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)))