import json
from types import SimpleNamespace

from app.config.ai_prompts import PROMPT_SPACE_AI_INTERACTION_OPTION_PLANNER
from app.schemas.customer_answer import CustomerEvidencePacket
from app.schemas.rag import RAGAskRequest
from app.services.customer_answer_presentation_service import (
    CustomerAnswerPresentationService,
    _render_state_shape_answer,
    _standardize_canvas_direct_answer,
)


FORBIDDEN_USER_TERMS = (
    "System Snapshot",
    "User Snapshot",
    "Session State",
    "Project database",
    "Knowledge Graph",
    "PROMPT_",
    "AdaptiveRAG",
    "SemanticDecision",
    "backend/",
    "frontend/",
    "tenant_id",
    "prompt_key",
    "trace_id",
)


def make_service() -> CustomerAnswerPresentationService:
    return CustomerAnswerPresentationService(object(), tenant_id=1)


def assert_customer_safe(text: str) -> None:
    assert str(text or "").strip()
    for term in FORBIDDEN_USER_TERMS:
        assert term not in text


def test_space_ai_interaction_option_planner_prompt_formats_with_json_schema() -> None:
    prompt = PROMPT_SPACE_AI_INTERACTION_OPTION_PLANNER.format(
        customer_evidence_packet=json.dumps({"answer_type": "strategic_judgement"}, ensure_ascii=False),
        question="我想做个AI混剪工具",
        answer="先收敛成一个新创意版本。",
        fallback_interaction_request=json.dumps(
            {
                "title": "还要你确认的点",
                "questions": [{"id": "target_user", "options": []}],
            },
            ensure_ascii=False,
        ),
    )

    assert '"title": "还要你确认的点"' in prompt
    assert '"id": "target_user | pain_point | mvp_loop | validation_signal | ..."' in prompt
    assert "我想做个AI混剪工具" in prompt


def test_customer_evidence_packet_accepts_runtime_answer_shapes_and_sanitizes_terms() -> None:
    packet = CustomerEvidencePacket.model_validate(
        {
            "question": "我想做个 AI 采购监督系统",
            "answer_type": "strategic_judgement",
            "facts": [
                {
                    "type": "runtime_evidence_finding",
                    "display_label": "Project database 里的外部证据",
                    "display_summary": "System Snapshot 显示监管压力真实存在",
                    "business_values": {"含义": "监管压力真实存在", "tenant_id": "raw"},
                    "source_display_labels": ["Project database"],
                }
            ],
            "source_summary": ["Project database"],
        }
    ).to_customer_prompt_json()

    dumped = json.dumps(packet, ensure_ascii=False)
    assert packet["answer_type"] == "strategic_judgement"
    assert_customer_safe(dumped)
    assert "监管压力真实存在" in dumped


def test_renderer_uses_turn_state_answer_shape_not_legacy_route_contract() -> None:
    service = make_service()
    packet = service.build_customer_evidence_packet(
        request=RAGAskRequest(
            question="我想做个 AI 采购监督系统，你觉得怎么样",
            surface_context={
                "workbench": {
                    "recentProjects": [
                        {
                            "id": 141,
                            "title": "童析-学龄前儿童行为特质AI评估助手",
                            "status": "active",
                        }
                    ]
                }
            },
        ),
        current_user=SimpleNamespace(id=7, tenant_id=1),
        payload={
            "pmflow_ai_route_plan": {
                "goal_kind": "project_inventory",
                "answer_contract": "surface_fact",
                "source_strategy": "internal_only",
            },
            "pmflow_ai_turn_state": {
                "answer_state": {"answer_shape": "strategic_judgement"},
                "object_state": {"primary_object": {"object_type": "new_idea", "binding_strength": "none"}},
            },
            "evidence_findings": [
                {
                    "fact": "公开政策显示采购监督压力真实存在。",
                    "implication": "机会不在报告生成，而在风险监督闭环。",
                    "source": "https://example.com/policy",
                    "relevance_to_goal": "validates_market_window",
                    "confidence": "high",
                }
            ],
            "sources": [{"source": "external", "display_label": "政策来源", "target_url": "https://example.com/policy"}],
        },
    )

    dumped = json.dumps(packet, ensure_ascii=False)
    assert packet["answer_type"] == "strategic_judgement"
    assert "童析" not in dumped
    assert "机会不在报告生成" in dumped


