import json
import re


def clean_json_string(text: str) -> str:
    if "```json" in text:
        text = text.split("```json")[1].split("```")[0]
    elif "```" in text:
        parts = text.split("```")
        if len(parts) >= 3:
            text = parts[1]
        elif len(parts) >= 2:
            text = parts[1]
    return text.strip()


def repair_json(json_str: str) -> str:
    json_str = json_str.strip()
    json_str = json_str.rstrip(", ")

    open_braces = json_str.count("{")
    close_braces = json_str.count("}")
    open_brackets = json_str.count("[")
    close_brackets = json_str.count("]")

    if open_braces > close_braces:
        json_str += "}" * (open_braces - close_braces)
    if open_brackets > close_brackets:
        json_str += "]" * (open_brackets - close_brackets)

    return json_str


def parse_and_repair(raw_text: str, max_preview=300):
    cleaned = clean_json_string(raw_text)
    try:
        return json.loads(cleaned), None
    except json.JSONDecodeError:
        print("[WARN] JSON Parse Error. Attempting repair with v2...")
        repaired = repair_json(cleaned)
        try:
            return json.loads(repaired), None
        except json.JSONDecodeError:
            try:
                repaired = repair_json_v2(repaired)
                return json.loads(repaired), None
            except json.JSONDecodeError as e:
                return None, {"error": str(e), "raw_preview": raw_text[:max_preview]}


def repair_json_v2(json_str: str) -> str:
    json_str = json_str.strip()
    json_str = json_str.rstrip(", ")

    in_string = False
    escape = False
    brace_depth = 0
    bracket_depth = 0
    last_good_pos = 0

    for i, ch in enumerate(json_str):
        if escape:
            escape = False
            continue
        if ch == '\\':
            escape = True
            continue
        if ch == '"' and not escape:
            in_string = not in_string
            continue
        if in_string:
            continue

        if ch == '{':
            brace_depth += 1
        elif ch == '}':
            brace_depth -= 1
        elif ch == '[':
            bracket_depth += 1
        elif ch == ']':
            bracket_depth -= 1

        if brace_depth >= 0 and bracket_depth >= 0:
            last_good_pos = i + 1

    end = last_good_pos
    trimmed = json_str[:end].rstrip(", ")

    cb = trimmed.count("{") - trimmed.count("}")
    sb = trimmed.count("[") - trimmed.count("]")

    if cb > 0:
        trimmed += "}" * cb
    if sb > 0:
        trimmed += "]" * sb

    return trimmed


def normalize_field(items, expected_type):
    """Convert model output to match Pydantic expectations."""
    if not isinstance(items, list):
        return items

    if expected_type == "object_list":
        if items and isinstance(items[0], str):
            return [{"title": s, "expected_ctr": "Medium"} for s in items]
        return items

    if expected_type == "string_list":
        if items and isinstance(items[0], dict):
            extracted = []
            for obj in items:
                val = obj.get("caption") or obj.get("description") or obj.get("title") or obj.get("name") or obj.get(
                    "url") or str(obj)
                extracted.append(val)
            return extracted
        return items

    if expected_type == "keyword_list":
        if items and isinstance(items[0], str):
            return [{"keyword": s, "search_volume": "Medium", "competition": "Medium", "relevance": 80} for s in items]
        return items

    if expected_type == "hashtag_list":
        if items and isinstance(items[0], str):
            return [{"tag": s, "post_count": "N/A"} for s in items]
        return items

    if expected_type == "caption_list":
        if items and isinstance(items[0], str):
            return [{"caption": s, "tone": "engaging"} for s in items]
        return items

    if expected_type == "pin_list":
        if items and isinstance(items[0], str):
            return [{"title": s, "description": s, "keyword_focus": s} for s in items]
        return items

    return items


def normalize_response(data: dict) -> dict:
    """Post-process model output to match expected Pydantic schemas."""
    if "video_titles" in data:
        data["video_titles"] = normalize_field(data["video_titles"], "object_list")
    if "thumbnail_ideas" in data:
        data["thumbnail_ideas"] = normalize_field(data["thumbnail_ideas"], "string_list")

