#!/usr/bin/env python3
"""
Analyze video transcript to identify topics and create chapter divisions.
"""

import json
import re
from datetime import timedelta

def seconds_to_timestamp(seconds):
    """Convert seconds to readable timestamp."""
    td = timedelta(seconds=float(seconds))
    total_seconds = int(td.total_seconds())
    hours, remainder = divmod(total_seconds, 3600)
    minutes, seconds = divmod(remainder, 60)
    return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

def load_transcript(file_path):
    """Load JSON transcript file."""
    with open(file_path, 'r', encoding='utf-8') as f:
        data = json.load(f)
    return data

def extract_transcript_segments(data):
    """Extract all transcript segments with timestamps."""
    segments = []

    # The structure has a 'tracks' key
    if 'tracks' in data[0]:
        for track in data[0]['tracks']:
            if track['kind'] == 'asr':  # Automatic Speech Recognition
                for event in track['events']:
                    start_time = event.get('tStartMs', 0) / 1000
                    duration = event.get('dDurationMs', 0) / 1000

                    # Extract text from segments
                    text_parts = []
                    if 'segs' in event:
                        for seg in event['segs']:
                            if 'utf8' in seg:
                                text_parts.append(seg['utf8'])

                    text = ' '.join(text_parts)
                    if text.strip():
                        segments.append({
                            'start': start_time,
                            'end': start_time + duration,
                            'text': text
                        })

    return segments

def group_by_time_window(segments, window_seconds=600):
    """Group segments into time windows for analysis."""
    groups = []
    current_time = 0

    while current_time < segments[-1]['end']:
        window_end = current_time + window_seconds
        window_segments = [s for s in segments
                          if current_time <= s['start'] < window_end]

        if window_segments:
            combined_text = ' '.join([s['text'] for s in window_segments])
            groups.append({
                'start': current_time,
                'end': window_end,
                'segments': window_segments,
                'text': combined_text
            })

        current_time = window_end

    return groups

def extract_keywords(text):
    """Extract key topics from text."""
    keywords = {
        'Market & Community': ['market', 'pasar', 'grup', 'komunitas', 'telegram', 'facebook', 'forum'],
        'Problem Finding': ['masalah', 'problem', 'kesulitan', 'permasalahan', 'error'],
        'Exploration': ['explor', 'coba', 'trial', 'nyoba', 'eksplor'],
        'Personal Branding': ['branding', 'personal branding', 'show off', 'image'],
        'AIDA/Funnel': ['aida', 'awareness', 'interest', 'desire', 'action', 'funel', 'funnel'],
        'Trust': ['trust', 'percaya', 'kepercayaan'],
        'Clients': ['klien', 'client', 'pelanggan', 'customer'],
        'Pricing': ['harga', 'price', 'bayar', 'budget', 'rp', 'juta', 'ribu'],
        'Negotiation': ['tawar', 'negosiasi', 'deal'],
        'Services': ['jasa', 'service', 'website', 'plugin', 'elementor', 'instal'],
        'Cold/Warm/Hot Market': ['cold', 'warm', 'hot', 'dingin', 'hangat'],
        'Network': ['network', 'jaringan', 'koneksi', 'hubungan'],
        'Sharing': ['sharing', 'share', 'bagi'],
        'Products': ['produk', 'product', 'template'],
    }

    found = []
    text_lower = text.lower()

    for topic, kw_list in keywords.items():
        count = sum(1 for kw in kw_list if kw.lower() in text_lower)
        if count > 0:
            found.append((topic, count))

    return sorted(found, key=lambda x: x[1], reverse=True)

def identify_main_topics():
    """Identify main topics throughout the video."""
    file_path = "/Users/dwindown/CascadeProjects/MeetDwindiCom/access-hub/Live Zoom - Diskusi Cara Jual Jasa via Online.json"

    print("Loading transcript...")
    data = load_transcript(file_path)

    print("Extracting segments...")
    segments = extract_transcript_segments(data)

    print(f"Total segments: {len(segments)}")

    if not segments:
        print("No segments found!")
        return

    total_duration = segments[-1]['end']
    print(f"Total duration: {seconds_to_timestamp(total_duration)} ({total_duration/60:.1f} minutes)")

    print("\n" + "="*80)
    print("ANALYZING CONTENT IN 10-MINUTE INTERVALS")
    print("="*80 + "\n")

    # Group by 10-minute windows
    groups = group_by_time_window(segments, window_seconds=600)

    for i, group in enumerate(groups, 1):
        print(f"\n{'='*80}")
        print(f"SECTION {i}: {seconds_to_timestamp(group['start'])} - {seconds_to_timestamp(group['end'])}")
        print(f"{'='*80}")

        # Get first 500 chars for preview
        preview = group['text'][:500]
        print(f"\nContent Preview:\n{preview}...")

        # Extract keywords
        keywords = extract_keywords(group['text'])
        if keywords:
            print(f"\nMain Topics:")
            for topic, count in keywords[:5]:
                print(f"  - {topic}: {count} mentions")

    print("\n" + "="*80)
    print("DETAILED BREAKDOWN (5-minute intervals for first hour)")
    print("="*80 + "\n")

    # More detailed for first hour
    detailed_groups = group_by_time_window(segments[:int(len(segments)*0.4)], window_seconds=300)

    for i, group in enumerate(detailed_groups, 1):
        print(f"\n--- {seconds_to_timestamp(group['start'])} - {seconds_to_timestamp(group['end'])} ---")

        # Get text summary
        text_summary = group['text'][:300]
        print(f"{text_summary}...")

if __name__ == "__main__":
    identify_main_topics()