Content Strategist

#!/usr/bin/env python3
"""
Thumbnail Analyzer

Analyzes thumbnail text, composition patterns, and their correlation with
video performance (CTR, views). Identifies which thumbnail characteristics
drive higher click-through rates.

Expected CSV columns: video_id, title, views, ctr_pct, has_face, has_text,
  text_words, colors_dominant, emotion

Usage:
    python thumbnail_analyzer.py thumbnails.csv
    python thumbnail_analyzer.py thumbnails.csv --format json
    python thumbnail_analyzer.py thumbnails.csv --min-views 1000
    python thumbnail_analyzer.py thumbnails.csv --top 10
"""

import argparse
import csv
import json
import statistics
import sys
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple


def load_thumbnail_data(filepath: str) -> List[Dict[str, Any]]:
    """Load thumbnail data from CSV."""
    data = []
    with open(filepath, "r", newline="", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                entry = {
                    "video_id": row.get("video_id", "").strip(),
                    "title": row.get("title", "").strip(),
                    "views": int(row.get("views", 0)),
                    "ctr_pct": float(row.get("ctr_pct", 0)),
                    "has_face": row.get("has_face", "").strip().lower() in ("yes", "true", "1"),
                    "has_text": row.get("has_text", "").strip().lower() in ("yes", "true", "1"),
                    "text_words": int(row.get("text_words", 0)),
                    "colors_dominant": row.get("colors_dominant", "").strip().lower(),
                    "emotion": row.get("emotion", "neutral").strip().lower(),
                }
                data.append(entry)
            except (ValueError, KeyError):
                continue
    return data


def calculate_stats(values: List[float]) -> Dict[str, float]:
    """Calculate basic statistics for a list of values."""
    if not values:
        return {"count": 0, "mean": 0, "median": 0, "stdev": 0, "min": 0, "max": 0}
    return {
        "count": len(values),
        "mean": round(statistics.mean(values), 2),
        "median": round(statistics.median(values), 2),
        "stdev": round(statistics.stdev(values), 2) if len(values) > 1 else 0,
        "min": round(min(values), 2),
        "max": round(max(values), 2),
    }


def analyze_by_attribute(data: List[Dict], attribute: str, value_key: str = "ctr_pct") -> Dict[str, Any]:
    """Analyze performance grouped by a boolean or categorical attribute."""
    groups = defaultdict(list)
    for entry in data:
        val = entry.get(attribute)
        if isinstance(val, bool):
            key = "yes" if val else "no"
        else:
            key = str(val) if val else "unknown"
        groups[key].append(entry[value_key])

    result = {}
    for key, values in sorted(groups.items()):
        result[key] = calculate_stats(values)
    return result


def analyze_text_word_count(data: List[Dict]) -> Dict[str, Any]:
    """Analyze CTR by number of text words on thumbnail."""
    buckets = {"0 words": [], "1-3 words": [], "4-5 words": [], "6+ words": []}

    for entry in data:
        wc = entry["text_words"]
        ctr = entry["ctr_pct"]
        if wc == 0:
            buckets["0 words"].append(ctr)
        elif wc <= 3:
            buckets["1-3 words"].append(ctr)
        elif wc <= 5:
            buckets["4-5 words"].append(ctr)
        else:
            buckets["6+ words"].append(ctr)

    return {k: calculate_stats(v) for k, v in buckets.items()}


def analyze_color_performance(data: List[Dict]) -> Dict[str, Any]:
    """Analyze CTR by dominant thumbnail colors."""
    color_ctrs = defaultdict(list)
    for entry in data:
        colors = entry["colors_dominant"]
        if not colors:
            continue
        # Handle multi-color entries like "red-yellow"
        for color in colors.replace(",", "-").split("-"):
            color = color.strip()
            if color:
                color_ctrs[color].append(entry["ctr_pct"])

    return {color: calculate_stats(ctrs) for color, ctrs in sorted(color_ctrs.items())}


def find_top_performers(data: List[Dict], n: int = 10) -> List[Dict]:
    """Find top N performing thumbnails by CTR."""
    sorted_data = sorted(data, key=lambda x: x["ctr_pct"], reverse=True)
    return sorted_data[:n]


def find_bottom_performers(data: List[Dict], n: int = 10) -> List[Dict]:
    """Find bottom N performing thumbnails by CTR."""
    sorted_data = sorted(data, key=lambda x: x["ctr_pct"])
    return sorted_data[:n]


def generate_recommendations(analysis: Dict[str, Any]) -> List[Dict[str, str]]:
    """Generate actionable recommendations from analysis."""
    recs = []

