mirror of
https://github.com/nethunterzist/trendyol-analiz
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c7be57064b
- FastAPI backend with Python - React + Vite admin panel - PostgreSQL database - Trendyol marketplace analytics - GitHub Actions CI/CD workflow Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
264 lines
8.3 KiB
Python
264 lines
8.3 KiB
Python
#!/usr/bin/env python3
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"""
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Google Trends Helper Module
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Provides Google search trend data for traffic source estimation
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"""
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from pytrends.request import TrendReq
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from typing import Dict, Optional
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from datetime import datetime, timedelta
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import time
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class GoogleTrendsCache:
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"""Simple cache for Google Trends API calls"""
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def __init__(self, cache_hours: int = 6):
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self.cache = {}
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self.cache_hours = cache_hours
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def get(self, key: str) -> Optional[Dict]:
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"""Get cached data if not expired"""
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if key in self.cache:
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data, timestamp = self.cache[key]
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if datetime.now() - timestamp < timedelta(hours=self.cache_hours):
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return data
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return None
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def set(self, key: str, data: Dict):
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"""Store data in cache"""
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self.cache[key] = (data, datetime.now())
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def clear(self):
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"""Clear all cache"""
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self.cache = {}
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# Global cache instance
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trends_cache = GoogleTrendsCache(cache_hours=6)
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def fetch_google_trends(product_name: str, retries: int = 3) -> Dict:
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"""
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Fetch Google Trends data for a product
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Args:
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product_name: Product name to search
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retries: Number of retry attempts
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Returns:
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Dict with search_volume, trend, status
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"""
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# Check cache first
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cache_key = f"trends_{product_name.lower()}"
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cached_data = trends_cache.get(cache_key)
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if cached_data:
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cached_data['from_cache'] = True
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return cached_data
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# Try to fetch from Google Trends
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for attempt in range(retries):
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try:
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# Initialize pytrends
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pytrends = TrendReq(
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hl='tr-TR', # Turkish
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tz=180, # GMT+3
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timeout=(10, 30) # Connection timeout, read timeout
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)
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# Build payload - last 3 months in Turkey
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pytrends.build_payload(
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[product_name],
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timeframe='today 3-m',
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geo='TR'
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)
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# Get interest over time
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interest_df = pytrends.interest_over_time()
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# No data available
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if interest_df.empty or product_name not in interest_df.columns:
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result = {
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'search_volume': 0,
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'trend': 'no_data',
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'recent_avg': 0,
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'previous_avg': 0,
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'status': 'no_data',
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'from_cache': False
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}
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trends_cache.set(cache_key, result)
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return result
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# Calculate total interest score (sum of weekly scores)
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total_interest = int(interest_df[product_name].sum())
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# Trend analysis: last 4 weeks vs previous 4 weeks
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recent_avg = float(interest_df[product_name][-4:].mean())
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previous_avg = float(interest_df[product_name][-8:-4].mean())
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# Determine trend direction
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if previous_avg == 0:
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trend = 'stable'
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elif recent_avg > previous_avg * 1.2:
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trend = 'rising'
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elif recent_avg < previous_avg * 0.8:
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trend = 'falling'
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else:
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trend = 'stable'
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# Prepare timeseries data for chart
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timeseries = []
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if not interest_df.empty:
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# Reset index to get date as a column
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df_reset = interest_df.reset_index()
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for _, row in df_reset.iterrows():
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timeseries.append({
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'date': row['date'].strftime('%Y-%m-%d'),
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'value': int(row[product_name])
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})
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result = {
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'search_volume': total_interest,
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'trend': trend,
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'recent_avg': round(recent_avg, 2),
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'previous_avg': round(previous_avg, 2),
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'timeseries': timeseries,
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'status': 'success',
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'from_cache': False
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}
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# Cache the result
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trends_cache.set(cache_key, result)
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return result
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except Exception as e:
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error_msg = str(e)
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print(f"Google Trends API Error (attempt {attempt + 1}/{retries}): {error_msg}")
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# Rate limit error - wait longer
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if '429' in error_msg or 'rate' in error_msg.lower():
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wait_time = 5 * (attempt + 1) # 5, 10, 15 seconds
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print(f"Rate limited. Waiting {wait_time} seconds...")
