"""
mas_generator.py — Multi-Agent System Generator.

Takes a use-case description and outputs a complete generated system:
  - Agent specs with roles
  - Flow (workflow graph)
  - Tools needed
  - Memory budget
  - Eval suite
  - Routing policy

Usage:
    from purpose_agent.mas_generator import generate
    
    mas = generate("Monitor GitHub repos for CVEs and alert the team")
    # → GeneratedMAS with agents, flow, tools, evals, routing
    
    # Run it
    team = mas.to_team(model=backend)
    result = team.run("Check for new CVEs today")
"""
from __future__ import annotations

import logging
from dataclasses import dataclass, field
from typing import Any

from purpose_agent.routing import RoutingPolicy, TaskComplexity
from purpose_agent.memory_homeostasis import MemoryBudget

logger = logging.getLogger(__name__)


@dataclass
class GeneratedAgent:
    """Spec for a generated agent."""
    name: str
    role: str
    expertise: list[str] = field(default_factory=list)
    tools_needed: list[str] = field(default_factory=list)


@dataclass
class GeneratedFlow:
    """Spec for a generated workflow."""
    nodes: list[str] = field(default_factory=list)         # Node names in order
    edges: list[tuple[str, str]] = field(default_factory=list)  # (from, to)
    conditional: dict[str, dict[str, str]] = field(default_factory=dict)  # node → {condition: target}


@dataclass
class GeneratedEval:
    """A generated evaluation case."""
    id: str
    purpose: str
    expected_behavior: str
    category: str = "general"


@dataclass
class GeneratedMAS:
    """Complete generated multi-agent system."""
    purpose: str
    agents: list[GeneratedAgent] = field(default_factory=list)
    flow: GeneratedFlow = field(default_factory=GeneratedFlow)
    tools: list[str] = field(default_factory=list)
    memory_budget: MemoryBudget = field(default_factory=MemoryBudget)
    eval_suite: list[GeneratedEval] = field(default_factory=list)
    routing_policy: RoutingPolicy = field(default_factory=RoutingPolicy)
    metadata: dict[str, Any] = field(default_factory=dict)

    def to_team(self, model=None):
        """Convert to a Purpose Agent Team for execution."""
        from purpose_agent.easy import Team
        agent_specs = [{"name": a.name, "role": a.role} for a in self.agents]
        return Team(purpose=self.purpose, agents=agent_specs, model=model)


# ═══════════════════════════════════════════════════════════════
# Templates — deterministic generation (no LLM needed)
# ═══════════════════════════════════════════════════════════════

_TEMPLATES = {
    "code": {
        "keywords": ["code", "program", "develop", "build", "software", "python", "debug", "function", "api", "script"],
        "agents": [
            GeneratedAgent("architect", "Design solution architecture and break into subtasks", ["design", "planning"]),
            GeneratedAgent("coder", "Write clean, tested code following best practices", ["coding", "python"], ["python_exec"]),
            GeneratedAgent("tester", "Review code for bugs, edge cases, and improvements", ["testing", "review"]),
        ],
        "flow_nodes": ["architect", "coder", "tester"],
        "flow_type": "sequential_with_review",
        "tools": ["python_exec", "read_file", "write_file"],
    },
    "security": {
        "keywords": ["security", "cve", "cves", "vulnerability", "audit", "penetration", "threat", "monitor", "alert"],
        "agents": [
            GeneratedAgent("scanner", "Scan and identify potential security issues", ["scanning", "detection"]),
            GeneratedAgent("analyst", "Analyze severity and impact of findings", ["analysis", "risk"]),
            GeneratedAgent("reporter", "Create clear security reports with recommendations", ["reporting"]),
            GeneratedAgent("security_critic", "Verify findings and check for false positives", ["verification"]),
        ],
        "flow_nodes": ["scanner", "analyst", "security_critic", "reporter"],
        "flow_type": "sequential_with_critic",
        "tools": ["read_file", "calculator"],
    },
    "research": {
        "keywords": ["research", "find", "search", "discover", "papers", "study", "investigate", "analyze"],
        "agents": [
            GeneratedAgent("researcher", "Find and gather relevant information", ["search", "gathering"]),
            GeneratedAgent("verifier", "Cross-check facts and verify sources", ["verification", "fact-check"]),
            GeneratedAgent("synthesizer", "Combine findings into coherent summary", ["synthesis", "writing"]),
        ],
        "flow_nodes": ["researcher", "verifier", "synthesizer"],
        "flow_type": "sequential",
        "tools": ["calculator"],
    },
    "data": {
        "keywords": ["data", "csv", "excel", "database", "analytics", "chart", "statistics", "report"],
        "agents": [
            GeneratedAgent("loader", "Load and validate data from sources", ["data_loading"], ["read_file"]),
            GeneratedAgent("analyst", "Analyze data, compute statistics, find patterns", ["analysis"], ["python_exec", "calculator"]),
            GeneratedAgent("validator", "Validate results and check for errors", ["validation"]),
            GeneratedAgent("reporter", "Present findings in clear format", ["reporting"]),
        ],
        "flow_nodes": ["loader", "analyst", "validator", "reporter"],
        "flow_type": "sequential",
        "tools": ["python_exec", "read_file", "calculator"],
    },
    "operations": {
        "keywords": ["deploy", "monitor", "operate", "maintain", "alert", "incident", "pipeline"],
        "agents": [
            GeneratedAgent("planner", "Plan operations and identify risks", ["planning"]),
            GeneratedAgent("executor", "Execute planned operations carefully", ["execution"], ["python_exec"]),
            GeneratedAgent("auditor", "Verify operations completed correctly", ["auditing"]),
        ],
        "flow_nodes": ["planner", "executor", "auditor"],
        "flow_type": "sequential_with_approval",
        "tools": ["python_exec", "read_file"],
    },
}


