import json
import random
from core import utils
from ai import models as ai_models
from story.bible_tracker import filter_characters


def enrich(bp, folder, context=""):
    utils.log("ENRICHER", "Fleshing out details from description...")

    if 'book_metadata' not in bp: bp['book_metadata'] = {}
    if 'characters' not in bp: bp['characters'] = []
    if 'plot_beats' not in bp: bp['plot_beats'] = []

    prompt = f"""
    ROLE: Creative Director
    TASK: Create a comprehensive Book Bible from the user description.

    INPUT DATA:
    - USER_DESCRIPTION: "{bp.get('manual_instruction', 'A generic story')}"
    - CONTEXT (Sequel): {context}

    STEPS:
    1. Generate a catchy Title.
    2. Define the Genre and Tone.
    3. Determine the Time Period (e.g. "Modern", "1920s", "Sci-Fi Future").
    4. Define Formatting Rules for text messages, thoughts, and chapter headers.
    5. Create Protagonist and Antagonist/Love Interest.
       - Logic: If sequel, reuse context. If new, create.
    6. Outline 5-7 core Plot Beats.
    7. Define a 'structure_prompt' describing the narrative arc (e.g. "Hero's Journey", "3-Act Structure", "Detective Procedural").

    OUTPUT_FORMAT (JSON):
    {{
      "book_metadata": {{ "title": "Book Title", "genre": "Genre", "content_warnings": ["Violence", "Major Character Death"], "structure_prompt": "...", "style": {{ "tone": "Tone", "time_period": "Modern", "formatting_rules": ["Chapter Headers: Number + Title", "Text Messages: Italic", "Thoughts: Italic"] }} }},
      "characters": [ {{ "name": "John Doe", "role": "Protagonist", "description": "Description", "key_events": ["Planned injury in Act 2"] }} ],
      "plot_beats": [ "Beat 1", "Beat 2", "..." ]
    }}
    """
    try:
        response = ai_models.model_logic.generate_content(prompt)
        utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
        ai_data = json.loads(utils.clean_json(response.text))

        if 'book_metadata' not in bp: bp['book_metadata'] = {}

        if 'title' not in bp['book_metadata']:
            bp['book_metadata']['title'] = ai_data.get('book_metadata', {}).get('title')
        if 'structure_prompt' not in bp['book_metadata']:
            bp['book_metadata']['structure_prompt'] = ai_data.get('book_metadata', {}).get('structure_prompt')
        if 'content_warnings' not in bp['book_metadata']:
            bp['book_metadata']['content_warnings'] = ai_data.get('book_metadata', {}).get('content_warnings', [])

        if 'style' not in bp['book_metadata']: bp['book_metadata']['style'] = {}

        source_style = ai_data.get('book_metadata', {}).get('style', {})
        for k, v in source_style.items():
            if k not in bp['book_metadata']['style']:
                bp['book_metadata']['style'][k] = v

        if 'characters' not in bp or not bp['characters']:
            bp['characters'] = ai_data.get('characters', [])

        if 'characters' in bp:
            bp['characters'] = filter_characters(bp['characters'])

        if 'plot_beats' not in bp or not bp['plot_beats']:
            bp['plot_beats'] = ai_data.get('plot_beats', [])

        return bp
    except Exception as e:
        utils.log("ENRICHER", f"Enrichment failed: {e}")
        return bp


def plan_structure(bp, folder):
    utils.log("ARCHITECT", "Creating structure...")

    structure_type = bp.get('book_metadata', {}).get('structure_prompt')

    if not structure_type:
        label = bp.get('length_settings', {}).get('label', 'Novel')
        structures = {
            "Chapter Book": "Create a simple episodic structure with clear chapter hooks.",
            "Young Adult": "Create a character-driven arc with high emotional stakes and a clear 'Coming of Age' theme.",
            "Flash Fiction": "Create a single, impactful scene structure with a twist.",
            "Short Story": "Create a concise narrative arc (Inciting Incident -> Rising Action -> Climax -> Resolution).",
            "Novella": "Create a standard 3-Act Structure.",
            "Novel": "Create a detailed 3-Act Structure with A and B plots.",
            "Epic": "Create a complex, multi-arc structure (Hero's Journey) with extensive world-building events."
        }
        structure_type = structures.get(label, "Create a 3-Act Structure.")