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time.sleep(wait_time)
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continue
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# Other errors - retry with exponential backoff
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if attempt < retries - 1:
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wait_time = 2 ** attempt # 1, 2, 4 seconds
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time.sleep(wait_time)
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continue
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# All retries failed
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result = {
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'search_volume': 0,
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'trend': 'unknown',
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'status': 'error',
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'error': error_msg,
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'from_cache': False
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}
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return result
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# Should not reach here, but just in case
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return {
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'search_volume': 0,
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'trend': 'unknown',
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'status': 'error',
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'error': 'Max retries exceeded',
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'from_cache': False
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}
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def estimate_traffic_sources(
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product_name: str,
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instagram_views: int = 0,
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tiktok_views: int = 0,
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twitter_shares: int = 0
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) -> Dict:
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"""
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Get Google Trends search volume for a product
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NOTE: Only uses Google Trends data, social media parameters are ignored
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Args:
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product_name: Product name for Google Trends lookup
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instagram_views: Ignored (kept for API compatibility)
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tiktok_views: Ignored (kept for API compatibility)
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twitter_shares: Ignored (kept for API compatibility)
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Returns:
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Dict with Google Trends search volume and trend
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"""
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# Get Google Trends data
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trends_data = fetch_google_trends(product_name)
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google_score = trends_data['search_volume']
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# No data available
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if google_score == 0 or trends_data['status'] != 'success':
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return {
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'sources': {
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'Google': 0
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},
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'raw_scores': {
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'google': 0,
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'total': 0
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},
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'google_trend': trends_data.get('trend', 'unknown'),
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'method': 'google_trends_only',
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'disclaimer': 'Google Trends verisi bulunamadı',
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'from_cache': trends_data.get('from_cache', False)
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}
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# Only show Google search volume (100%)
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sources = {
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'Google': 100.0
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}
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return {
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'sources': sources,
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'raw_scores': {
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'google': google_score,
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'total': google_score
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},
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'google_trend': trends_data['trend'],
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'recent_avg': trends_data.get('recent_avg', 0),
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'previous_avg': trends_data.get('previous_avg', 0),
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'timeseries': trends_data.get('timeseries', []),
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'method': 'google_trends_only',
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'disclaimer': 'Google Trends arama hacmi verisi',
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'from_cache': trends_data.get('from_cache', False)
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}
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# Test function
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if __name__ == "__main__":
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# Test with a sample product
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test_product = "Casio Edifice Kol Saati"
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print(f"Testing Google Trends for: {test_product}")
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print("=" * 80)
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# Test fetch_google_trends
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trends_data = fetch_google_trends(test_product)
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print("\n📊 Google Trends Data:")
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print(f" Search Volume: {trends_data['search_volume']}")
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print(f" Trend: {trends_data['trend']}")
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print(f" Status: {trends_data['status']}")
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print(f" From Cache: {trends_data.get('from_cache', False)}")
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# Test estimate_traffic_sources
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print("\n🎯 Google Trends Analysis:")
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traffic_estimate = estimate_traffic_sources(product_name=test_product)
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print(f"\n Search Volume: {traffic_estimate['raw_scores']['google']}")
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print(f" Google Trend: {traffic_estimate['google_trend']}")
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print(f" Recent Average: {traffic_estimate.get('recent_avg', 'N/A')}")
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print(f" Previous Average: {traffic_estimate.get('previous_avg', 'N/A')}")
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print(f" Method: {traffic_estimate['method']}")
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print(f" From Cache: {traffic_estimate.get('from_cache', False)}")
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print(f" Disclaimer: {traffic_estimate['disclaimer']}")
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print("\n" + "=" * 80)
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print("✅ Test completed!")
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