"""
SocialAgent - AI agent behavior for ad reaction simulation.

No longer a Ray actor — agents are plain Python objects.
LLM calls go through a shared QwenLLM actor pool (Ray-backed, Ollama API).
This follows AgentSociety's pattern where agents are regular objects
and only LLM calls are distributed via Ray actors.
"""
import random
import logging
import json
import re
from typing import List, Dict, Any, Optional

logger = logging.getLogger(__name__)


class SocialAgent:
    """
    Simulated person reacting to advertisements.

    Implements:
    - Profile & Memory
    - Emotional state
    - Social interactions
    - Mind-Behavior Coupling for decision making

    NOTE: No @ray.remote — this is a plain object. LLM calls are dispatched
    through the GeminiLLM actor pool passed in via `llm_pool`.
    """

    def __init__(
        self,
        agent_id: str,
        profile: Dict[str, Any],
        experiment_id: str,
        llm_pool=None,
        friends: List[str] = None,
        memory_store=None,
    ):
        """
        Initialize agent with profile.

        Args:
            agent_id: Unique identifier
            profile: Demographics and values dict
            experiment_id: Experiment this agent belongs to
            llm_pool: QwenLLM actor pool for LLM calls
            friends: List of friend agent IDs
            memory_store: Optional AgentMemoryStore instance (shared)
        """
        self.agent_id = agent_id
        self.profile = profile
        self.experiment_id = experiment_id
        self.llm_pool = llm_pool
        self.friends = friends or []

        # Internal state
        self.emotion = "neutral"
        self.emotion_intensity = 0.0
        self.opinion_on_ad = None
        self.has_seen_ad = False
        self.reasoning = ""
        self.inbox: List[Dict] = []
        self.opinion_history: List[Dict] = []
        self.day: int = 0

        # Event log for this agent
        self.event_log: List[Dict[str, Any]] = []

        # Optional memory (ChromaDB) — may be None
        self.memory = None
        if memory_store:
            try:
                memory_store.create_agent_profile(agent_id, profile)
                self.memory = memory_store
            except Exception as e:
                logger.debug(f"Agent {agent_id}: Memory init skipped: {e}")

        logger.debug(
            f"Agent {agent_id} initialized: "
            f"{profile.get('age')}yo {profile.get('gender')} "
            f"from {profile.get('location')}"
        )

    async def perceive_ad(self, ad_content: str) -> Dict[str, Any]:
        """
        Main decision-making: React to advertisement content (async).

        Implements Mind-Behavior Coupling:
        1. Retrieve relevant memories (RAG)
        2. Generate emotional response via LLM
        3. Form opinion
        4. Take action
        """
        self.has_seen_ad = True

        # Step 1: Query memory for relevant context
        memory_context = []
        if self.memory:
            try:
                logger.info(f"[{self.agent_id}] Querying memory for context...")
                memory_context = self.memory.query_relevant_context(
                    self.agent_id, ad_content, n_results=3
                )
                logger.info(f"[{self.agent_id}] Memory returned {len(memory_context)} items")
            except Exception as me:
                logger.warning(f"[{self.agent_id}] Memory query failed: {me}")

        # Step 2: Build prompt with personality
        prompt = self._build_reaction_prompt(ad_content, memory_context)

        # Step 3: Get LLM response via actor pool
        try:
            if self.llm_pool:
                import time as _time
                _t0 = _time.time()
                logger.info(f"[{self.agent_id}] Sending LLM request...")
                response = await self.llm_pool.atext_request(
                    prompt=prompt, max_tokens=500
                )
                _elapsed = _time.time() - _t0
                logger.info(f"[{self.agent_id}] LLM responded in {_elapsed:.1f}s ({len(response)} chars)")
            else:
                logger.warning(f"[{self.agent_id}] No LLM pool available!")
                response = ""

            parsed = self._parse_llm_response(response)
            logger.info(f"[{self.agent_id}] Result: {parsed['emotion']} / {parsed['opinion']} (intensity: {parsed['intensity']})")

            self.opinion_on_ad = parsed["opinion"]
            self.emotion = parsed["emotion"]
            self.emotion_intensity = parsed["intensity"]
            self.reasoning = parsed["reasoning"]

