| from flask import Flask, request, jsonify | |
| # from pytrends.request import TrendReq # Uncomment and install if using real Google Trends | |
| import random | |
| import os | |
| app = Flask(__name__) | |
| # --- IMPORTANT: API KEY MANAGEMENT --- | |
| # For real external APIs (Google Trends, Twitter, etc.), you MUST store API keys | |
| # securely as environment variables in your Hugging Face Space settings. | |
| # Do NOT hardcode them here in production. | |
| # Example: | |
| # GOOGLE_TRENDS_API_KEY = os.environ.get("GOOGLE_TRENDS_API_KEY") | |
| # TWITTER_BEARER_TOKEN = os.environ.get("TWITTER_BEARER_TOKEN") | |
| def get_trend_popularity(): | |
| """ | |
| Conceptual backend endpoint for real-world trend data integration. | |
| This function simulates fetching data from external APIs and calculating scores. | |
| """ | |
| data = request.get_json() | |
| trend_name = data.get('trendName', '') | |
| trend_description = data.get('trendDescription', '') | |
| if not trend_name: | |
| return jsonify({"error": "Trend name is required"}), 400 | |
| # --- REAL-WORLD EXTERNAL API CALLS (CONCEPTUAL) --- | |
| # In a real application, you would replace the simulated logic below | |
| # with actual calls to external APIs. | |
| # Example conceptual integration with Google Trends (requires pytrends library) | |
| # try: | |
| # pytrends_client = TrendReq(hl='en-US', tz=360) | |
| # keywords = [trend_name] | |
| # if trend_description: | |
| # keywords.append(trend_description.split(' ')[0]) # Use first word of desc as another keyword | |
| # pytrends_client.build_payload(keywords, cat=0, timeframe='today 3-m', geo='', gprop='') | |
| # trend_data = pytrends_client.interest_over_time() | |
| # if not trend_data.empty and trend_name in trend_data.columns: | |
| # search_interest = trend_data[trend_name].iloc[-1] # Get latest interest score | |
| # else: | |
| # search_interest = random.randint(1, 20) # Fallback if no data | |
| # except Exception as e: | |
| # print(f"Error fetching Google Trends data: {e}") | |
| # search_interest = random.randint(1, 20) # Fallback on error | |
| # Example conceptual integration with Social Media API (e.g., Twitter) | |
| # try: | |
| # # Use a library like `tweepy` or `python-twitter` | |
| # # response = twitter_api.search_recent_tweets(query=trend_name, tweet_fields=["public_metrics"]) | |
| # # social_mentions_count = response.meta.get('total_tweet_count', 0) | |
| # social_mentions_count = random.randint(50, 500) # Simulated | |
| # except Exception as e: | |
| # print(f"Error fetching Twitter data: {e}") | |
| # social_mentions_count = random.randint(10, 100) # Fallback | |
| # --- SIMULATED CALCULATION FOR PROTOTYPE --- | |
| # This part remains for the prototype to provide dynamic, but simulated, updates. | |
| # In a real scenario, these values would be derived from the actual API data. | |
| # Simulate a search popularity increase (e.g., 1-5 points) | |
| simulated_search_increase = random.randint(1, 5) | |
| # Simulate a small engagement boost (e.g., 0-3 points) | |
| simulated_engagement_boost = random.randint(0, 3) | |
| # Simulate a small positive sentiment boost (e.g., 0-2 points) | |
| simulated_sentiment_boost = random.randint(0, 2) | |
| # In a real system, you'd calculate these based on `search_interest`, `social_mentions_count`, etc. | |
| # For example: | |
| # search_popularity_increase = int(search_interest / 10) + random.randint(0,2) | |
| # engagement_boost = int(social_mentions_count / 100) + random.randint(0,1) | |
| # sentiment_boost = 1 if some_sentiment_analysis_is_positive else 0 | |
| return jsonify({ | |
| "newSearchPopularityIncrease": simulated_search_increase, | |
| "engagementBoost": simulated_engagement_boost, | |
| "sentimentBoost": simulated_sentiment_boost | |
| }), 200 | |
| if __name__ == '__main__': | |
| # This is for local development. Hugging Face Spaces will run your app | |
| # using their own server (e.g., Gunicorn/Uvicorn). | |
| app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 7860))) | |