import requests
import json
from datetime import datetime, timedelta

class GoogleAIPatternAnalyzer:
    """
    Simulates how Google's AI analyzes personal patterns from your data
    This demonstrates the type of pattern recognition described in the article
    """
    
    def __init__(self, user_id):
        self.user_id = user_id
        self.patterns = {
            'habit_failures': [],
            'emotional_patterns': [],
            'predictive_events': []
        }
    
    def analyze_search_history(self, search_data):
        """Analyzes search patterns for emotional states and life events"""
        emotional_keywords = ['anxiety', 'depressed', 'stressed', 'tired', 'worried']
        life_event_keywords = ['pregnant', 'divorce', 'job', 'moving', 'diet']
        
        for search in search_data:
            query = search['query'].lower()
            timestamp = search['timestamp']
            
            # Detect emotional patterns
            for emotion in emotional_keywords:
                if emotion in query:
                    self.patterns['emotional_patterns'].append({
                        'emotion': emotion,
                        'timestamp': timestamp,
                        'query': query
                    })
            
            # Detect life events
            for event in life_event_keywords:
                if event in query:
                    self.patterns['predictive_events'].append({
                        'event': event,
                        'timestamp': timestamp,
                        'confidence': 0.85  # AI confidence score
                    })
        
        return self.patterns
    
    def predict_failure_patterns(self, calendar_events):
        """Predicts when users are likely to abandon habits"""
        # Example: Gym membership cancellations around Jan 15th
        january_cancellations = [
            event for event in calendar_events 
            if 'cancel' in event['title'].lower() 
            and event['date'].month == 1
            and 10 <= event['date'].day <= 20
        ]
        
        if january_cancellations:
            self.patterns['habit_failures'].append({
                'pattern': 'January habit abandonment',
                'next_predicted_date': datetime(2024, 1, 15),
                'confidence': 0.92
            })
        
        return self.patterns

# Usage example:
analyzer = GoogleAIPatternAnalyzer('user_123')
# analyzer.analyze_search_history(your_search_data)
# analyzer.predict_failure_patterns(your_calendar_events)

The 'Personalization' That Knows Too Much

Remember when personalization meant Amazon recommending books based on what you'd bought? Those were simpler times. Now, Google's AI can apparently predict your life choices based on the 3:47 AM search for 'do I have anxiety or am I just tired' you made in 2017. The company's pitch is simple: 'We know you better than you know yourself, so let us help!' It's like having a stalker who also manages your calendar.

The irony is delicious. For years, Google's privacy policy has been longer than the U.S. Constitution and about as readable. Now they're saying, 'Hey, remember all that data we said we weren't really using? Turns out we were using it to build a digital clone of you. Surprise!'

Your Digital Shadow Knows You're Going to Fail

Here's what Google's AI knows about you that you probably don't: your patterns of failure. That gym membership you cancel every January 15th? Noted. The diet that lasts exactly 11 days? Documented. The career change you research every quarter but never pursue? Filed under 'recurring delusions.'

Their AI doesn't just know what you want—it knows what you'll actually do. And more importantly, what you won't. It's like having a brutally honest friend who's been keeping score for decades. 'Based on your historical data, you have a 92% chance of abandoning this New Year's resolution by February. Would you like me to schedule the disappointment now?'

The Surveillance-Service Spectrum

Google wants us to believe there's a clear line between 'helpful AI' and 'creepy surveillance.' Spoiler alert: there isn't. It's more of a gradient, like:

• Level 1: 'Here's traffic to work' (Helpful!)
• Level 2: 'You're running late, I've emailed your boss' (Convenient!)
• Level 3: 'Your stress levels suggest you should quit your job. Here are divorce lawyers in your area' (Wait, what?)
• Level 4: 'Based on your search history and biometric data, you're having an existential crisis. Would you like to purchase antidepressants?' (Okay, now this is just rude.)

The problem isn't that Google knows things about us—it's that they know everything. Your search for 'unusual rash' at 2 AM? They know. Your YouTube binge of 'failed proposal' videos? Noted. Your Maps history showing you parked outside your ex's house for 45 minutes? Yeah, they have thoughts about that.

The 'Helpful' That Feels Like Judgment

Google's AI promises to be uniquely helpful because it 'understands context.' Translation: it remembers that time in 2015 when you got really into conspiracy theories about birds not being real. It knows about your brief but passionate affair with cryptocurrency. It remembers your 'minimalist lifestyle' phase that lasted exactly as long as it took to realize you'd have to get rid of your stuff.

This creates AI interactions that feel less like assistance and more like therapy sessions with a judgey robot. 'I see you're searching for productivity tips again. This is the 47th time this year. Perhaps the problem isn't your tools, but your complete lack of discipline?' Thanks, Google. Really helpful.

The Privacy Paradox We Pretend Doesn't Exist

Tech companies love to talk about the 'privacy-value exchange.' You give us your data, we give you free services! It's a fair trade! Except nobody ever actually agreed to this deal—it just sort of happened while we were trying to figure out how to use Google Docs.

Now, with AI, the exchange has become: 'You give us your entire digital life, and we'll... make our ads slightly more relevant?' The math isn't mathing. It's like trading your house for a slightly better toaster.

And let's be honest—we're all complicit. We complain about privacy while happily using services that track our every move. We use incognito mode like it actually does something (it doesn't). We accept cookies without reading the policies (who has time?). We're digital exhibitionists complaining that someone's watching.

The Future: Personalized Dystopia

Where does this lead? To AI systems that don't just know your preferences, but your vulnerabilities. Systems that can predict when you're most likely to make impulse purchases (3 PM on Tuesdays, apparently). Systems that know which emotional triggers will get you to click, buy, or share.

Google's AI advantage isn't technological—it's psychological. They've been running the world's largest behavioral experiment for 25 years, and now they're applying what they've learned. The result is AI that doesn't just answer your questions—it anticipates your needs, manipulates your choices, and monetizes your weaknesses.

But hey, at least it can recommend a good restaurant.