    if "captions" in data:
        data["captions"] = normalize_field(data["captions"], "caption_list")
    if "hashtag_sets" in data and isinstance(data["hashtag_sets"], dict):
        for tier in ["small", "medium", "large"]:
            if tier in data["hashtag_sets"] and isinstance(data["hashtag_sets"][tier], list):
                if data["hashtag_sets"][tier] and isinstance(data["hashtag_sets"][tier][0], dict):
                    data["hashtag_sets"][tier] = [obj.get("tag", obj.get("name", str(obj))) for obj in
                                                  data["hashtag_sets"][tier]]
    if "content_ideas" in data and isinstance(data["content_ideas"], dict):
        for key in ["reels", "carousels", "stories"]:
            if key in data["content_ideas"]:
                data["content_ideas"][key] = normalize_field(data["content_ideas"][key], "string_list")

    if "core_keywords" in data:
        data["core_keywords"] = normalize_field(data["core_keywords"], "keyword_list")
        # LongCat returns search_volume as int, relevance as float → fix types
        for kw in data["core_keywords"]:
            if isinstance(kw, dict):
                if not isinstance(kw.get("search_volume"), str):
                    kw["search_volume"] = str(kw.get("search_volume", "Medium"))
                if isinstance(kw.get("relevance"), float):
                    kw["relevance"] = int(kw["relevance"] * 100) if kw["relevance"] < 1 else int(kw["relevance"])
                if kw.get("competition") is None:
                    kw["competition"] = "Medium"
                elif isinstance(kw["competition"], str):
                    kw["competition"] = kw["competition"].title()
    if "viral_hashtags" in data:
        data["viral_hashtags"] = normalize_field(data["viral_hashtags"], "hashtag_list")
        # LongCat returns post_count as int → convert to str
        for h in data["viral_hashtags"]:
            if isinstance(h, dict) and not isinstance(h.get("post_count"), str):
                h["post_count"] = str(h.get("post_count", "N/A"))

    if "pin_ideas" in data:
        data["pin_ideas"] = normalize_field(data["pin_ideas"], "pin_list")
    if "seo_keywords" in data:
        data["seo_keywords"] = normalize_field(data["seo_keywords"], "string_list")

    for field in ["related_phrases", "strategy_tips", "content_titles",
                  "tags", "engagement_strategies", "growth_strategies",
                  "article_topics", "thought_leadership_angles",
                  "engagement_hooks", "ad_copy_suggestions", "page_growth_tips",
                  "viral_hooks", "engagement_tactics",
                  "trending_angles", "viral_strategies",
                  "board_organization", "traffic_strategies",
                  "hashtags"]:
        if field in data:
            data[field] = normalize_field(data[field], "string_list")

    if "post_drafts" in data:
        if isinstance(data["post_drafts"], dict):
            data["post_drafts"] = [data["post_drafts"]]
        elif data["post_drafts"] and isinstance(data["post_drafts"][0], str):
            data["post_drafts"] = [{"headline": s, "body": s, "hook": s} for s in data["post_drafts"]]

    if "post_ideas" in data:
        if isinstance(data["post_ideas"], dict):
            data["post_ideas"] = [data["post_ideas"]]
        elif data["post_ideas"] and isinstance(data["post_ideas"][0], str):
            data["post_ideas"] = [{"type": "text", "content": s} for s in data["post_ideas"]]

    if "tweet_threads" in data:
        if isinstance(data["tweet_threads"], dict):
            data["tweet_threads"] = [data["tweet_threads"]]
        elif data["tweet_threads"] and isinstance(data["tweet_threads"][0], str):
            data["tweet_threads"] = [{"tweets": [data["tweet_threads"][0]], "theme": "topic"}]

    if "video_concepts" in data:
        if isinstance(data["video_concepts"], dict):
            data["video_concepts"] = [data["video_concepts"]]
        elif data["video_concepts"] and isinstance(data["video_concepts"][0], str):
            data["video_concepts"] = [{"hook": s, "script_snippet": s, "sound_suggestion": "Trending"} for s in
                                      data["video_concepts"]]

