"""
LinkedIn Comment Response Generator

This module provides AI-powered generation of professional and engaging responses
to comments on LinkedIn posts.
"""

import json
from typing import Dict, List, Optional
from loguru import logger

from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search

class LinkedInCommentResponseGenerator:
    """
    AI-powered generator for professional and engaging LinkedIn comment responses.
    """
    
    def __init__(self):
        """Initialize the LinkedIn Comment Response Generator."""
        self.response_tones = [
            "professional",
            "friendly",
            "expert",
            "supportive",
            "diplomatic",
            "appreciative"
        ]
        
        self.comment_types = [
            "question",
            "agreement",
            "disagreement",
            "appreciation",
            "criticism",
            "suggestion",
            "experience_sharing"
        ]
    
    async def analyze_comment(self, comment: str) -> Dict:
        """
        Analyze the comment to determine its type, tone, and key points.
        
        Args:
            comment: The comment text to analyze
            
        Returns:
            Dict containing comment analysis
        """
        prompt = f"""
        As a LinkedIn engagement expert, analyze this comment:
        
        Comment: {comment}
        
        Provide a detailed analysis including:
        - Comment type (question/agreement/disagreement/etc.)
        - Emotional tone
        - Key points or questions raised
        - Intent and context
        - Engagement potential
        
        Return a JSON with:
        - comment_type: The type of comment
        - tone: Emotional tone
        - key_points: List of main points
        - questions: Any questions raised
        - intent: Perceived intent
        - engagement_potential: High/Medium/Low
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response)
    
    async def generate_response(self,
        comment: str,
        post_context: str,
        brand_voice: str,
        engagement_goal: str
    ) -> Dict:
        """
        Generate an appropriate response to the comment.
        
        Args:
            comment: The comment to respond to
            post_context: The context of the original post
            brand_voice: Desired brand voice/tone
            engagement_goal: Goal for the response
            
        Returns:
            Dict containing response and strategy
        """
        # First analyze the comment
        analysis = await self.analyze_comment(comment)
        
        prompt = f"""
        As a LinkedIn engagement expert, generate a response to this comment:
        
        Comment: {comment}
        Post Context: {post_context}
        Brand Voice: {brand_voice}
        Engagement Goal: {engagement_goal}
        
        Comment Analysis: {json.dumps(analysis)}
        
        Generate a response that:
        - Maintains professional tone
        - Addresses key points/questions
        - Aligns with brand voice
        - Encourages further engagement
        - Builds community
        - Adds value
        
        Return a JSON with:
        - response: The generated response
        - tone_used: Tone of the response
        - key_points_addressed: Points addressed
        - engagement_hooks: Elements to encourage interaction
        - value_adds: Additional value provided
        - follow_up_suggestions: Potential follow-up points
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response)
    
    async def handle_disagreement(self,
        comment: str,
        post_context: str,
        brand_voice: str
    ) -> Dict:
        """
        Generate a professional response to a disagreement.
        
        Args:
            comment: The disagreeing comment
            post_context: Original post context
            brand_voice: Desired brand voice
            
        Returns:
            Dict containing diplomatic response
        """
        prompt = f"""
        As a LinkedIn communication expert, craft a diplomatic response to this disagreement:
        
        Comment: {comment}
        Post Context: {post_context}
        Brand Voice: {brand_voice}
        
        Generate a response that:
        - Maintains professionalism
        - Acknowledges the perspective
        - Provides supporting evidence
        - Finds common ground
        - Keeps discussion constructive
        - Invites further dialogue
        
        Return a JSON with:
        - response: The diplomatic response
        - acknowledgment: How the perspective was acknowledged
        - evidence: Supporting points provided
        - common_ground: Areas of agreement
        - dialogue_hooks: Elements to continue discussion
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response)
    
    async def generate_value_add_response(self,
        comment: str,
        industry: str,
        expertise_areas: List[str]
    ) -> Dict:
        """
        Generate a response that adds significant value.
        
