Cómo monitorear reseñas de aplicaciones a través de una API de búsqueda

import os, requests, re, json, datetime

API_KEY = os.environ['SCAVIO_API_KEY']

def search_app_reviews(app_name: str) -> list:
    queries = [
        f'{app_name} app reviews 2026',
        f'{app_name} app store rating',
        f'{app_name} user reviews',
    ]
    all_results = []
    for query in queries:
        resp = requests.post('https://api.scavio.dev/api/v1/search',
            headers={'x-api-key': API_KEY},
            json={'platform': 'google', 'query': query}, timeout=15)
        results = resp.json().get('organic_results', [])
        all_results.extend(results[:3])
    return all_results

reviews = search_app_reviews('Slack')
print(f'Found {len(reviews)} review-related results')
for r in reviews[:3]:
    print(f"  {r.get('title', '')[:60]}")

def extract_ratings(results: list) -> dict:
    ratings = []
    for r in results:
        text = (r.get('title', '') + ' ' + r.get('snippet', '')).lower()
        # Look for rating patterns like "4.5/5", "4.5 stars", "rated 4.5"
        rating_matches = re.findall(r'(\d+\.?\d*)\s*(?:/5|stars?|out of 5|rating)', text)
        for match in rating_matches:
            try:
                val = float(match)
                if 1 <= val <= 5:
                    ratings.append(val)
            except ValueError:
                pass
        # Look for review counts like "10K reviews", "5,000 reviews"
        count_matches = re.findall(r'([\d,]+[KkMm]?)\s*reviews?', text)
        if count_matches:
            pass  # Store for tracking
    avg_rating = round(sum(ratings) / len(ratings), 1) if ratings else 0
    return {
        'ratings_found': len(ratings),
        'average': avg_rating,
        'min': min(ratings) if ratings else 0,
        'max': max(ratings) if ratings else 0,
    }

rating_data = extract_ratings(reviews)
print(f"Average rating: {rating_data['average']} (from {rating_data['ratings_found']} mentions)")

POSITIVE = ['love', 'great', 'excellent', 'best', 'amazing', 'fast', 'easy', 'reliable', 'intuitive']
NEGATIVE = ['slow', 'buggy', 'crash', 'expensive', 'terrible', 'worst', 'broken', 'frustrating', 'unusable']

def analyze_sentiment(results: list) -> dict:
    positive_count = 0
    negative_count = 0
    positive_snippets = []
    negative_snippets = []
    for r in results:
        text = r.get('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:
            positive_count += 1
            positive_snippets.append(r.get('snippet', '')[:100])
        elif neg > pos:
            negative_count += 1
            negative_snippets.append(r.get('snippet', '')[:100])
    total = positive_count + negative_count
    return {
        'positive': positive_count,
        'negative': negative_count,
        'sentiment_ratio': round(positive_count / total, 2) if total > 0 else 0,
        'top_positive': positive_snippets[:2],
        'top_negative': negative_snippets[:2],
    }

sentiment = analyze_sentiment(reviews)
print(f"Sentiment: {sentiment['positive']} positive, {sentiment['negative']} negative")
print(f"Ratio: {sentiment['sentiment_ratio']}")

HISTORY_FILE = 'app_review_history.json'

def store_review_data(app: str, rating_data: dict, sentiment: dict):
    history = []
    try:
        with open(HISTORY_FILE) as f:
            history = json.load(f)
    except FileNotFoundError:
        pass
    entry = {
        'app': app,
        'date': datetime.date.today().isoformat(),
        'rating': rating_data,
        'sentiment': sentiment,
    }
    history.append(entry)
    with open(HISTORY_FILE, 'w') as f:
        json.dump(history, f, indent=2)
    return entry

def detect_changes(app: str) -> dict:
    try:
        with open(HISTORY_FILE) as f:
            history = json.load(f)
    except FileNotFoundError:
        return {'change': 'no history'}
    entries = [h for h in history if h['app'] == app]
    if len(entries) < 2:
        return {'change': 'insufficient data'}
    prev = entries[-2]
    curr = entries[-1]
    rating_change = curr['rating']['average'] - prev['rating']['average']
    sentiment_change = curr['sentiment']['sentiment_ratio'] - prev['sentiment']['sentiment_ratio']
    return {
        'rating_change': round(rating_change, 1),
        'sentiment_change': round(sentiment_change, 2),
        'alert': abs(rating_change) > 0.3 or abs(sentiment_change) > 0.2,
    }

store_review_data('Slack', rating_data, sentiment)
changes = detect_changes('Slack')
print(f"Changes: {changes}")