def test_renderer_demotes_legacy_packet_answer_type_to_runtime_general_shape() -> None:
    service = make_service()
    packet = service.build_customer_evidence_packet(
        request=RAGAskRequest(question="这个项目的 MVP 还差多少"),
        current_user=SimpleNamespace(id=7, tenant_id=1),
        payload={
            "customer_evidence_packet": {"answer_type": "project_mvp_readiness"},
            "evidence_findings": [
                {
                    "fact": "当前项目缺少正式任务和验收标准。",
                    "source": "当前项目执行态证据",
                    "relevance_to_goal": "判断 MVP readiness",
                    "confidence": "high",
                }
            ],
        },
    )
    canvas = service.build_answer_canvas(packet=packet, answer="当前项目缺少正式任务和验收标准。")

    assert packet["answer_type"] == "general_answer"
    assert canvas["headline"] == "回答"


def test_process_packet_does_not_fabricate_renderer_steps() -> None:
    service = make_service()
    process = service.build_answer_process_packet(
        packet={
            "answer_type": "strategic_judgement",
            "facts": [
                {
                    "type": "runtime_evidence_finding",
                    "display_label": "外部公开来源",
                    "display_summary": "机会不在浅工具，而在闭环。",
                    "confidence": "high",
                }
            ],
        },
        verification={"ok": True},
    )

    assert process["answer_type"] == "strategic_judgement"
    assert process["steps"] == []


def test_process_packet_preserves_explicit_runtime_steps_only() -> None:
    service = make_service()
    process = service.build_answer_process_packet(
        packet={
            "answer_type": "execution_guidance",
            "process_steps": [
                {
                    "key": "inspect_project_status",
                    "label": "读取项目执行态证据",
                    "status": "completed",
                    "duration_ms": 120,
                }
            ],
        },
        verification={"ok": True},
    )

    assert process["steps"][0]["key"] == "inspect_project_status"
    assert process["steps"][0]["label"] == "读取项目执行态证据"


def test_customer_evidence_packet_does_not_invent_default_source_summary() -> None:
    service = make_service()
    packet = service.build_customer_evidence_packet(
        request=RAGAskRequest(question="现在有什么项目"),
        current_user=SimpleNamespace(id=7, tenant_id=1),
        payload={
            "grounding_status": "partially_grounded",
            "evidence_findings": [
                {
                    "fact": "当前可见项目 1 个",
                    "implication": "可以回答项目数量",
                    "source": "",
                }
            ],
        },
    )

    assert packet["source_summary"] == []


def test_answer_canvas_headline_and_evidence_are_shape_based() -> None:
    service = make_service()
    answer = "判断：值得继续看，但要做成监督闭环，不是报告生成。"
    canvas = service.build_answer_canvas(
        packet={
            "answer_type": "strategic_judgement",
            "facts": [
                {
                    "type": "runtime_evidence_finding",
                    "display_label": "外部公开来源",
                    "display_summary": "监管和内控压力是真实存在的。",
                    "confidence": "high",
                    "source_display_labels": ["外部公开来源"],
                }
            ],
            "limitations": ["还缺目标客户预算证据"],
        },
        answer=answer,
    )

    assert canvas["headline"] == "方向判断"
    assert canvas["direct_answer"] == "值得继续看，但要做成监督闭环，不是报告生成。"
    assert canvas["evidence_blocks"][0]["summary"] == "监管和内控压力是真实存在的。"
    assert canvas["limitations"] == ["还缺目标客户预算证据"]


def test_packet_az_canvas_direct_answer_does_not_generate_fixed_evidence_sections() -> None:
    answer = (
        "这个方向可以继续看，但要先验证采购团队是否愿意为监督闭环付费。"
        "已有监管压力能支持问题存在。"
        "还缺预算和真实流程证据。"
        "第一版先做风险提示和留痕。"
    )

    direct = _standardize_canvas_direct_answer(answer)

    assert "**结论**" not in direct
    assert "**判断依据**" not in direct
    assert "这个方向可以继续看" in direct
    assert "第一版先做风险提示" in direct


def test_packet_az_state_shape_answer_does_not_prefix_by_answer_type() -> None:
    answer = _render_state_shape_answer(
        {
            "answer_type": "execution_guidance",
            "facts": [
                {"display_summary": "先整理加密评审材料"},
                {"display_summary": "直接发布还缺测试质量证据"},
            ],
        }
    )

    assert answer == "先整理加密评审材料；直接发布还缺测试质量证据。"
    assert not answer.startswith("可执行建议：")


def test_packet_az_canvas_sanitizer_strips_generated_section_headings() -> None:
    direct = _standardize_canvas_direct_answer(
        "**结论**\n可以继续看。\n\n**判断依据**\n- 已有明确需求。\n- 还缺预算证据。"
    )

    assert "**结论**" not in direct
    assert "**判断依据**" not in direct
    assert "可以继续看" in direct
    assert "已有明确需求" in direct