            # Step 4: Take action based on opinion
            if parsed["opinion"] == "NEGATIVE":
                self._log_event("BOYCOTT", parsed["reasoning"])
            elif parsed["opinion"] == "POSITIVE":
                if random.random() < self._get_sharing_probability():
                    self._log_event("ENDORSEMENT", parsed["reasoning"])
                else:
                    self._log_event("ENDORSEMENT", parsed["reasoning"])
            else:
                self._log_event("IGNORE", "No strong reaction")

            # Step 5: Update memory
            if self.memory:
                try:
                    self.memory.add_experience(
                        self.agent_id,
                        f"Reacted {parsed['opinion']} to ad: {parsed['reasoning'][:100]}",
                        experience_type=parsed["opinion"].lower(),
                    )
                except Exception:
                    pass

        except Exception as e:
            logger.error(f"Agent {self.agent_id} failed to process ad: {e}")
            self.opinion_on_ad = "NEUTRAL"
            self._log_event("ERROR", str(e))

        # Capture initial opinion on Day 0
        self.opinion_history.append({"day": 0, "opinion": self.opinion_on_ad})

        return self.get_state()

    def _build_reaction_prompt(
        self, ad_content: str, memory_context: List[str]
    ) -> str:
        """Build LLM prompt with agent personality"""
        values = self.profile.get("values", [])
        values_str = ", ".join(values) if values else "Not specified"

        memory_str = (
            "\n".join(memory_context) if memory_context else "No past experiences"
        )

        bio = self.profile.get('bio', '')
        name = self.profile.get('name', 'a person')
        personality_str = ", ".join(self.profile.get('personality_traits', [])) if self.profile.get('personality_traits') else 'Not specified'

        return f"""You are roleplaying as {name}, a real person with this profile:
- Age: {self.profile.get('age', 'Unknown')}, Gender: {self.profile.get('gender', 'Unknown')}
- Location: {self.profile.get('location', 'Unknown')}, Sri Lanka
- Occupation: {self.profile.get('occupation', 'Not specified')}
- Education: {self.profile.get('education', 'Not specified')}
- Income Level: {self.profile.get('income_level', 'Not specified')}
- Religion: {self.profile.get('religion', 'Not specified')}
- Ethnicity: {self.profile.get('ethnicity', 'Not specified')}  
- Political Leaning: {self.profile.get('political_leaning', 'Not specified')}
- Social Media Usage: {self.profile.get('social_media_usage', 'Not specified')}
- Core Values: {values_str}
- Personality: {personality_str}
- Background: {bio if bio else 'No specific background provided'}

Your past experiences relevant to this ad:
{memory_str}

You just saw this advertisement:
{ad_content}

React authentically as {name}. Consider your background, 
values, religion, and social context when forming your opinion.
Strong reactions should reflect genuine conflicts or alignments 
with your identity and values.

Analyze your reaction as this person:
1. How does this ad make you FEEL? (Choose one: HAPPY, ANGRY, SAD, NEUTRAL)
2. What is your OPINION? (Choose one: POSITIVE, NEUTRAL, NEGATIVE)
3. WHY do you feel this way? (2-3 sentences from YOUR perspective as this person)

You MUST respond in this JSON format:
{{"emotion": "ANGRY", "opinion": "NEGATIVE", "reasoning": "This ad shows something I disagree with because..."}}"""

    def _parse_llm_response(self, response: str) -> Dict[str, Any]:
        """Extract structured data from LLM response"""
        default = {
            "emotion": "neutral",
            "opinion": "NEUTRAL",
            "reasoning": "",
            "intensity": 0.3,
        }

        if not response:
            return default

        try:
            # Find JSON in response
            json_match = re.search(r"\{[^{}]*\}", response, re.DOTALL)
            if json_match:
                data = json.loads(json_match.group())

                emotion = data.get("emotion", "NEUTRAL").upper()
                opinion = data.get("opinion", "NEUTRAL").upper()

                # Validate values
                if opinion not in ["POSITIVE", "NEUTRAL", "NEGATIVE"]:
                    opinion = "NEUTRAL"

