from elevenlabs import VoiceSettings, generate, save

def generate_voiceover(script_text: str, output_path: str = "voiceover.mp3"):
    api_key = "YOUR_ELEVENLABS_API_KEY"
    voice_id = "pNInz6obpgDQGcFmaJgB"  # Adam voice

    audio = generate(
        api_key=api_key,
        text=script_text,
        voice=voice_id,
        model="eleven_turbo_v2",
        voice_settings=VoiceSettings(
            stability=0.75,
            similarity_boost=0.85,
            style=0.3,
            use_speaker_boost=True,
        ),
    )
    save(audio, output_path)
    print(f"✅ Voiceover saved to {output_path}")
    return output_path

import whisper

def generate_srt(audio_path: str, output_srt: str = "subtitles.srt"):
    model = whisper.load_model("base")
    result = model.transcribe(audio_path, task="transcribe")
    with open(output_srt, "w") as f:
        for i, segment in enumerate(result["segments"], 1):
            start = format_time(segment["start"])
            end = format_time(segment["end"])
            f.write(f"{i}\n{start} --> {end}\n{segment['text'].strip()}\n\n")
    print(f"✅ Subtitles saved to {output_srt}")
    return output_srt

def format_time(seconds):
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    ms = int((seconds % 1) * 1000)
    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"

import requests, os
from pathlib import Path

CAPCUT_API = "http://localhost:9001"
DRAFTS_DIR = r"C:\Users\clint\AppData\Local\CapCut\User Data\Projects\com.lveditor.draft"

def assemble_dg_video(topic_title, voiceover_path, broll_clips, srt_path, key_term=None, key_stat=None):
    print(f"🎬 Creating draft: {topic_title}")
    res = requests.post(f"{CAPCUT_API}/create_draft", json={"name": topic_title})
    draft_id = res.json()["draft_id"]

    timestamp = 0
    for clip in broll_clips:
        requests.post(f"{CAPCUT_API}/add_video", json={"draft_id": draft_id, "video_path": clip["path"], "start_time": timestamp, "duration": clip["duration"], "transition": "fade"})
        timestamp += clip["duration"]

    requests.post(f"{CAPCUT_API}/add_audio", json={"draft_id": draft_id, "audio_path": voiceover_path, "volume": 1.0, "track_index": 1})

    bg = "./assets/bg_music_corporate.mp3"
    if os.path.exists(bg):
        requests.post(f"{CAPCUT_API}/add_audio", json={"draft_id": draft_id, "audio_path": bg, "volume": 0.12, "track_index": 2})

    requests.post(f"{CAPCUT_API}/add_subtitle", json={"draft_id": draft_id, "subtitle_path": srt_path, "language": "en-US", "font_size": 38, "bold": True, "position": {"x": 0.5, "y": 0.82}})

    if key_term:
        requests.post(f"{CAPCUT_API}/add_text", json={"draft_id": draft_id, "text": f"📌 {key_term}", "start_time": 10.0, "duration": 5.0, "font_size": 44, "bold": True, "color": "#00d4ff", "position": {"x": 0.5, "y": 0.12}})
    if key_stat:
        requests.post(f"{CAPCUT_API}/add_text", json={"draft_id": draft_id, "text": key_stat, "start_time": 5.0, "duration": 4.0, "font_size": 64, "bold": True, "color": "#f0a828", "position": {"x": 0.5, "y": 0.25}})

    requests.post(f"{CAPCUT_API}/add_image", json={"draft_id": draft_id, "image_path": "./assets/brand_watermark.png", "start_time": 0, "duration": timestamp, "position": {"x": 0.88, "y": 0.06}, "scale": 0.15, "opacity": 0.7})

    output_path = str(Path(DRAFTS_DIR) / topic_title.replace(" ", "_"))
    requests.post(f"{CAPCUT_API}/save_draft", json={"draft_id": draft_id, "draft_folder": output_path})
    print(f"✅ Draft saved: {output_path}")
    return draft_id