    # Face analysis
    face_data = analysis.get("face_analysis", {})
    if "yes" in face_data and "no" in face_data:
        face_ctr = face_data["yes"]["mean"]
        no_face_ctr = face_data["no"]["mean"]
        if face_ctr > no_face_ctr * 1.1:
            lift = ((face_ctr - no_face_ctr) / no_face_ctr * 100)
            recs.append({
                "category": "Faces",
                "recommendation": f"Include faces in thumbnails - {lift:.0f}% higher CTR with faces "
                                  f"({face_ctr:.1f}% vs {no_face_ctr:.1f}%)",
                "priority": "high",
            })
        elif no_face_ctr > face_ctr * 1.1:
            recs.append({
                "category": "Faces",
                "recommendation": "Thumbnails without faces perform better for your content - "
                                  "consider graphic/text-focused designs",
                "priority": "medium",
            })

    # Text analysis
    text_data = analysis.get("text_analysis", {})
    if "yes" in text_data and "no" in text_data:
        text_ctr = text_data["yes"]["mean"]
        no_text_ctr = text_data["no"]["mean"]
        if text_ctr > no_text_ctr * 1.05:
            recs.append({
                "category": "Text",
                "recommendation": f"Text overlays improve CTR ({text_ctr:.1f}% vs {no_text_ctr:.1f}%)",
                "priority": "high",
            })

    # Word count analysis
    wc_data = analysis.get("word_count_analysis", {})
    if wc_data:
        best_bucket = max(
            [(k, v["mean"]) for k, v in wc_data.items() if v["count"] >= 3],
            key=lambda x: x[1],
            default=(None, 0),
        )
        if best_bucket[0]:
            recs.append({
                "category": "Text Length",
                "recommendation": f"Optimal text length: {best_bucket[0]} (avg CTR: {best_bucket[1]:.1f}%)",
                "priority": "medium",
            })

    # Emotion analysis
    emotion_data = analysis.get("emotion_analysis", {})
    if emotion_data:
        valid_emotions = [(k, v["mean"]) for k, v in emotion_data.items() if v["count"] >= 3]
        if valid_emotions:
            best_emotion = max(valid_emotions, key=lambda x: x[1])
            if best_emotion[1] > 0:
                recs.append({
                    "category": "Emotion",
                    "recommendation": f"Best performing emotion: '{best_emotion[0]}' "
                                      f"(avg CTR: {best_emotion[1]:.1f}%)",
                    "priority": "medium",
                })

    # Color analysis
    color_data = analysis.get("color_analysis", {})
    if color_data:
        valid_colors = [(k, v["mean"]) for k, v in color_data.items() if v["count"] >= 3]
        if valid_colors:
            top_colors = sorted(valid_colors, key=lambda x: x[1], reverse=True)[:3]
            color_list = ", ".join(f"{c[0]} ({c[1]:.1f}%)" for c in top_colors)
            recs.append({
                "category": "Colors",
                "recommendation": f"Top performing colors: {color_list}",
                "priority": "low",
            })

    return recs


def print_human(analysis: Dict[str, Any], top: List[Dict], bottom: List[Dict],
                recs: List[Dict]) -> None:
    """Print analysis in human-readable format."""
    print("=" * 70)
    print("  Thumbnail Performance Analysis")
    print("=" * 70)

    overall = analysis["overall_stats"]
    print(f"\n  Total Thumbnails Analyzed: {overall['count']}")
    print(f"  Average CTR: {overall['mean']:.2f}%")
    print(f"  Median CTR:  {overall['median']:.2f}%")
    print(f"  CTR Range:   {overall['min']:.2f}% - {overall['max']:.2f}%")

    # Face analysis
    print(f"\n  --- Face Presence ---")
    for key, stats in analysis.get("face_analysis", {}).items():
        print(f"  {key:<6}: avg CTR {stats['mean']:.2f}% (n={stats['count']})")

    # Text analysis
    print(f"\n  --- Text Overlay ---")
    for key, stats in analysis.get("text_analysis", {}).items():
        print(f"  {key:<6}: avg CTR {stats['mean']:.2f}% (n={stats['count']})")

    # Word count
    print(f"\n  --- Text Word Count ---")
    for bucket, stats in analysis.get("word_count_analysis", {}).items():
        if stats["count"] > 0:
            bar = "#" * max(1, int(stats["mean"] * 2))
            print(f"  {bucket:<12}: avg CTR {stats['mean']:.2f}% (n={stats['count']})  {bar}")

    # Emotion
    print(f"\n  --- Emotion ---")
    for emotion, stats in sorted(analysis.get("emotion_analysis", {}).items(),
                                  key=lambda x: x[1]["mean"], reverse=True):
        if stats["count"] > 0:
            bar = "#" * max(1, int(stats["mean"] * 2))
            print(f"  {emotion:<12}: avg CTR {stats['mean']:.2f}% (n={stats['count']})  {bar}")

