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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")
@app.route('/api/get-trend-popularity', methods=['POST'])
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)))
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