    if "corrections" in data:
        if isinstance(data["corrections"], list) and data["corrections"]:
            if isinstance(data["corrections"][0], str):
                data["corrections"] = [
                    {"original": s, "corrected": s, "error_type": "grammar", "explanation": "Automatically corrected."}
                    for s in data["corrections"]]
            elif isinstance(data["corrections"][0], dict):
                for c in data["corrections"]:
                    c.setdefault("error_type", "grammar")
                    c.setdefault("explanation", "Review and correct this.")
    if "issues" in data:
        if isinstance(data["issues"], list) and data["issues"]:
            if isinstance(data["issues"][0], str):
                data["issues"] = [{"issue_type": s, "location": "text", "suggestion": "Review this section."} for s in
                                  data["issues"]]
            elif isinstance(data["issues"][0], dict):
                for iss in data["issues"]:
                    iss.setdefault("issue_type", "style")
                    iss.setdefault("location", "text")
                    iss.setdefault("suggestion", "Review this section.")
    if "grammar_score" in data:
        if not isinstance(data["grammar_score"], (int, float)):
            try:
                data["grammar_score"] = int(float(str(data["grammar_score"])))
            except (ValueError, TypeError):
                data["grammar_score"] = 85
        data["grammar_score"] = max(0, min(100, data["grammar_score"]))
    if "word_count" in data and not isinstance(data["word_count"], int):
        try:
            data["word_count"] = int(float(str(data["word_count"])))
        except (ValueError, TypeError):
            data["word_count"] = 0
    if "sentence_count" in data and not isinstance(data["sentence_count"], int):
        try:
            data["sentence_count"] = int(float(str(data["sentence_count"])))
        except (ValueError, TypeError):
            data["sentence_count"] = 0

    if "description_template" in data and isinstance(data["description_template"], dict):
        data["description_template"] = str(data["description_template"])

    if "target_audience" in data and isinstance(data["target_audience"], dict):
        data["target_audience"] = str(data["target_audience"])

    return data


def cross_map_fields(data: dict) -> dict:
    if "video_titles" in data and "content_titles" not in data:
        data["content_titles"] = [t.get("title", str(t)) for t in data["video_titles"] if isinstance(t, dict)]
    if "video_titles" in data and "related_phrases" not in data:
        titles = data.get("content_titles") or [t.get("title", str(t)) for t in data["video_titles"] if isinstance(t, dict)]
        data["related_phrases"] = titles[:3]
    if "tags" in data and "hashtags" not in data:
        data["hashtags"] = data["tags"]
    if "tags" in data and "core_keywords" not in data:
        data["core_keywords"] = [{"keyword": t, "search_volume": "Medium", "competition": "Medium", "relevance": 80} for t in data["tags"]]
    if "engagement_strategies" in data and "strategy_tips" not in data:
        data["strategy_tips"] = data["engagement_strategies"]
    if "growth_strategies" in data and "strategy_tips" not in data:
        data["strategy_tips"] = data["growth_strategies"]
    if "hashtags" in data and "viral_hashtags" not in data:
        data["viral_hashtags"] = [{"tag": h, "post_count": "N/A"} for h in data["hashtags"]]
    if "best_posting_time" in data and "strategy_tips" not in data:
        data["strategy_tips"] = [f"Post during: {data['best_posting_time']}"]

    # Instagram cross-maps
    if "hashtag_sets" in data and isinstance(data["hashtag_sets"], dict) and "hashtags" not in data:
        all_tags = []
        for tier in ["small", "medium", "large"]:
            if tier in data["hashtag_sets"] and isinstance(data["hashtag_sets"][tier], list):
                all_tags.extend(data["hashtag_sets"][tier])
        if all_tags:
            data["hashtags"] = all_tags
    if "hashtags" in data and "core_keywords" not in data:
        data["core_keywords"] = [{"keyword": t.lstrip("#"), "search_volume": "Medium", "competition": "Medium", "relevance": 80} for t in data["hashtags"]]
    if "captions" in data and "content_titles" not in data:
        data["content_titles"] = [c.get("caption", str(c)) for c in data["captions"] if isinstance(c, dict)]
    if "content_ideas" in data and isinstance(data["content_ideas"], dict) and "strategy_tips" not in data:
        ideas = []
        for key in ["reels", "carousels", "stories"]:
            if key in data["content_ideas"] and isinstance(data["content_ideas"][key], list):
                ideas.extend(data["content_ideas"][key])
        if ideas:
            data["strategy_tips"] = ideas

