import uuid
from typing import Dict, Any, List
# import langgraph primitives when integrated, for now structure the state and logic
# from langgraph.graph import StateGraph, END

class PartnershipScoutWorkflow:
    """
    Partnership Scout Agent (LangGraph-based state machine).
    Reads market signals and generates detailed partnership scorecards.
    """
    
    def __init__(self, memory_store, decision_engine):
        self.memory_store = memory_store
        self.decision_engine = decision_engine
        
    def _fetch_company_data(self, state: dict) -> dict:
        signal = state.get("signal", {})
        target = signal.get("company_name")
        # In a real environment, call clearbit/linkedin/web-scraper here
        state["enriched_data"] = {"name": target, "industry": "SaaS", "revenue_tier": "mid-market"}
        return state

    def _score_partnership_fit(self, state: dict) -> dict:
        data = state.get("enriched_data", {})
        score = 85 if data.get("industry") == "SaaS" else 40
        state["fit_score"] = score
        state["synergy_potential"] = ["Product reselling", "Data API sharing"]
        return state

    def _generate_decision_memo(self, state: dict) -> dict:
        # Generate the structured Decision Memo
        score = state.get("fit_score", 0)
        target = state.get("enriched_data", {}).get("name", "Unknown")
        
        memo_kwargs = {
            "decision_context": f"Market signal detected interest for {target}",
            "inputs_used": ["LinkedIn API", "Crunchbase Signal"],
            "assumptions": ["Revenue over 10M SAR", "No strict exclusivity clauses"],
            "alternatives_considered": ["Ignore signal", "Direct M&A (ruled out due to size)"],
            "expected_financial_impact": {
                "revenue_upside_sar": 250000.0,
                "cost_downside_sar": 15000.0,
                "capital_at_risk_sar": 0.0
            },
            "risk_register": [
                {"risk": "Brand overlap", "severity": "medium", "mitigation": "Co-branding guidelines"}
            ],
            "required_approvals": ["VP Partnerships"],
            "next_best_action": "Send Partnership introductory email to C-level",
            "rollback_plan": "Cease communications and mark as disqualified in CRM",
            "evidence_links": ["https://crm.dealix.local/signals/1"]
        }
        
        recommendation = f"Initiate Alliance Structuring with {target} (Score: {score})" if score > 70 else "Discard lead."
        
        memo = self.decision_engine.create_memo(
            agent_id="partnership_scout",
            objective="Evaluate partnership market fit",
            recommendation=recommendation,
            confidence=float(score),
            **memo_kwargs
        )
        
        state["final_memo"] = memo.to_json()
        return state

    def execute_flow(self, signal: Dict[str, Any]) -> Dict[str, Any]:
        """
        Simulates the LangGraph execution flow: fetch -> score -> memo.
        """
        state = {"signal": signal}
        state = self._fetch_company_data(state)
        state = self._score_partnership_fit(state)
        state = self._generate_decision_memo(state)
        
        # Save to memory backbone
        memo_id = self.memory_store.store_item(
            domain="partners",
            title=f"Partner Scout: {signal.get('company_name', 'Unknown')}",
            memory_type="Partner Evaluation",
            owner="VP Partnerships",
            confidence=int(state.get("fit_score", 0)),
            summary=state["final_memo"]["recommendation_ar"]
        )
        
        return {"status": "scouted", "memo_id": memo_id, "score": state["fit_score"]}