    # Colors
    print(f"\n  --- Dominant Colors ---")
    for color, stats in sorted(analysis.get("color_analysis", {}).items(),
                                key=lambda x: x[1]["mean"], reverse=True):
        if stats["count"] > 0:
            bar = "#" * max(1, int(stats["mean"] * 2))
            print(f"  {color:<12}: avg CTR {stats['mean']:.2f}% (n={stats['count']})  {bar}")

    # Top performers
    print(f"\n  --- Top Performers ---")
    for i, t in enumerate(top[:5], 1):
        face = "Face" if t["has_face"] else "No face"
        text_info = f"{t['text_words']} words" if t["has_text"] else "No text"
        print(f"  {i}. {t['title'][:35]:<35} CTR: {t['ctr_pct']:.1f}%  ({face}, {text_info})")

    # Bottom performers
    print(f"\n  --- Bottom Performers ---")
    for i, t in enumerate(bottom[:5], 1):
        face = "Face" if t["has_face"] else "No face"
        text_info = f"{t['text_words']} words" if t["has_text"] else "No text"
        print(f"  {i}. {t['title'][:35]:<35} CTR: {t['ctr_pct']:.1f}%  ({face}, {text_info})")

    # Recommendations
    if recs:
        print(f"\n  --- Recommendations ---")
        priority_order = {"high": 0, "medium": 1, "low": 2}
        for rec in sorted(recs, key=lambda r: priority_order.get(r["priority"], 9)):
            marker = {"high": "[!!!]", "medium": "[ ! ]", "low": "[   ]"}.get(rec["priority"], "[   ]")
            print(f"  {marker} {rec['category']}: {rec['recommendation']}")

    print()


def main():
    parser = argparse.ArgumentParser(
        description="Analyze thumbnail text and composition patterns for optimization"
    )
    parser.add_argument("file", help="CSV file with thumbnail data")
    parser.add_argument("--format", choices=["human", "json"], default="human", help="Output format")
    parser.add_argument("--min-views", type=int, default=0, help="Minimum views filter")
    parser.add_argument("--top", type=int, default=10, help="Number of top/bottom performers to show")
    args = parser.parse_args()

    data = load_thumbnail_data(args.file)
    if not data:
        print("Error: No valid thumbnail data found", file=sys.stderr)
        sys.exit(1)

    if args.min_views > 0:
        data = [d for d in data if d["views"] >= args.min_views]
        if not data:
            print(f"Error: No thumbnails with >= {args.min_views} views", file=sys.stderr)
            sys.exit(1)

    ctrs = [d["ctr_pct"] for d in data]

    analysis = {
        "overall_stats": calculate_stats(ctrs),
        "face_analysis": analyze_by_attribute(data, "has_face"),
        "text_analysis": analyze_by_attribute(data, "has_text"),
        "word_count_analysis": analyze_text_word_count(data),
        "emotion_analysis": analyze_by_attribute(data, "emotion"),
        "color_analysis": analyze_color_performance(data),
    }

    top = find_top_performers(data, args.top)
    bottom = find_bottom_performers(data, args.top)
    recs = generate_recommendations(analysis)

    if args.format == "json":
        output = {
            "analysis": analysis,
            "top_performers": top,
            "bottom_performers": bottom,
            "recommendations": recs,
        }
        print(json.dumps(output, indent=2, default=str))
    else:
        print_human(analysis, top, bottom, recs)


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""
Video Content Planner

Plans video content calendar from topics, audience data, and content pillars.
Distributes topics across weeks, assigns production slots, and balances
content mix by pillar and funnel stage.

Expected JSON input with: channel, audience, content_pillars, topics

Usage:
    python video_content_planner.py topics.json
    python video_content_planner.py topics.json --weeks 8 --frequency 3
    python video_content_planner.py topics.json --platforms youtube,tiktok
    python video_content_planner.py topics.json --format json
"""

import argparse
import json
import sys
from collections import defaultdict
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Tuple


PLATFORM_DEFAULTS = {
    "youtube": {"optimal_length_min": 12, "max_length_min": 25, "best_days": ["Tuesday", "Thursday", "Saturday"]},
    "tiktok": {"optimal_length_min": 1, "max_length_min": 3, "best_days": ["Tuesday", "Wednesday", "Thursday", "Friday"]},
    "linkedin": {"optimal_length_min": 2, "max_length_min": 10, "best_days": ["Tuesday", "Wednesday", "Thursday"]},
    "instagram": {"optimal_length_min": 1, "max_length_min": 1.5, "best_days": ["Monday", "Wednesday", "Friday"]},
    "shorts": {"optimal_length_min": 0.75, "max_length_min": 1, "best_days": ["Monday", "Wednesday", "Friday", "Saturday"]},
}