    # Pinterest cross-maps
    if "seo_keywords" in data and "core_keywords" not in data:
        data["core_keywords"] = [{"keyword": k, "search_volume": "Medium", "competition": "Medium", "relevance": 80} for k in data["seo_keywords"]]
    if "pin_ideas" in data and "content_titles" not in data:
        data["content_titles"] = [p.get("title", str(p)) for p in data["pin_ideas"] if isinstance(p, dict)]
    if "pin_ideas" in data and "related_phrases" not in data:
        data["related_phrases"] = [p.get("keyword_focus", p.get("description", str(p)))[:100] for p in data["pin_ideas"] if isinstance(p, dict)]
    if "traffic_strategies" in data and "strategy_tips" not in data:
        data["strategy_tips"] = data["traffic_strategies"]
    if "board_organization" in data and "strategy_tips" not in data:
        data["strategy_tips"] = data["board_organization"]
    if "pin_ideas" in data and "hashtags" not in data:
        keywords = [p.get("keyword_focus", "") for p in data["pin_ideas"] if isinstance(p, dict)]
        keywords = [k for k in keywords if k]
        if keywords:
            data["hashtags"] = keywords

    # Catch-all: if hashtags was set by any platform mapping above but viral_hashtags wasn't
    if "hashtags" in data and ("viral_hashtags" not in data or data.get("viral_hashtags") is None):
        data["viral_hashtags"] = [{"tag": h, "post_count": "N/A"} for h in data["hashtags"]]

    # LinkedIn cross-maps
    if "post_drafts" in data and "content_titles" not in data:
        data["content_titles"] = [d.get("headline", str(d)) for d in data["post_drafts"] if isinstance(d, dict)]
    if "post_drafts" in data and "related_phrases" not in data:
        data["related_phrases"] = [d.get("body", str(d))[:100] for d in data["post_drafts"] if isinstance(d, dict)]
    if "thought_leadership_angles" in data and "strategy_tips" not in data:
        data["strategy_tips"] = data["thought_leadership_angles"]
    if "engagement_prompts" in data and "related_phrases" not in data:
        data["related_phrases"] = data["engagement_prompts"]

    return data


def validate_and_fill_data_defaults(data: dict, defaults: dict) -> dict:
    for key, default_val in defaults.items():
        if key not in data or data[key] is None:
            data[key] = default_val
    return data


def run_analysis(messages, defaults=None, temperature=0.3, max_new_tokens=2000):
    try:
        from services.model_router import generate_text
        raw = generate_text(messages, temperature, max_new_tokens)
    except Exception as e:
        print(f"[generate_text error] {e}")
        base = dict(defaults) if defaults else {}
        base["error"] = "AI model temporarily unavailable. Please try again."
        return base
    if raw is None:
        base = dict(defaults) if defaults else {}
        base["error"] = "Model failed to load on server startup. Check logs."
        return base

    data, err = parse_and_repair(raw)
    if err:
        base = dict(defaults) if defaults else {}
        base["error"] = "The AI response was malformed. Please try again."
        return base

    if not isinstance(data, dict):
        print(f"[run_analysis] Expected dict, got {type(data).__name__}")
        base = dict(defaults) if defaults else {}
        base["error"] = "The AI response was malformed. Please try again."
        return base

    try:
        data = normalize_response(data)
    except Exception as e:
        print(f"[normalize_response error] {e}")
        base = dict(defaults) if defaults else {}
        base["error"] = "Failed to process response. Please try again."
        return base

    if defaults:
        try:
            data = validate_and_fill_data_defaults(data, defaults)
            data = cross_map_fields(data)
            return data
        except Exception as e:
            print(f"[validate error] {e}")
            base = dict(defaults) if defaults else {}
            base["error"] = "Failed to validate response. Please try again."
            return base
    data = cross_map_fields(data)
    return data