{
  "brand": {
    "watermark_path": "./assets/brand_watermark.png",
    "bg_music_path": "./assets/bg_music_corporate.mp3",
    "bg_music_volume": 0.12,
    "subtitle_font_size": 38,
    "accent_color": "#00d4ff",
    "stat_color": "#f0a828"
  },
  "video": {"aspect_ratio": "9:16", "resolution": "1080x1920", "fps": 30, "format": "mp4"},
  "capcut_server": {"host": "localhost", "port": 9001, "drafts_dir": "C:\\Users\\clint\\AppData\\Local\\CapCut\\User Data\\Projects\\com.lveditor.draft"}
}

import requests, os
from dotenv import load_dotenv
load_dotenv()

TIKTOK_BASE = "https://open.tiktokapis.com/v2"
DG_HASHTAGS = ["#datagovernance","#datamanagement","#dataquality",
    "#dataengineer","#dataanalyst","#MDM","#datalineage",
    "#tecktok","#learnontiktok","#datagovernor"]

def post_video(video_url: str, topic_title: str) -> str:
    token = os.getenv("TIKTOK_ACCESS_TOKEN")
    caption = f"{topic_title} | thedatagovernor.com\n\n" + " ".join(DG_HASHTAGS[:8])
    res = requests.post(
        f"{TIKTOK_BASE}/post/publish/video/init/",
        headers={"Authorization": f"Bearer {token}",
                 "Content-Type": "application/json"},
        json={"post_info": {
                "title": caption[:2200],
                "privacy_level": "PUBLIC_TO_EVERYONE",
                "disable_duet": False, "disable_comment": False,
                "disable_stitch": False,
                "video_cover_timestamp_ms": 2000},
              "source_info": {
                "source": "PULL_FROM_URL",
                "video_url": video_url}},
    )
    data = res.json()
    if res.status_code == 200:
        print(f"✅ Posted! publish_id: {data['data']['publish_id']}")
        return data["data"]["publish_id"]
    raise Exception(f"❌ Failed: {data}")

import requests, os
from dotenv import load_dotenv
load_dotenv()

def fetch_video_metrics(publish_ids: list) -> list:
    token = os.getenv("TIKTOK_ACCESS_TOKEN")
    headers = {"Authorization": f"Bearer {token}"}
    results = []
    for pid in publish_ids:
        res = requests.get(
            f"https://open.tiktokapis.com/v2/video/query/",
            headers=headers,
            params={"fields": "id,title,view_count,like_count,comment_count,share_count,average_time_watched",
                    "filters": {"video_ids": [pid]}})
        v = res.json()["data"]["videos"][0]
        avg_watch_pct = round((v["average_time_watched"] / 75) * 100, 1)
        perf = ("🔥 Viral" if v["view_count"]>=10000
                else "✅ Good" if v["view_count"]>=2000
                else "⚠️ Low" if v["view_count"]>=500
                else "❌ Poor")
        results.append({
            "publish_id": pid, "title": v["title"][:60],
            "views": v["view_count"], "likes": v["like_count"],
            "avg_watch_pct": avg_watch_pct, "performance": perf})
    return results

import schedule, time

def daily_pipeline():
    print("🚀 Starting Daily Data Governor Pipeline...")
    topic = run_topic_engine()
    script = run_script_workshop(topic)
    audio = run_voiceover(script["text"],
        f"./output/audio/{topic['slug']}.mp3")
    broll = run_visual_sourcing(topic["keywords"])
    draft = run_capcut_assembly(
        topic_title=topic["title"],
        voiceover_path=audio["path"],
        broll_clips=broll["clips"],
        srt_path=audio["srt_path"],
        key_term=topic.get("key_term"),
        key_stat=topic.get("key_stat"))
    print(f"⏸️  HUMAN REVIEW: Open CapCut, review draft, export to ./output/videos/")
    video_path = wait_for_export(
        f"./output/videos/{topic['slug']}.mp4")
    publish_id = run_tiktok_publish(video_path, topic["title"])
    run_analytics_fetch()
    run_feedback_loop()
    print("✅ Pipeline complete!")

schedule.every().day.at("06:00").do(daily_pipeline)

if __name__ == "__main__":
    print("⏰ Scheduler active. Runs at 06:00 AM daily.")
    while True:
        schedule.run_pending()
        time.sleep(60)