FUNNEL_STAGES = {
    "tofu": {"label": "Top of Funnel", "target_ratio": 0.50},
    "mofu": {"label": "Middle of Funnel", "target_ratio": 0.30},
    "bofu": {"label": "Bottom of Funnel", "target_ratio": 0.20},
}

DAY_NAMES = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]


def load_topics(filepath: str) -> Dict[str, Any]:
    """Load topics and channel data from JSON."""
    with open(filepath, "r", encoding="utf-8") as f:
        return json.load(f)


def assign_funnel_stage(topic: Dict) -> str:
    """Assign funnel stage based on topic properties."""
    stage = topic.get("funnel_stage", "").lower()
    if stage in FUNNEL_STAGES:
        return stage

    # Infer from format/pillar
    fmt = topic.get("format", "").lower()
    pillar = topic.get("pillar", "").lower()

    if any(kw in pillar for kw in ["how", "tutorial", "guide", "education"]):
        return "tofu"
    if any(kw in pillar for kw in ["case study", "demo", "review", "comparison"]):
        return "mofu"
    if any(kw in pillar for kw in ["testimonial", "conversion", "pricing"]):
        return "bofu"
    if fmt in ("tutorial", "commentary"):
        return "tofu"
    if fmt in ("interview", "vlog"):
        return "mofu"
    return "tofu"


def prioritize_topics(topics: List[Dict]) -> List[Dict]:
    """Sort topics by priority score."""
    priority_values = {"high": 3, "medium": 2, "low": 1}
    return sorted(topics, key=lambda t: priority_values.get(t.get("priority", "medium"), 2), reverse=True)


def distribute_to_calendar(
    topics: List[Dict],
    pillars: List[Dict],
    weeks: int,
    frequency: int,
    platforms: List[str],
    start_date: datetime,
) -> List[Dict]:
    """Distribute topics across weekly calendar slots."""
    sorted_topics = prioritize_topics(list(topics))

    # Build pillar lookup
    pillar_map = {}
    for p in pillars:
        pillar_map[p["name"].lower()] = p

    # Calculate slots
    total_slots = weeks * frequency
    calendar = []
    topic_idx = 0
    used_pillars = defaultdict(int)

    for week in range(weeks):
        week_start = start_date + timedelta(weeks=week)
        week_entries = []

        # Determine which days to publish this week
        primary_platform = platforms[0] if platforms else "youtube"
        best_days = PLATFORM_DEFAULTS.get(primary_platform, {}).get("best_days", ["Tuesday", "Thursday"])

        publish_days = []
        for day_name in best_days[:frequency]:
            day_idx = DAY_NAMES.index(day_name)
            publish_date = week_start + timedelta(days=(day_idx - week_start.weekday()) % 7)
            if publish_date < week_start:
                publish_date += timedelta(weeks=1)
            publish_days.append(publish_date)

        # Fill remaining slots if frequency > best_days
        while len(publish_days) < frequency:
            for day_idx in range(7):
                candidate = week_start + timedelta(days=day_idx)
                if candidate not in publish_days:
                    publish_days.append(candidate)
                    if len(publish_days) >= frequency:
                        break

        publish_days.sort()

        for slot_idx, pub_date in enumerate(publish_days):
            if topic_idx < len(sorted_topics):
                topic = sorted_topics[topic_idx]
                topic_idx += 1
            else:
                # Generate placeholder for remaining slots
                topic = {
                    "title": f"[TBD] Week {week + 1} Slot {slot_idx + 1}",
                    "pillar": pillars[slot_idx % len(pillars)]["name"] if pillars else "General",
                    "priority": "low",
                    "placeholder": True,
                }

            pillar_name = topic.get("pillar", "General")
            pillar_info = pillar_map.get(pillar_name.lower(), {})
            funnel = assign_funnel_stage(topic)
            used_pillars[pillar_name] += 1

