import random
import datetime
import matplotlib.pyplot as plt
from collections import Counter
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
import io
from wordcloud import WordCloud

# Mock setup for NLTK (normally requires download)
nltk.download('vader_lexicon', quiet=True)

# Simulated X user data
class XUserAnalyzer:
    def __init__(self, username):
        self.username = username
        self.posts = self._generate_mock_posts()
        self.sentiment_analyzer = SentimentIntensityAnalyzer()

    def _generate_mock_posts(self):
        topics = ["AI", "space", "tech", "xAI", "future", "memes"]
        times = [datetime.datetime.now() - datetime.timedelta(hours=i) for i in range(50)]
        return [{"text": f"{random.choice(topics)} {random.choice(['rules', 'is dope', 'will save us'])}!",
                 "timestamp": t, "likes": random.randint(1, 1000)} for t in times]

    def analyze_posting_patterns(self):
        hours = [post["timestamp"].hour for post in self.posts]
        hour_counts = Counter(hours)
        return hour_counts

    def sentiment_analysis(self):
        sentiments = [self.sentiment_analyzer.polarity_scores(post["text"])["compound"] 
                     for post in self.posts]
        avg_sentiment = sum(sentiments) / len(sentiments)
        return avg_sentiment

    def top_words(self):
        words = " ".join(post["text"] for post in self.posts).split()
        word_counts = Counter(words)
        return word_counts.most_common(5)

    def generate_summary(self):
        hour_counts = self.analyze_posting_patterns()
        sentiment = self.sentiment_analysis()
        top_words = self.top_words()
        
        peak_hour = max(hour_counts, key=hour_counts.get)
        sentiment_desc = "positive" if sentiment > 0.1 else "neutral" if sentiment > -0.1 else "negative"
        
        summary = (f"Analysis for @{self.username}:\n"
                  f"- Most active hour: {peak_hour}:00 ({hour_counts[peak_hour]} posts)\n"
                  f"- Average sentiment: {sentiment:.2f} ({sentiment_desc})\n"
                  f"- Top words: {', '.join(f'{w} ({c})' for w, c in top_words)}")
        return summary

    def plot_activity(self):
        hour_counts = self.analyze_posting_patterns()
        plt.figure(figsize=(10, 6))
        plt.bar(hour_counts.keys(), hour_counts.values(), color='skyblue')
        plt.title(f"@{self.username}'s Posting Activity by Hour")
        plt.xlabel("Hour of Day")
        plt.ylabel("Number of Posts")
        plt.savefig("activity_plot.png")
        plt.close()

    def generate_wordcloud(self):
        text = " ".join(post["text"] for post in self.posts)
        wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
        wordcloud.to_file("wordcloud.png")

# Main app
def run_grok3_demo(username="GrokFan42"):
    print("Initializing Grok 3 X User Analysis Demo...")
    analyzer = XUserAnalyzer(username)
    
    # Generate and display summary
    summary = analyzer.generate_summary()
    print("\nUser Summary:")
    print(summary)
    
    # Generate visualizations
    analyzer.plot_activity()
    analyzer.generate_wordcloud()
    
    print("\nVisualizations generated:")
    print("- activity_plot.png: Bar chart of posting activity by hour")
    print("- wordcloud.png: Word cloud of most frequent words")
    
    # Simulated advanced insight
    sentiment = analyzer.sentiment_analysis()
    if sentiment > 0.3:
        print("\nGrok 3 Insight: User exhibits strong positive engagement with tech topics!")
    elif sentiment < -0.1:
        print("\nGrok 3 Insight: User may be critical or skeptical of discussed topics.")
    else:
        print("\nGrok 3 Insight: User maintains a balanced tone in their posts.")

if __name__ == "__main__":
    run_grok3_demo()