, 833 signal_intensity=signal.signal_intensity, 834 threat_level=signal.threat_level, 835 focus_alignment=signal.focus_alignment, 836 source=signal.source, 837 notes=signal.notes, 838 ) 839 840 841def _excellence_payload(summary: ExcellenceSummary) -> ExcellenceInsight: 842 return ExcellenceInsight( 843 excellence_index=summary.excellence_index, 844 maturity_level=summary.maturity_level, 845 headline=summary.headline, 846 strengths=summary.strengths, 847 watchouts=summary.watchouts, 848 process_coverage=summary.process_coverage, 849 notes=summary.notes, 850 ) 851 852 853def _directive_payload(directive: StrategicDirective) -> StrategicDirectiveInsight: 854 return StrategicDirectiveInsight( 855 name=directive.name, 856 priority=directive.priority, 857 objective=directive.objective, 858 primary_channel=directive.primary_channel, 859 supporting_channels=directive.supporting_channels, 860 investment_usd=directive.investment_usd, 861 expected_revenue_usd=directive.expected_revenue_usd, 862 confidence=directive.confidence, 863 time_horizon_hours=directive.time_horizon_hours, 864 north_star_metric=directive.north_star_metric, 865 supporting_metrics=directive.supporting_metrics, 866 action_steps=directive.action_steps, 867 dependencies=directive.dependencies, 868 risk_mitigations=directive.risk_mitigations, 869 inspiration=directive.inspiration, 870 source=directive.source, 871 notes=directive.notes, 872 ) 873 874 875def _playbook_payload(playbook: ExecutivePlaybook) -> ExecutivePlaybookInsight: 876 return ExecutivePlaybookInsight( 877 headline=playbook.headline, 878 focus=playbook.focus, 879 urgency=playbook.urgency, 880 north_star_metric=playbook.north_star_metric, 881 total_budget_usd=playbook.total_budget_usd, 882 expected_revenue_usd=playbook.expected_revenue_usd, 883 confidence=playbook.confidence, 884 directives=[_directive_payload(directive) for directive in playbook.directives], 885 summary=playbook.summary, 886 notes=playbook.notes, 887 ) 888 889 890def _scenario_projection_payload(projection: ScenarioProjection) -> ScenarioProjectionInsight: 891 return ScenarioProjectionInsight( 892 scenario=projection.scenario, 893 expected_revenue=projection.expected_revenue, 894 expected_roi=projection.expected_roi, 895 probability=projection.probability, 896 confidence=projection.confidence, 897 accelerators=projection.accelerators, 898 stabilizers=projection.stabilizers, 899 watchouts=projection.watchouts, 900 source=projection.source, 901 notes=projection.notes, 902 ) 903 904 905def _scenario_planning_payload(analysis: ScenarioAnalysis) -> ScenarioPlanningInsight: 906 return ScenarioPlanningInsight( 907 headline=analysis.headline, 908 summary=analysis.summary, 909 projections=[_scenario_projection_payload(proj) for proj in analysis.projections], 910 confidence=analysis.confidence, 911 accelerators=analysis.accelerators, 912 stabilizers=analysis.stabilizers, 913 source=analysis.source, 914 notes=analysis.notes, 915 ) 916 917 918@app.post("/intelligence/marketing/diagnostics", response_model=DiagnosticResponse) 919def marketing_diagnostics(query: MarketingQuery) -> DiagnosticResponse: 920 engine = default_marketing_engine() 921 diagnostics = engine.diagnose(query.prompt) 922 return _diagnostics_payload(query.prompt, diagnostics) 923 924 925@app.post("/intelligence/marketing/processes", response_model=MarketingProcessesResponse) 926def marketing_processes(query: MarketingQuery) -> MarketingProcessesResponse: 927 engine = default_marketing_engine() 928 rag_campaigns = engine.recommend(query.prompt, query.limit) 929 rag_insights = [ 930 _campaign_to_insight(engine, query.prompt, campaign) for campaign in rag_campaigns 931 ] 932 rag_summary = engine.summarize(query.prompt, rag_campaigns) 933 934 ranked = engine.recommend_statistical(query.prompt, query.limit) 935 egg_experiments = engine.expansion_grid(query.prompt, ranked) 936 google_ads_data = engine.google_ads_enrichment(query.prompt, ranked, query.limit) 937 cj_affiliate = engine.cj_affiliate_enrichment(query.prompt, ranked, query.limit) 938 crypto_markets = engine.crypto_market_enrichment(query.prompt, query.limit) 