            # Calculate production dates
            script_due = pub_date - timedelta(days=7)
            film_date = pub_date - timedelta(days=4)
            edit_due = pub_date - timedelta(days=2)

            entry = {
                "week": week + 1,
                "publish_date": pub_date.strftime("%Y-%m-%d"),
                "publish_day": DAY_NAMES[pub_date.weekday()],
                "title": topic.get("title", "Untitled"),
                "pillar": pillar_name,
                "format": topic.get("format", pillar_info.get("format", "standard")),
                "funnel_stage": funnel,
                "priority": topic.get("priority", "medium"),
                "est_length_min": pillar_info.get("avg_length_min", 10),
                "platforms": platforms,
                "production_schedule": {
                    "script_due": script_due.strftime("%Y-%m-%d"),
                    "film_date": film_date.strftime("%Y-%m-%d"),
                    "edit_due": edit_due.strftime("%Y-%m-%d"),
                },
                "placeholder": topic.get("placeholder", False),
            }
            calendar.append(entry)

    return calendar


def analyze_calendar_balance(calendar: List[Dict], pillars: List[Dict]) -> Dict[str, Any]:
    """Analyze content mix balance in the calendar."""
    pillar_counts = defaultdict(int)
    funnel_counts = defaultdict(int)
    format_counts = defaultdict(int)
    total = len(calendar)

    for entry in calendar:
        if not entry.get("placeholder"):
            pillar_counts[entry["pillar"]] += 1
            funnel_counts[entry["funnel_stage"]] += 1
            format_counts[entry["format"]] += 1

    actual_total = sum(pillar_counts.values())

    # Compare pillar distribution to targets
    pillar_analysis = {}
    for p in pillars:
        name = p["name"]
        target = p.get("ratio", 1 / len(pillars))
        actual = pillar_counts.get(name, 0) / actual_total if actual_total > 0 else 0
        deviation = abs(actual - target)
        pillar_analysis[name] = {
            "target_pct": round(target * 100, 1),
            "actual_pct": round(actual * 100, 1),
            "count": pillar_counts.get(name, 0),
            "balanced": deviation < 0.10,
        }

    # Funnel analysis
    funnel_analysis = {}
    for stage, info in FUNNEL_STAGES.items():
        actual = funnel_counts.get(stage, 0) / actual_total if actual_total > 0 else 0
        funnel_analysis[stage] = {
            "label": info["label"],
            "target_pct": round(info["target_ratio"] * 100, 1),
            "actual_pct": round(actual * 100, 1),
            "count": funnel_counts.get(stage, 0),
        }

    issues = []
    for name, data in pillar_analysis.items():
        if not data["balanced"]:
            if data["actual_pct"] < data["target_pct"] - 10:
                issues.append(f"Under-represented pillar: {name} ({data['actual_pct']}% vs {data['target_pct']}% target)")
            elif data["actual_pct"] > data["target_pct"] + 10:
                issues.append(f"Over-represented pillar: {name} ({data['actual_pct']}% vs {data['target_pct']}% target)")

    return {
        "total_videos": total,
        "planned_videos": actual_total,
        "placeholder_videos": total - actual_total,
        "pillar_balance": pillar_analysis,
        "funnel_balance": funnel_analysis,
        "format_mix": dict(format_counts),
        "issues": issues,
    }


def print_human(calendar: List[Dict], analysis: Dict, channel: str) -> None:
    """Print calendar in human-readable format."""
    print("=" * 76)
    print(f"  Video Content Calendar - {channel}")
    print("=" * 76)
    print(f"\n  Total Videos: {analysis['total_videos']} ({analysis['planned_videos']} planned, "
          f"{analysis['placeholder_videos']} TBD)")

    current_week = 0
    for entry in calendar:
        if entry["week"] != current_week:
            current_week = entry["week"]
            print(f"\n  --- Week {current_week} ---")

        placeholder = " [TBD]" if entry.get("placeholder") else ""
        print(f"  {entry['publish_date']} ({entry['publish_day']:<9}): {entry['title']}{placeholder}")
        print(f"    Pillar: {entry['pillar']} | Format: {entry['format']} | "
              f"Funnel: {entry['funnel_stage']} | ~{entry['est_length_min']} min")
        sched = entry["production_schedule"]
        print(f"    Script: {sched['script_due']} | Film: {sched['film_date']} | Edit: {sched['edit_due']}")

    # Balance analysis
    print(f"\n  {'=' * 72}")
    print(f"  CONTENT MIX ANALYSIS")
    print(f"  {'=' * 72}")

    print(f"\n  Pillar Distribution:")
    for name, data in analysis["pillar_balance"].items():
        status = "OK" if data["balanced"] else "!!"
        bar = "#" * max(1, int(data["actual_pct"] / 3))
        print(f"    [{status}] {name:<25} {data['actual_pct']:>5.1f}% (target: {data['target_pct']:.1f}%)  {bar}")

    print(f"\n  Funnel Distribution:")
    for stage, data in analysis["funnel_balance"].items():
        bar = "#" * max(1, int(data["actual_pct"] / 3))
        print(f"    {data['label']:<20} {data['actual_pct']:>5.1f}% (target: {data['target_pct']:.1f}%)  {bar}")

