import random
from datetime import datetime

class SentimentAnalyzer:
    def __init__(self):
        self.sentiment_sources = [
            "Federal Reserve hints at rate pause - positive for gold",
            "Inflation data higher than expected - gold demand rising",
            "Dollar strength weighs on precious metals",
            "Central banks continue gold accumulation",
            "Geopolitical tensions support safe-haven demand",
            "Gold ETFs see outflows amid risk-on sentiment",
            "Technical breakout above resistance level",
            "Profit-taking observed after recent rally"
        ]
    
    def analyze_gold_sentiment(self):
        """Analyze sentiment for gold market"""
        try:
            # Simulate sentiment analysis
            # In production, would use actual news API and NLP model
            
            # Generate random sentiment around current market conditions
            base_sentiment = random.uniform(-0.5, 0.5)
            
            # Add some realistic variation
            if random.random() > 0.7:
                # Strong sentiment event
                sentiment = base_sentiment + random.uniform(-0.5, 0.5)
            else:
                sentiment = base_sentiment
            
            # Clamp between -1 and 1
            sentiment = max(-1, min(1, sentiment))
            
            # Generate news summary
            num_news = random.randint(3, 6)
            selected_news = random.sample(self.sentiment_sources, num_news)
            
            news_html = "<div style='max-height: 300px; overflow-y: auto;'>"
            news_html += "<h4 style='color: #FFD700;'>Latest Gold News</h4>"
            
            for i, news in enumerate(selected_news, 1):
                sentiment_label = "🟢" if "positive" in news or "rising" in news or "support" in news else \
                                  "🔴" if "weighs" in news or "outflows" in news or "Profit-taking" in news else \
                                  "🟡"
                news_html += f"<p style='margin: 10px 0; padding: 10px; background: rgba(255,255,255,0.05); border-radius: 5px;'>{sentiment_label} {news}</p>"
            
            news_html += "</div>"
            
            return sentiment, news_html
            
        except Exception as e:
            return 0, f"<p>Error analyzing sentiment: {str(e)}</p>"