939 stripe_plans = engine.stripe_checkout_enrichment(query.prompt, ranked, query.limit) 940 paypal_plans = engine.paypal_checkout_enrichment(query.prompt, ranked, query.limit) 941 influencer_partners = engine.influencer_collaboration_enrichment( 942 query.prompt, ranked, query.limit 943 ) 944 amazon_listings = engine.amazon_marketplace_enrichment(query.prompt, ranked, query.limit) 945 ebay_listings = engine.ebay_marketplace_enrichment(query.prompt, ranked, query.limit) 946 aws_programs = engine.aws_cloud_enrichment(query.prompt, ranked, query.limit) 947 budget_plan = engine.budget_optimizer(query.prompt, ranked, query.limit) 948 automation_run = engine.automation_sprint(query.prompt, ranked, query.limit) 949 learning_recommendations = engine.learning_loop(query.prompt, ranked, query.limit) 950 competitive_signals = engine.competitive_landscape(query.prompt, ranked, query.limit) 951 excellence = engine.excellence_summary( 952 query.prompt, 953 ranked, 954 limit=query.limit, 955 rag=rag_campaigns, 956 egg=egg_experiments, 957 google_ads=google_ads_data, 958 cj_affiliate=cj_affiliate, 959 crypto_markets=crypto_markets, 960 stripe=stripe_plans, 961 paypal=paypal_plans, 962 influencers=influencer_partners, 963 amazon=amazon_listings, 964 ebay=ebay_listings, 965 aws=aws_programs, 966 budget=budget_plan, 967 automation=automation_run, 968 learning_loop=learning_recommendations, 969 competitive=competitive_signals, 970 ) 971 scenario_analysis = engine.scenario_planning( 972 query.prompt, 973 ranked, 974 limit=query.limit, 975 budget=budget_plan, 976 google_ads=google_ads_data, 977 cj_affiliate=cj_affiliate, 978 amazon=amazon_listings, 979 ebay=ebay_listings, 980 aws=aws_programs, 981 stripe=stripe_plans, 982 paypal=paypal_plans, 983 influencers=influencer_partners, 984 crypto=crypto_markets, 985 ) 986 playbook = engine.executive_playbook( 987 query.prompt, 988 ranked, 989 limit=query.limit, 990 rag=rag_campaigns, 991 egg=egg_experiments, 992 google_ads=google_ads_data, 993 cj_affiliate=cj_affiliate, 994 amazon=amazon_listings, 995 ebay=ebay_listings, 996 paypal=paypal_plans, 997 influencers=influencer_partners, 998 budget=budget_plan, 999 automation=automation_run, 1000 learning_loop=learning_recommendations, 1001 competitive=competitive_signals, 1002 excellence=excellence, 1003 ) 1004 amazon_listings = engine.amazon_marketplace_enrichment(query.prompt, ranked, query.limit) 1005 aws_programs = engine.aws_cloud_enrichment(query.prompt, ranked, query.limit) 1006 1007 return MarketingProcessesResponse( 1008 query=query.prompt, 1009 rag=ProcessRun(process="rag", recommendations=rag_insights, summary=rag_summary), 1010 egg=[_experiment_payload(experiment) for experiment in egg_experiments], 1011 google_ads=[_google_ads_payload(performance) for performance in google_ads_data], 1012 cj_affiliate=[_cj_affiliate_payload(rec) for rec in cj_affiliate], 1013 crypto_markets=[_crypto_market_payload(snapshot) for snapshot in crypto_markets], 1014 stripe=[_stripe_plan_payload(plan) for plan in stripe_plans], 1015 paypal=[_paypal_plan_payload(plan) for plan in paypal_plans], 1016 influencer_collaborations=[ 1017 _influencer_payload(partner) for partner in influencer_partners 1018 ], 1019 amazon_marketplace=[_amazon_listing_payload(rec) for rec in amazon_listings], 1020 ebay_marketplace=[_ebay_listing_payload(rec) for rec in ebay_listings], 1021 aws_cloud=[_aws_revenue_payload(rec) for rec in aws_programs], 1022 budget_optimizer=_budget_plan_payload(budget_plan), 1023 automation=_automation_sprint_payload(automation_run), 1024 learning_loop=[_learning_loop_payload(rec) for rec in learning_recommendations], 1025 competitive_landscape=[_competitive_signal_payload(signal) for signal in competitive_signals], 1026 excellence=_excellence_payload(excellence), 1027 executive_playbook=_playbook_payload(playbook), 1028 scenario_planning=_scenario_planning_payload(scenario_analysis), 1029 amazon_marketplace=[_amazon_listing_payload(rec) for rec in amazon_listings], 1030 aws_cloud=[_aws_revenue_payload(rec) for rec in aws_programs], 1031 ) 1032 1033