    if analysis["issues"]:
        print(f"\n  Issues:")
        for issue in analysis["issues"]:
            print(f"    [!] {issue}")

    print()


def main():
    parser = argparse.ArgumentParser(
        description="Plan video content calendar from topics and audience data"
    )
    parser.add_argument("file", help="JSON file with topics and channel data")
    parser.add_argument("--format", choices=["human", "json"], default="human", help="Output format")
    parser.add_argument("--weeks", type=int, default=4, help="Number of weeks to plan (default: 4)")
    parser.add_argument("--frequency", type=int, default=2,
                        help="Videos per week (default: 2)")
    parser.add_argument("--platforms", default="youtube",
                        help="Comma-separated target platforms (default: youtube)")
    parser.add_argument("--start-date", help="Start date YYYY-MM-DD (default: next Monday)")
    args = parser.parse_args()

    data = load_topics(args.file)

    channel = data.get("channel", "My Channel")
    pillars = data.get("content_pillars", [])
    topics = data.get("topics", [])
    platforms = [p.strip() for p in args.platforms.split(",")]

    if args.start_date:
        start = datetime.strptime(args.start_date, "%Y-%m-%d")
    else:
        today = datetime.now()
        days_until_monday = (7 - today.weekday()) % 7
        if days_until_monday == 0:
            days_until_monday = 7
        start = today + timedelta(days=days_until_monday)

    calendar = distribute_to_calendar(topics, pillars, args.weeks, args.frequency, platforms, start)
    analysis = analyze_calendar_balance(calendar, pillars)

    if args.format == "json":
        print(json.dumps({"channel": channel, "calendar": calendar, "analysis": analysis}, indent=2))
    else:
        print_human(calendar, analysis, channel)


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""
Video SEO Optimizer

Optimizes video titles, descriptions, and tags for platform-specific SEO.
Analyzes existing metadata and provides optimization recommendations with
improved alternatives.

Expected JSON input with video details: title, description, tags, platform, topic

Usage:
    python video_seo_optimizer.py video_data.json
    python video_seo_optimizer.py video_data.json --platform youtube
    python video_seo_optimizer.py video_data.json --format json
    python video_seo_optimizer.py video_data.json --batch
"""

import argparse
import json
import re
import sys
import string
from collections import Counter
from typing import Any, Dict, List, Optional, Tuple


PLATFORM_LIMITS = {
    "youtube": {"title_max": 100, "title_optimal": 60, "desc_max": 5000, "tags_max_chars": 500, "tags_max_count": 30},
    "tiktok": {"title_max": 150, "title_optimal": 80, "desc_max": 2200, "tags_max_chars": 300, "tags_max_count": 10},
    "linkedin": {"title_max": 150, "title_optimal": 70, "desc_max": 3000, "tags_max_chars": 200, "tags_max_count": 5},
}

# Common power words that drive clicks
POWER_WORDS = {
    "curiosity": ["secret", "hidden", "revealed", "truth", "shocking", "surprising", "unexpected"],
    "urgency": ["now", "today", "immediately", "urgent", "before", "deadline", "limited"],
    "value": ["free", "ultimate", "complete", "proven", "guaranteed", "essential", "must-know"],
    "numbers": ["top", "best", "worst", "first", "last", "only"],
    "how_to": ["how", "why", "what", "when", "guide", "tutorial", "step", "tips", "tricks"],
}

# Stop words to identify keyword density
STOP_WORDS = {
    "the", "a", "an", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had",
    "do", "does", "did", "will", "would", "shall", "should", "may", "might", "must", "can",
    "could", "to", "of", "in", "for", "on", "with", "at", "by", "from", "as", "into", "through",
    "and", "but", "or", "nor", "not", "so", "yet", "both", "either", "neither", "each", "every",
    "this", "that", "these", "those", "i", "me", "my", "you", "your", "he", "she", "it", "we",
    "they", "them", "his", "her", "its", "our", "their",
}


def analyze_title(title: str, platform: str) -> Dict[str, Any]:
    """Analyze title for SEO effectiveness."""
    limits = PLATFORM_LIMITS.get(platform, PLATFORM_LIMITS["youtube"])
    issues = []
    score = 70  # Base score

    # Length check
    length = len(title)
    if length > limits["title_max"]:
        issues.append(f"Title exceeds maximum ({length}/{limits['title_max']} chars) - will be truncated")
        score -= 20
    elif length > limits["title_optimal"]:
        issues.append(f"Title may be truncated on mobile ({length}/{limits['title_optimal']} optimal chars)")
        score -= 5
    elif length < 20:
        issues.append("Title is very short - may lack keyword coverage")
        score -= 10

