"""
LinkedIn Post Generation Prompts

This module contains prompt templates and builders for generating LinkedIn posts.
"""

from typing import Any, Optional, Dict


class PostPromptBuilder:
    """Builder class for LinkedIn post generation prompts."""
    
    @staticmethod
    def build_post_prompt(request: Any, persona: Optional[Dict[str, Any]] = None) -> str:
        """
        Build prompt for post generation.
        
        Args:
            request: LinkedInPostRequest object containing generation parameters
            
        Returns:
            Formatted prompt string for post generation
        """
        persona_block = ""
        if persona:
            try:
                # Expecting structure similar to persona_service.get_persona_for_platform output
                core = persona.get('core_persona', persona)
                platform_adaptation = persona.get('platform_adaptation', persona.get('platform_persona', {}))
                linguistic = core.get('linguistic_fingerprint', {})
                sentence_metrics = linguistic.get('sentence_metrics', {})
                lexical_features = linguistic.get('lexical_features', {})
                rhetorical_devices = linguistic.get('rhetorical_devices', {})
                tonal_range = core.get('tonal_range', {})

                persona_block = f"""
        PERSONA CONTEXT:
        - Persona Name: {core.get('persona_name', 'N/A')}
        - Archetype: {core.get('archetype', 'N/A')}
        - Core Belief: {core.get('core_belief', 'N/A')}
        - Tone: {tonal_range.get('default_tone', request.tone)}
        - Sentence Length (avg): {sentence_metrics.get('average_sentence_length_words', 15)} words
        - Preferred Sentence Type: {sentence_metrics.get('preferred_sentence_type', 'simple and compound')}
        - Go-to Words: {', '.join(lexical_features.get('go_to_words', [])[:5])}
        - Avoid Words: {', '.join(lexical_features.get('avoid_words', [])[:5])}
        - Rhetorical Style: {rhetorical_devices.get('summary','balanced rhetorical questions and examples')}
        """.rstrip()
            except Exception:
                persona_block = ""

        prompt = f"""
        You are an expert LinkedIn content strategist with 10+ years of experience in the {request.industry} industry. Create a highly engaging, professional LinkedIn post that drives meaningful engagement and establishes thought leadership.

        TOPIC: {request.topic}
        INDUSTRY: {request.industry}
        TONE: {request.tone}
        TARGET AUDIENCE: {request.target_audience or 'Industry professionals, decision-makers, and thought leaders'}
        MAX LENGTH: {request.max_length} characters

        {persona_block}

        CONTENT REQUIREMENTS:
        - Start with a compelling hook that addresses a pain point or opportunity
        - Include 2-3 specific, actionable insights or data points
        - Use storytelling elements to make it relatable and memorable
        - Include industry-specific examples or case studies when relevant
        - End with a thought-provoking question or clear call-to-action
        - Use professional yet conversational language that encourages discussion

        ENGAGEMENT STRATEGY:
        - Include 3-5 highly relevant, trending hashtags (mix of broad and niche)
        - Use line breaks and emojis strategically for readability
        - Encourage comments by asking for opinions or experiences
        - Make it shareable by providing genuine value

        KEY POINTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Current industry trends, challenges, and opportunities'}

        FORMATTING:
        - Use bullet points or numbered lists for key insights
        - Include relevant emojis to enhance visual appeal
        - Break text into digestible paragraphs (2-3 lines max)
        - Leave space for engagement (don't fill the entire character limit)

        REMEMBER: This post should position the author as a knowledgeable industry expert while being genuinely helpful to the audience.
        """
        return prompt.strip()