        Args:
            comment: The comment to respond to
            industry: Relevant industry
            expertise_areas: Areas of expertise
            
        Returns:
            Dict containing value-adding response
        """
        # Research relevant insights
        research = await do_tavily_ai_search(
            f"latest insights trends {industry} {' '.join(expertise_areas)}"
        )
        
        prompt = f"""
        As a LinkedIn thought leader, generate a value-adding response:
        
        Comment: {comment}
        Industry: {industry}
        Expertise Areas: {expertise_areas}
        Research Insights: {json.dumps(research)}
        
        Create a response that:
        - Shares relevant insights
        - Provides actionable advice
        - References credible sources
        - Demonstrates expertise
        - Encourages implementation
        - Invites questions
        
        Return a JSON with:
        - response: The value-adding response
        - insights_shared: Key insights provided
        - action_items: Actionable takeaways
        - sources: Referenced sources
        - expertise_demonstrated: How expertise was shown
        - engagement_hooks: Questions or prompts
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response)
    
    async def suggest_resources(self,
        comment: str,
        topic: str,
        expertise_level: str
    ) -> Dict:
        """
        Suggest relevant resources in response to a comment.
        
        Args:
            comment: The comment requesting/needing resources
            topic: The topic of discussion
            expertise_level: User's expertise level
            
        Returns:
            Dict containing resource suggestions
        """
        # Research relevant resources
        resources = await metaphor_search_articles(
            f"best resources tutorials guides {topic} {expertise_level}"
        )
        
        prompt = f"""
        As a LinkedIn learning facilitator, suggest helpful resources:
        
        Comment: {comment}
        Topic: {topic}
        Expertise Level: {expertise_level}
        Found Resources: {json.dumps(resources)}
        
        Provide suggestions that:
        - Match expertise level
        - Cover key aspects
        - Include various formats
        - Are readily accessible
        - Support learning goals
        - Encourage application
        
        Return a JSON with:
        - response: Resource suggestion response
        - recommended_resources: List of resources
        - learning_path: Suggested learning sequence
        - application_tips: How to apply resources
        - follow_up_support: Additional support offered
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response)
    
    async def generate_follow_up_questions(self,
        comment: str,
        discussion_context: str
    ) -> Dict:
        """
        Generate engaging follow-up questions to continue the discussion.
        
        Args:
            comment: The comment to generate questions for
            discussion_context: Context of the discussion
            
        Returns:
            Dict containing follow-up questions
        """
        prompt = f"""
        As a LinkedIn engagement expert, generate follow-up questions:
        
        Comment: {comment}
        Discussion Context: {discussion_context}
        
        Generate questions that:
        - Deepen the discussion
        - Explore different angles
        - Draw out experiences
        - Encourage reflection
        - Maintain professionalism
        - Drive engagement
        
        Return a JSON with:
        - primary_question: Main follow-up question
        - secondary_questions: Additional questions
        - discussion_angles: New perspectives to explore
        - engagement_prompts: Ways to encourage participation
        - value_exploration: Areas to uncover value
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response)
    
    async def optimize_response_tone(self,
        response: str,
        target_tone: str,
        audience: str
    ) -> Dict:
        """
        Optimize the tone of a response for the target audience.
        
        Args:
            response: The response to optimize
            target_tone: Desired tone
            audience: Target audience
            
        Returns:
            Dict containing tone-optimized response
        """
        prompt = f"""
        As a LinkedIn communication expert, optimize this response's tone:
        
        Response: {response}
        Target Tone: {target_tone}
        Audience: {audience}
        
        Optimize the response to:
        - Match desired tone
        - Resonate with audience
        - Maintain professionalism
        - Enhance engagement
        - Build rapport
        - Reflect brand voice
        
        Return a JSON with:
        - optimized_response: Tone-adjusted response
        - tone_adjustments: Changes made
        - audience_alignment: How it matches audience
        - engagement_potential: Expected engagement
        - relationship_building: How it builds connection
        """
        
        response = await llm_text_gen(prompt)
        return json.loads(response) 