    # Power words
    title_lower = title.lower()
    found_power = []
    for category, words in POWER_WORDS.items():
        for word in words:
            if word in title_lower:
                found_power.append({"word": word, "category": category})
    if found_power:
        score += min(15, len(found_power) * 5)
    else:
        issues.append("No power words found - consider adding curiosity or value triggers")
        score -= 5

    # Number presence
    has_number = bool(re.search(r'\d', title))
    if has_number:
        score += 5
    else:
        issues.append("No numbers in title - titles with numbers get 36% more clicks")

    # ALL CAPS check
    words = title.split()
    caps_words = sum(1 for w in words if w.isupper() and len(w) > 1)
    if caps_words > len(words) * 0.5:
        issues.append("Excessive ALL CAPS - use sparingly for emphasis (1-2 words max)")
        score -= 10

    # Keyword front-loading
    # First 5 words should contain primary topic
    first_five = " ".join(words[:5]).lower()

    # Question format detection
    is_question = title.strip().endswith("?") or title_lower.startswith(("how", "why", "what", "when", "where"))

    return {
        "title": title,
        "length": length,
        "optimal_length": limits["title_optimal"],
        "max_length": limits["title_max"],
        "has_number": has_number,
        "is_question": is_question,
        "power_words": found_power,
        "caps_words": caps_words,
        "issues": issues,
        "score": max(0, min(100, score)),
    }


def analyze_description(description: str, platform: str, keywords: List[str] = None) -> Dict[str, Any]:
    """Analyze description for SEO effectiveness."""
    limits = PLATFORM_LIMITS.get(platform, PLATFORM_LIMITS["youtube"])
    issues = []
    score = 70

    length = len(description)
    word_count = len(description.split())

    if length > limits["desc_max"]:
        issues.append(f"Description exceeds limit ({length}/{limits['desc_max']} chars)")
        score -= 15
    elif word_count < 50:
        issues.append("Description is thin - aim for 150-300 words for SEO value")
        score -= 10
    elif word_count >= 150:
        score += 10

    # First 2-3 lines check (visible without expanding)
    lines = description.strip().split("\n")
    first_lines = " ".join(lines[:3])
    if len(first_lines) < 50:
        issues.append("First 2-3 lines are too short - this is the visible preview")
        score -= 5

    # Keyword density
    if keywords:
        desc_lower = description.lower()
        for kw in keywords:
            count = desc_lower.count(kw.lower())
            if count == 0:
                issues.append(f"Keyword '{kw}' not found in description")
                score -= 5
            elif count >= 5:
                issues.append(f"Keyword '{kw}' may be over-stuffed ({count} occurrences)")
                score -= 3

    # Has timestamps (YouTube)
    has_timestamps = bool(re.search(r'\d{1,2}:\d{2}', description))
    if platform == "youtube" and not has_timestamps:
        issues.append("No timestamps found - add chapter markers for longer videos")
    elif has_timestamps:
        score += 5

    # Has links/CTA
    has_links = "http" in description or "www." in description
    has_cta = any(cta in description.lower() for cta in ["subscribe", "follow", "sign up", "download", "click", "link"])

    if not has_cta:
        issues.append("No call-to-action found in description")

    return {
        "length": length,
        "word_count": word_count,
        "has_timestamps": has_timestamps,
        "has_links": has_links,
        "has_cta": has_cta,
        "issues": issues,
        "score": max(0, min(100, score)),
    }


def analyze_tags(tags: List[str], platform: str) -> Dict[str, Any]:
    """Analyze tags for SEO effectiveness."""
    limits = PLATFORM_LIMITS.get(platform, PLATFORM_LIMITS["youtube"])
    issues = []
    score = 70

    if not tags:
        return {"count": 0, "issues": ["No tags provided"], "score": 20}

    total_chars = sum(len(t) for t in tags)
    count = len(tags)

    if count > limits["tags_max_count"]:
        issues.append(f"Too many tags ({count}/{limits['tags_max_count']} max)")
        score -= 10
    elif count < 5:
        issues.append(f"Too few tags ({count}) - aim for 10-20 for YouTube")
        score -= 10

    if total_chars > limits["tags_max_chars"]:
        issues.append(f"Total tag characters exceed limit ({total_chars}/{limits['tags_max_chars']})")
        score -= 10

    # Tag length analysis
    short_tags = [t for t in tags if len(t.split()) == 1]
    long_tail = [t for t in tags if len(t.split()) >= 3]

    if not long_tail:
        issues.append("No long-tail tags - add 3+ word phrases for specific search queries")
        score -= 5
    else:
        score += 5