def _match_template(use_case: str) -> dict:
    """Match use case to best template by keyword overlap."""
    words = set(use_case.lower().split())
    best_template = None
    best_score = 0

    for name, template in _TEMPLATES.items():
        score = len(words & set(template["keywords"]))
        if score > best_score:
            best_score = score
            best_template = template

    # Default to research if no match
    return best_template or _TEMPLATES["research"]


def _generate_flow(template: dict) -> GeneratedFlow:
    """Generate flow graph from template."""
    nodes = template["flow_nodes"]
    flow = GeneratedFlow(nodes=list(nodes))

    # Sequential edges
    for i in range(len(nodes) - 1):
        flow.edges.append((nodes[i], nodes[i + 1]))

    # Add review loop for certain types
    if template.get("flow_type") == "sequential_with_review":
        # Last node can loop back to second node on failure
        flow.conditional[nodes[-1]] = {"pass": "__END__", "fail": nodes[1]}
    elif template.get("flow_type") == "sequential_with_critic":
        # Critic can reject and loop back
        critic = [n for n in nodes if "critic" in n]
        if critic:
            flow.conditional[critic[0]] = {"approve": nodes[-1], "reject": nodes[0]}

    return flow


def _generate_evals(use_case: str, agents: list[GeneratedAgent]) -> list[GeneratedEval]:
    """Generate evaluation cases for the system."""
    evals = [
        GeneratedEval(
            id="eval_runs",
            purpose=f"System can process: {use_case}",
            expected_behavior="Completes without error",
            category="basic",
        ),
        GeneratedEval(
            id="eval_agents_contribute",
            purpose="Each agent contributes to the output",
            expected_behavior="All agents produce non-empty output",
            category="coverage",
        ),
    ]
    # Add per-agent evals
    for agent in agents[:3]:
        evals.append(GeneratedEval(
            id=f"eval_{agent.name}",
            purpose=f"{agent.name} performs its role: {agent.role}",
            expected_behavior=f"{agent.name} produces relevant output for its expertise",
            category="agent_role",
        ))
    return evals


def generate(use_case: str, constraints: dict[str, Any] | None = None) -> GeneratedMAS:
    """
    Generate a complete multi-agent system from a use-case description.
    
    Uses deterministic templates (no LLM required).
    
    Args:
        use_case: Plain English description of what the system should do
        constraints: Optional overrides (max_agents, prefer_local, etc.)
        
    Returns:
        GeneratedMAS with agents, flow, tools, evals, and routing policy
    """
    constraints = constraints or {}
    template = _match_template(use_case)

    agents = template["agents"]
    flow = _generate_flow(template)
    tools = template["tools"]
    evals = _generate_evals(use_case, agents)

    # Routing policy based on template complexity
    n_agents = len(agents)
    policy = RoutingPolicy(
        prefer_local=constraints.get("prefer_local", True),
        max_cost_per_task_usd=constraints.get("max_cost", 0.10),
    )

    # Memory budget scaled to system size
    budget = MemoryBudget(
        max_active_cards=min(512, n_agents * 128),
        max_injected_tokens=min(500, n_agents * 150),
    )

    mas = GeneratedMAS(
        purpose=use_case,
        agents=agents,
        flow=flow,
        tools=tools,
        memory_budget=budget,
        eval_suite=evals,
        routing_policy=policy,
        metadata={"template": next((k for k, v in _TEMPLATES.items() if v is template), "research")},
    )

    logger.info(f"MAS generated: {len(agents)} agents, {len(flow.nodes)} nodes, {len(evals)} evals")
    return mas