                # Calculate intensity
                intensity = 0.8 if opinion in ["POSITIVE", "NEGATIVE"] else 0.3

                return {
                    "emotion": emotion.lower(),
                    "opinion": opinion,
                    "reasoning": data.get("reasoning", ""),
                    "intensity": intensity,
                }
        except Exception as e:
            logger.warning(f"Failed to parse LLM response: {e}")

        return default

    def receive_peer_message(self, from_agent_id: str, opinion: str, message: str):
        """Receive a message from a peer and store it in inbox."""
        self.inbox.append({
            "from": from_agent_id,
            "opinion": opinion,
            "message": message
        })

    async def generate_social_message(self) -> str:
        """Generate a custom message to share with a friend."""
        if self.opinion_on_ad is None:
            return ""

        fallback = f"I thought the ad was {self.opinion_on_ad.lower()}, what did you think?"
        
        prompt = f"""You are Roleplaying as this person:
- Age: {self.profile.get('age', 'Unknown')}
- Gender: {self.profile.get('gender', 'Unknown')}
- Location: {self.profile.get('location', 'Unknown')}
- Core values: {", ".join(self.profile.get('values', []))}

Your current opinion on the ad is {self.opinion_on_ad}.
Write 1-2 casual sentences that you would send to a friend about the ad, in character. Do not include quotes."""

        try:
            if self.llm_pool:
                response = await self.llm_pool.atext_request(prompt=prompt, max_tokens=100)
                if response:
                    return response.strip(' "')
        except Exception:
            pass
            
        return fallback

    async def social_deliberation(self, ad_content: str) -> Dict[str, Any]:
        """Review inbox messages from friends and reconsider opinion."""
        if not self.inbox:
            return self.get_state()

        friends_summary = "\n".join(
            f"- A friend thinks {msg['opinion']}: {msg['message']}" 
            for msg in self.inbox
        )

        prompt = f"""You are Roleplaying as this person:
- Age: {self.profile.get('age', 'Unknown')}
- Gender: {self.profile.get('gender', 'Unknown')}
- Location: {self.profile.get('location', 'Unknown')}
- Core values: {", ".join(self.profile.get('values', []))}

Your current opinion on the ad is {self.opinion_on_ad}.

Here is a summary of what your friends said:
{friends_summary}

Given your values and what your friends think, do you change your opinion?
Respond in JSON format:
{{"new_opinion": "POSITIVE/NEUTRAL/NEGATIVE", "changed": true/false, "reasoning": "..."}}"""

        try:
            if self.llm_pool:
                response = await self.llm_pool.atext_request(prompt=prompt, max_tokens=200)
                json_match = re.search(r"\{[^{}]*\}", response, re.DOTALL)
                
                if json_match:
                    data = json.loads(json_match.group())
                    new_opinion = data.get("new_opinion", self.opinion_on_ad).upper()
                    
                    if new_opinion not in ["POSITIVE", "NEUTRAL", "NEGATIVE"]:
                        new_opinion = self.opinion_on_ad
                        
                    if new_opinion != self.opinion_on_ad:
                        self._log_event("OPINION_CHANGE", data.get("reasoning", "Influenced by friends"))
                        self.opinion_on_ad = new_opinion

        except Exception as e:
            logger.warning(f"[{self.agent_id}] Failed social deliberation: {e}")

        self.opinion_history.append({"day": self.day, "opinion": self.opinion_on_ad})
        self.inbox.clear()
        self.day += 1

        return self.get_state()


    def _get_sharing_probability(self) -> float:
        """Calculate likelihood of sharing based on personality"""
        base_prob = 0.3
        age = self.profile.get("age", 35)
        if age < 25:
            base_prob += 0.2
        elif age < 35:
            base_prob += 0.1
        return min(base_prob, 0.6)

    def _log_event(self, event_type: str, details: str):
        """Log an event locally"""
        self.event_log.append(
            {
                "agent_id": self.agent_id,
                "event_type": event_type,
                "details": details,
                "opinion": self.opinion_on_ad,
                "emotion": self.emotion,
            }
        )

    def get_state(self) -> Dict[str, Any]:
        """Get current agent state"""
        return {
            "agent_id": self.agent_id,
            "opinion": self.opinion_on_ad,
            "emotion": self.emotion,
            "emotion_intensity": self.emotion_intensity,
            "reasoning": self.reasoning,
            "has_seen_ad": self.has_seen_ad,
            "profile": self.profile,
            "opinion_history": self.opinion_history,
            "day": self.day,
        }

    def get_event_log(self) -> List[Dict[str, Any]]:
        """Get all logged events"""
        return self.event_log