    # Duplicate check
    normalized = [t.lower().strip() for t in tags]
    dupes = [t for t, c in Counter(normalized).items() if c > 1]
    if dupes:
        issues.append(f"Duplicate tags found: {', '.join(dupes)}")
        score -= 5

    return {
        "count": count,
        "total_chars": total_chars,
        "max_chars": limits["tags_max_chars"],
        "short_tags": len(short_tags),
        "long_tail_tags": len(long_tail),
        "duplicates": dupes,
        "issues": issues,
        "score": max(0, min(100, score)),
    }


def optimize_video(video: Dict[str, Any], platform: str) -> Dict[str, Any]:
    """Run full optimization analysis on a video."""
    title = video.get("title", "")
    description = video.get("description", "")
    tags = video.get("tags", [])
    keywords = video.get("keywords", [])

    title_analysis = analyze_title(title, platform)
    desc_analysis = analyze_description(description, platform, keywords)
    tags_analysis = analyze_tags(tags, platform)

    # Overall score
    overall_score = (
        title_analysis["score"] * 0.40
        + desc_analysis["score"] * 0.35
        + tags_analysis["score"] * 0.25
    )

    # Aggregate recommendations
    all_issues = []
    all_issues.extend([{"area": "title", "issue": i} for i in title_analysis["issues"]])
    all_issues.extend([{"area": "description", "issue": i} for i in desc_analysis["issues"]])
    all_issues.extend([{"area": "tags", "issue": i} for i in tags_analysis["issues"]])

    return {
        "video_id": video.get("video_id", ""),
        "title": title,
        "platform": platform,
        "title_analysis": title_analysis,
        "description_analysis": desc_analysis,
        "tags_analysis": tags_analysis,
        "overall_score": round(overall_score, 1),
        "total_issues": len(all_issues),
        "issues": all_issues,
    }


def print_human(results: List[Dict[str, Any]]) -> None:
    """Print optimization results in human-readable format."""
    for result in results:
        print("=" * 70)
        print(f"  Video SEO Analysis - {result['platform'].upper()}")
        print("=" * 70)
        print(f"\n  Title: {result['title']}")
        print(f"  Overall SEO Score: {result['overall_score']}/100")

        # Title
        ta = result["title_analysis"]
        print(f"\n  --- Title Analysis (Score: {ta['score']}/100) ---")
        print(f"  Length: {ta['length']}/{ta['optimal_length']} optimal chars")
        print(f"  Has Number: {'Yes' if ta['has_number'] else 'No'}")
        print(f"  Question Format: {'Yes' if ta['is_question'] else 'No'}")
        if ta["power_words"]:
            pw = ", ".join(f"{p['word']} ({p['category']})" for p in ta["power_words"])
            print(f"  Power Words: {pw}")

        # Description
        da = result["description_analysis"]
        print(f"\n  --- Description Analysis (Score: {da['score']}/100) ---")
        print(f"  Word Count: {da['word_count']}")
        print(f"  Timestamps: {'Yes' if da['has_timestamps'] else 'No'}")
        print(f"  Links: {'Yes' if da['has_links'] else 'No'}")
        print(f"  CTA: {'Yes' if da['has_cta'] else 'No'}")

        # Tags
        tg = result["tags_analysis"]
        print(f"\n  --- Tags Analysis (Score: {tg['score']}/100) ---")
        print(f"  Tag Count: {tg['count']}")
        print(f"  Long-tail Tags: {tg.get('long_tail_tags', 0)}")

        # Issues
        if result["issues"]:
            print(f"\n  --- Issues ({result['total_issues']}) ---")
            for item in result["issues"]:
                print(f"  [{item['area']:>11}] {item['issue']}")

        print()


def main():
    parser = argparse.ArgumentParser(
        description="Optimize video titles, descriptions, and tags for platform SEO"
    )
    parser.add_argument("file", help="JSON file with video data")
    parser.add_argument("--format", choices=["human", "json"], default="human", help="Output format")
    parser.add_argument("--platform", choices=["youtube", "tiktok", "linkedin"],
                        default="youtube", help="Target platform (default: youtube)")
    parser.add_argument("--batch", action="store_true",
                        help="Process multiple videos (expects 'videos' array in JSON)")
    args = parser.parse_args()

    with open(args.file, "r", encoding="utf-8") as f:
        data = json.load(f)

    if args.batch:
        videos = data.get("videos", [])
    else:
        videos = [data]

    if not videos:
        print("Error: No video data found", file=sys.stderr)
        sys.exit(1)

    results = [optimize_video(v, args.platform) for v in videos]

    if args.format == "json":
        print(json.dumps({"results": results} if len(results) > 1 else results[0], indent=2))
    else:
        print_human(results)


if __name__ == "__main__":
    main()