    beats_context = bp.get('plot_beats', [])
    target_chapters = bp.get('length_settings', {}).get('chapters', 'flexible')
    target_words = bp.get('length_settings', {}).get('words', 'flexible')
    chars_summary = [{"name": c.get("name"), "role": c.get("role")} for c in bp.get('characters', [])]

    prompt = f"""
    ROLE: Story Architect
    TASK: Create a detailed structural event outline for a {target_chapters}-chapter book.

    BOOK:
    - TITLE: {bp['book_metadata']['title']}
    - GENRE: {bp.get('book_metadata', {}).get('genre', 'Fiction')}
    - TARGET_CHAPTERS: {target_chapters}
    - TARGET_WORDS: {target_words}
    - STRUCTURE: {structure_type}

    CHARACTERS: {json.dumps(chars_summary)}

    USER_BEATS (must all be preserved and woven into the outline):
    {json.dumps(beats_context)}

    REQUIREMENTS:
    - Produce enough events to fill approximately {target_chapters} chapters.
    - Each event must serve a narrative purpose (setup, escalation, reversal, climax, resolution).
    - Distribute events across a beginning, middle, and end — avoid front-loading.
    - Character arcs must be visible through the events (growth, change, revelation).

    OUTPUT_FORMAT (JSON): {{ "events": [{{ "description": "String", "purpose": "String" }}] }}
    """
    try:
        response = ai_models.model_logic.generate_content(prompt)
        utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
        return json.loads(utils.clean_json(response.text))['events']
    except:
        return []


def expand(events, pass_num, target_chapters, bp, folder):
    utils.log("ARCHITECT", f"Expansion pass {pass_num} | Current Beats: {len(events)} | Target Chaps: {target_chapters}")

    event_ceiling = int(target_chapters * 1.5)
    if len(events) >= event_ceiling:
        task = (
            f"The outline already has {len(events)} beats for a {target_chapters}-chapter book — do NOT add more events. "
            f"Instead, enrich each existing beat's description with more specific detail: setting, characters involved, emotional stakes, and how it connects to what follows."
        )
    else:
        task = (
            f"Expand the outline toward {target_chapters} chapters. "
            f"Current count: {len(events)} beats. "
            f"Add intermediate events to fill pacing gaps, deepen subplots, and ensure character arcs are visible. "
            f"Do not overshoot — aim for {target_chapters} to {event_ceiling} total events."
        )

    original_beats = bp.get('plot_beats', [])

    prompt = f"""
    ROLE: Story Architect
    TASK: {task}

    ORIGINAL_USER_BEATS (must all remain present):
    {json.dumps(original_beats)}

    CURRENT_EVENTS:
    {json.dumps(events)}

    RULES:
    1. PRESERVE all original user beats — do not remove or alter them.
    2. New events must serve a clear narrative purpose (tension, character, world, reversal).
    3. Avoid repetitive events — each beat must be distinct.
    4. Distribute additions evenly — do not front-load the outline.

    OUTPUT_FORMAT (JSON): {{ "events": [{{"description": "String", "purpose": "String"}}] }}
    """
    try:
        response = ai_models.model_logic.generate_content(prompt)
        utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
        new_events = json.loads(utils.clean_json(response.text))['events']

        if len(new_events) > len(events):
            utils.log("ARCHITECT", f"  -> Added {len(new_events) - len(events)} new beats.")
        elif len(str(new_events)) > len(str(events)) + 20:
            utils.log("ARCHITECT", f"  -> Fleshed out descriptions (Text grew by {len(str(new_events)) - len(str(events))} chars).")
        else:
            utils.log("ARCHITECT", "  -> No significant changes.")
        return new_events
    except Exception as e:
        utils.log("ARCHITECT", f"  -> Pass skipped due to error: {e}")
        return events


