Cómo crear un monitor de marca multiplataforma

import os, requests, json
from datetime import datetime
from collections import Counter

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}

BRAND = 'Scavio'

def search_platform(query, platform=None):
    body = {'query': query, 'country_code': 'us'}
    if platform:
        body['platform'] = platform
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json=body).json()
    return [{'title': r.get('title', ''), 'link': r.get('link', ''),
             'snippet': r.get('snippet', ''), 'platform': platform or 'google'}
            for r in data.get('organic_results', [])]

def search_tiktok(query):
    data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
        headers=TH, json={'query': query}).json()
    videos = data.get('videos', data.get('data', {}).get('videos', []))
    return [{'title': v.get('desc', '')[:80], 'link': f'tiktok.com/@{v.get("author", {}).get("uniqueId", "")}',
             'snippet': v.get('desc', ''), 'platform': 'tiktok',
             'plays': v.get('stats', {}).get('playCount', 0)} for v in videos]

all_mentions = []
for platform in [None, 'reddit', 'youtube']:
    mentions = search_platform(BRAND, platform)
    all_mentions.extend(mentions)
    print(f'{platform or "google":10}: {len(mentions)} mentions')
tiktok_mentions = search_tiktok(BRAND)
all_mentions.extend(tiktok_mentions)
print(f'{"tiktok":10}: {len(tiktok_mentions)} mentions')
print(f'\nTotal: {len(all_mentions)} mentions. Cost: $0.020')

POSITIVE = ['best', 'great', 'love', 'recommend', 'amazing', 'excellent', 'perfect']
NEGATIVE = ['worst', 'terrible', 'avoid', 'hate', 'broken', 'expensive', 'scam']

def score_mention(mention):
    text = f'{mention["title"]} {mention["snippet"]}'.lower()
    pos = sum(1 for w in POSITIVE if w in text)
    neg = sum(1 for w in NEGATIVE if w in text)
    if pos > neg: return 'positive'
    if neg > pos: return 'negative'
    return 'neutral'

def platform_report(mentions):
    by_platform = {}
    for m in mentions:
        p = m['platform']
        if p not in by_platform:
            by_platform[p] = []
        by_platform[p].append(m)
    print(f'\n=== Cross-Platform Brand Report - {BRAND} ===')
    for platform, items in by_platform.items():
        sentiments = Counter(score_mention(m) for m in items)
        print(f'\n  [{platform.upper()}] {len(items)} mentions')
        print(f'    Positive: {sentiments["positive"]} | Neutral: {sentiments["neutral"]} | Negative: {sentiments["negative"]}')
        for item in items[:2]:
            print(f'    - {item["title"][:55]}')

platform_report(all_mentions)

def daily_digest(mentions):
    total = len(mentions)
    sentiments = Counter(score_mention(m) for m in mentions)
    platforms = Counter(m['platform'] for m in mentions)
    print(f'\n=== Daily Brand Digest - {datetime.now().strftime("%Y-%m-%d")} ===')
    print(f'  Brand: {BRAND}')
    print(f'  Total mentions: {total}')
    print(f'  Sentiment: +{sentiments["positive"]} neutral:{sentiments["neutral"]} -{sentiments["negative"]}')
    print(f'  Platforms: {", ".join(f"{p}({c})" for p, c in platforms.most_common())}')
    # Highlight negative mentions that need attention
    negative = [m for m in mentions if score_mention(m) == 'negative']
    if negative:
        print(f'\n  NEEDS ATTENTION ({len(negative)} negative mentions):')
        for m in negative[:3]:
            print(f'    [{m["platform"]}] {m["title"][:50]}')
            print(f'      {m["link"][:60]}')
    # Highlight high-reach mentions
    tiktok_high = [m for m in mentions if m.get('plays', 0) > 10000]
    if tiktok_high:
        print(f'\n  HIGH REACH TikTok ({len(tiktok_high)} videos >10K plays):')
        for m in tiktok_high[:3]:
            print(f'    {m["plays"]:,} plays: {m["title"][:40]}')
    print(f'\n  Daily cost: $0.020 (4 platform searches)')

daily_digest(all_mentions)