from services.seeder_api.app import (
    MarketingQuery,
    marketing_diagnostics,
    marketing_intelligence,
    marketing_processes,
    marketing_statistical_intelligence,
)
from services.seeder_api.intelligence import (
    MarketingCampaign,
    MarketingIntelligenceEngine,
)


def test_marketing_campaign_parsing_handles_standard_format():
    raw = (
        "Campaign: social ads targeting climate tech founders leveraging video testimonial to drive product demos."
    )
    campaign = MarketingCampaign.parse(raw)
    assert campaign.channel == "social ads"
    assert campaign.audience == "climate tech founders"
    assert campaign.offer == "video testimonial"
    assert campaign.objective == "drive product demos"


def test_marketing_engine_recommendations_use_prompt_keywords():
    engine = MarketingIntelligenceEngine.from_file()
    results = engine.recommend("B2B SaaS founders looking for pipeline velocity", limit=2)
    assert len(results) == 2
    assert any("B2B SaaS founders" in campaign.audience for campaign in results)


def test_marketing_statistical_engine_enriches_results_with_metrics():
    engine = MarketingIntelligenceEngine.from_file()
    scored = engine.recommend_statistical(
        "Need ROI positive programs for B2B SaaS founders", limit=2
    )
    assert len(scored) == 2
    assert all(score.metrics.expected_roi > 0 for score in scored)
    assert any(score.keyword_coverage["audience"] > 0 for score in scored)


def test_marketing_intelligence_endpoint_returns_structured_payload():
    payload = marketing_intelligence(
        MarketingQuery(prompt="Need to accelerate demos for B2B SaaS founders", limit=2)
    )
    assert payload.query == "Need to accelerate demos for B2B SaaS founders"
    assert len(payload.recommendations) == 2
    audiences = {entry.audience for entry in payload.recommendations}
    assert any("B2B SaaS founders" in audience for audience in audiences)
    assert "accelerate" in payload.insight.lower()


def test_marketing_statistical_endpoint_returns_portfolio_metrics():
    payload = marketing_statistical_intelligence(
        MarketingQuery(prompt="Optimize ROI for AI founders", limit=2)
    )
    assert payload.query == "Optimize ROI for AI founders"
    assert len(payload.recommendations) == 2
    assert payload.portfolio.expected_roi > 0
    assert payload.portfolio.confidence > 0
    assert payload.portfolio.strongest_drivers


def test_marketing_engine_diagnostics_detects_stage_and_focus():
    engine = MarketingIntelligenceEngine.from_file()
    diagnostics = engine.diagnose("Need urgent conversion plays for enterprise SaaS founders")
    assert diagnostics.stage in {"conversion", "consideration"}
    assert diagnostics.urgency != "standard"
    assert diagnostics.focus.startswith("enterprise") or "startup" in diagnostics.focus
    assert diagnostics.recommended_kpis
    assert diagnostics.keyword_summary["tokens"]


def test_marketing_diagnostics_endpoint_returns_structured_analysis():
    payload = marketing_diagnostics(
        MarketingQuery(prompt="Pipeline is stalled for B2B founders, need fast ROI", limit=3)
    )
    assert payload.query == "Pipeline is stalled for B2B founders, need fast ROI"
    assert payload.stage in {"conversion", "consideration"}
    assert "pipeline" in payload.recommended_kpis[0].lower() or payload.recommended_kpis
    assert payload.opportunities
    assert "Performance plateau" in " ".join(payload.risk_signals) or not payload.risk_signals


def test_marketing_engine_expansion_grid_generates_egg_experiments():
    engine = MarketingIntelligenceEngine.from_file()
    scored = engine.recommend_statistical(
        "Need urgent conversion plays for enterprise SaaS founders", limit=2
    )
    experiments = engine.expansion_grid("Need urgent conversion plays for enterprise SaaS founders", scored)
    assert experiments
    first = experiments[0]
    assert first.primary_metric
    assert first.playbook
    assert 0.2 <= first.confidence <= 0.99


def test_marketing_engine_google_ads_fallback_without_credentials(monkeypatch):
    monkeypatch.delenv("GOOGLE_ADS_DEVELOPER_TOKEN", raising=False)
    monkeypatch.delenv("GOOGLE_ADS_CLIENT_ID", raising=False)
    monkeypatch.delenv("GOOGLE_ADS_CLIENT_SECRET", raising=False)
    monkeypatch.delenv("GOOGLE_ADS_REFRESH_TOKEN", raising=False)
    monkeypatch.delenv("GOOGLE_ADS_CUSTOMER_ID", raising=False)

    engine = MarketingIntelligenceEngine.from_file()
    scored = engine.recommend_statistical(
        "Need urgent conversion plays for enterprise SaaS founders", limit=2
    )
    insights = engine.google_ads_enrichment(
        "Need urgent conversion plays for enterprise SaaS founders", scored, limit=2
    )
    assert len(insights) == 2
    assert all(entry.source == "simulated" for entry in insights)
    assert all(entry.clicks > 0 for entry in insights)


def test_marketing_processes_endpoint_combines_rag_and_egg():
    payload = marketing_processes(
        MarketingQuery(prompt="Need urgent conversion plays for enterprise SaaS founders", limit=2)
    )
    assert payload.query == "Need urgent conversion plays for enterprise SaaS founders"
    assert payload.rag.process == "rag"
    assert len(payload.rag.recommendations) == 2
    assert payload.egg
    assert payload.egg[0].playbook
    assert payload.google_ads
    assert payload.google_ads[0].source in {"simulated", "google_ads_api"}