"""
Recovery/Enrichment Agent
Implements prioritized enrichment source config, rate limiting, retry/backoff, queue/handoff, and full action/decision logging for leads enrichment and recovery workflows.
Integrates lead_scoring_agent to score leads at the start of processing and inject scoring fields into the asset flow for analytics and UI visibility.
"""
import time
import logging
from enum import Enum
from typing import List, Dict, Any, Optional
from lead_scoring_agent import score_lead

# Agent config
class EnrichmentSource(Enum):
    API = "api"
    BROWSER_AGENT = "browser_agent"
    HUMAN_REVIEW = "human_review"

default_agent_config = {
    "priority_sources": [EnrichmentSource.API, EnrichmentSource.BROWSER_AGENT, EnrichmentSource.HUMAN_REVIEW],
    "rate_limit_per_minute": 10,
    "max_retries": 3,
    "retry_backoff_seconds": 5,
}

# Audit and log actions/decisions/edge cases
def log_event(event_type: str, info: dict):
    logging.info({"event_type": event_type, **info})

# Simulate lead batch handoff and enrichment
class LeadBatchHandler:
    def __init__(self, config=default_agent_config):
        self.config = config
        self.queue = []
        self.history = []
        self.rate_limit_counter = 0
        self.last_reset = time.time()
    
    def reset_rate_limit(self):
        now = time.time()
        if now - self.last_reset > 60:
            self.rate_limit_counter = 0
            self.last_reset = now
            log_event("rate_limit_reset", {})
    
    def handle_lead_batch(self, leads: List[Dict[str, Any]]):
        results = []
        for lead in leads:
            result = self._process_lead(lead)
            results.append(result)
        return results
    
    def _process_lead(self, lead: Dict[str, Any]) -> Dict[str, Any]:
        # Score the lead at the start of processing and inject scoring fields
        scoring_data = score_lead(lead)
        lead.update({
            "lead_score": scoring_data.get("score", 0),
            "score_tier": scoring_data.get("tier", "unknown"),
            "score_factors": scoring_data.get("factors", {}),
            "scored_at": time.time()
        })
        log_event("lead_scored", {"lead": lead, "scoring": scoring_data})
        
        self.reset_rate_limit()
        for source in self.config["priority_sources"]:
            for attempt in range(self.config["max_retries"]):
                if self.rate_limit_counter >= self.config["rate_limit_per_minute"]:
                    wait_time = 60 - (time.time() - self.last_reset)
                    if wait_time > 0:
                        log_event("rate_limit_wait", {"wait": wait_time})
                        time.sleep(wait_time)
                    self.reset_rate_limit()
                log_event("enrich_attempt", {"source": source.value, "lead": lead, "attempt": attempt+1})
                self.rate_limit_counter += 1
                success, data = self._enrich_from_source(source, lead)
                if success:
                    log_event("enrich_success", {"source": source.value, "lead": lead})
                    # Preserve scoring fields in enriched result
                    return {"lead": lead, "enriched": data, "lead_score": lead["lead_score"], "score_tier": lead["score_tier"]}
                else:
                    log_event("enrich_failure", {"source": source.value, "lead": lead, "attempt": attempt+1})
                    time.sleep(self.config["retry_backoff_seconds"])
        self._escalate_to_human(lead)
        return {"lead": lead, "enriched": None, "escalated": True, "lead_score": lead["lead_score"], "score_tier": lead["score_tier"]}
    
    def _enrich_from_source(self, source: EnrichmentSource, lead: Dict[str, Any]) -> (bool, Optional[dict]):
        # Simulation logic; replace with real API/browser/human integration
        if source == EnrichmentSource.API:
            return False, None  # simulate API failure
        if source == EnrichmentSource.BROWSER_AGENT:
            return True, {"company": "Simulated Corp", "status": "Browser enrichment success"}
        if source == EnrichmentSource.HUMAN_REVIEW:
            return True, {"company": "Simulated Corp", "status": "Human reviewed"}
        return False, None
    
    def _escalate_to_human(self, lead: Dict[str, Any]):
        self.queue.append(lead)
        log_event("escalate", {"lead": lead})
    
    def get_audit_log(self):
        # Return all logged history (simulate)
        pass

# Config interface sample
def get_sample_config() -> dict:
    return default_agent_config

# Usage sample
def simulate_lead_handoff():
    handler = LeadBatchHandler()
    leads = [{"first_name": "Mila", "company": "Sample Inc"}, {"first_name": "Rob", "company": "Unknown Ltd"}]
    results = handler.handle_lead_batch(leads)
    print(results)
    # add more logic for audit/fallback/escalation as needed

if __name__ == "__main__":
    simulate_lead_handoff()