def create_chapter_plan(events, bp, folder):
    utils.log("ARCHITECT", "Finalizing Chapters...")
    target = bp['length_settings']['chapters']
    words = bp['length_settings'].get('words', 'Flexible')

    include_prologue = bp.get('length_settings', {}).get('include_prologue', False)
    include_epilogue = bp.get('length_settings', {}).get('include_epilogue', False)

    structure_instructions = ""
    if include_prologue: structure_instructions += "- Include a 'Prologue' (chapter_number: 0) to set the scene.\n"
    if include_epilogue: structure_instructions += "- Include an 'Epilogue' (chapter_number: 'Epilogue') to wrap up.\n"

    meta = bp.get('book_metadata', {})
    style = meta.get('style', {})
    pov_chars = style.get('pov_characters', [])
    pov_instruction = ""
    if pov_chars:
        pov_instruction = f"- Assign a 'pov_character' for each chapter from this list: {json.dumps(pov_chars)}."

    prompt = f"""
    ROLE: Pacing Specialist
    TASK: Group the provided events into chapters for a {meta.get('genre', 'Fiction')} {bp['length_settings'].get('label', 'novel')}.

    GUIDELINES:
    - AIM for approximately {target} chapters, but the final count may vary ±15% if the story structure demands it.
    - TARGET_WORDS for the whole book: {words}
    - Assign pacing to each chapter: Very Fast / Fast / Standard / Slow / Very Slow
    - estimated_words per chapter should reflect its pacing:
      Very Fast ≈ 60% of average, Fast ≈ 80%, Standard ≈ 100%, Slow ≈ 125%, Very Slow ≈ 150%
    - Do NOT force equal word counts. Natural variation makes the book feel alive.
    {structure_instructions}
    {pov_instruction}

    INPUT_EVENTS: {json.dumps(events)}

    OUTPUT_FORMAT (JSON): [{{"chapter_number": 1, "title": "String", "pov_character": "String", "pacing": "String", "estimated_words": 2000, "beats": ["String"]}}]
    """
    try:
        response = ai_models.model_logic.generate_content(prompt)
        utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
        plan = json.loads(utils.clean_json(response.text))

        target_str = str(words).lower().replace(',', '').replace('k', '000').replace('+', '').replace(' ', '')
        target_val = 0
        if '-' in target_str:
            try:
                parts = target_str.split('-')
                target_val = int((int(parts[0]) + int(parts[1])) / 2)
            except: pass
        else:
            try: target_val = int(target_str)
            except: pass

        if target_val > 0:
            variance = random.uniform(0.92, 1.08)
            target_val = int(target_val * variance)
            utils.log("ARCHITECT", f"Word target after variance ({variance:.2f}x): {target_val} words.")

            current_sum = sum(int(c.get('estimated_words', 0)) for c in plan)
            if current_sum > 0:
                base_factor = target_val / current_sum
                pacing_weight = {
                    'very fast': 0.60, 'fast': 0.80, 'standard': 1.00,
                    'slow': 1.25, 'very slow': 1.50
                }
                for c in plan:
                    pw = pacing_weight.get(c.get('pacing', 'standard').lower(), 1.0)
                    c['estimated_words'] = max(300, int(c.get('estimated_words', 0) * base_factor * pw))

                adjusted_sum = sum(c['estimated_words'] for c in plan)
                if adjusted_sum > 0:
                    norm = target_val / adjusted_sum
                    for c in plan:
                        c['estimated_words'] = max(300, int(c['estimated_words'] * norm))

                utils.log("ARCHITECT", f"Chapter lengths scaled by pacing. Total ≈ {sum(c['estimated_words'] for c in plan)} words across {len(plan)} chapters.")

        return plan
    except Exception as e:
        utils.log("ARCHITECT", f"Failed to create chapter plan: {e}")
        return []