📚 Complete Integration Guide

# .env file (create in your project root)
EMOJIHOUSE_API_KEY=your_api_key_here_from_dashboard

# Example: 
# EMOJIHOUSE_API_KEY=eh_live_a1b2c3d4e5f6g7h8i9j0

const analyzeUser = async (sessionData) => {
  try {
    const response = await fetch('https://ads.emojihouse.com/ml/analyze', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        user_id: sessionData.userId,
        session_id: sessionData.sessionId,
        platform: 'web', // or 'mobile_app', 'desktop'
        session_duration: sessionData.duration, // seconds
        interactions: sessionData.interactions, // ['click', 'scroll', 'share']
        content_type: 'social_feed', // or 'product_page', 'article'
        context: 'discovery_mode' // or 'search_result', 'recommendation'
      })
    });

    if (!response.ok) {
      throw new Error(`API Error: ${response.status}`);
    }

    const result = await response.json();
    
    console.log('ML Analysis Result:');
    console.log('Trust Score:', result.emoji_score);
    console.log('Confidence:', result.confidence);
    console.log('Trust Level:', result.trust_level);
    
    return result;
    
  } catch (error) {
    console.error('EmojiHouse API Error:', error);
    throw error;
  }
};

// Example usage:
const sessionData = {
  userId: 'user_12345',
  sessionId: 'sess_' + Date.now(),
  duration: 180,
  interactions: ['scroll', 'click', 'share'],
};

analyzeUser(sessionData)
  .then(result => {
    // Revenue is automatically attributed to your account!
    console.log('Analysis complete, revenue tracking active');
  })
  .catch(error => {
    console.error('Failed:', error);
  });

import requests
import os
from datetime import datetime

def analyze_user_session(session_data):
    # Analyze user session with EmojiHouse ML API
    # Returns ML analysis result and starts revenue tracking
    try:
        # API call to EmojiHouse
        response = requests.post(
            'https://ads.emojihouse.com/ml/analyze',
            headers={
                'Content-Type': 'application/json'
            },
            json={
                'user_id': session_data['user_id'],
                'session_id': session_data['session_id'],
                'platform': session_data.get('platform', 'web'),
                'session_duration': session_data.get('duration', 0),
                'interactions': session_data.get('interactions', []),
                'content_type': session_data.get('content_type', 'general'),
                'context': session_data.get('context', 'discovery_mode')
            },
            timeout=10
        )
        
        response.raise_for_status()  # Raise exception for HTTP errors
        result = response.json()
        
        print(f"ML Analysis Result:")
        print(f"Trust Score: {result['emoji_score']:.3f}")
        print(f"Confidence: {result['confidence']:.3f}")
        print(f"Trust Level: {result['trust_level']}")
        print(f"Processing Time: {result.get('total_processing_ms', 0):.1f}ms")
        
        # Revenue is automatically attributed to your account!
        
        return result
        
    except requests.exceptions.RequestException as e:
        print(f"EmojiHouse API Error: {e}")
        raise
    except Exception as e:
        print(f"Unexpected error: {e}")
        raise

# Example usage in Django view:
def user_activity_view(request):
    session_data = {
        'user_id': f"user_{request.user.id}",
        'session_id': f"sess_{int(datetime.now().timestamp())}",
        'platform': 'web',
        'duration': 180,  # 3 minutes
        'interactions': ['scroll', 'click', 'like'],
        'content_type': 'social_feed',
        'context': 'homepage_browse'
    }
    
    try:
        ml_result = analyze_user_session(session_data)
        
        # Use the ML result in your app logic
        if ml_result['trust_level'] == 'HIGH':
            # Show premium content
            pass
        
        return JsonResponse({
            'success': True,
            'trust_score': ml_result['emoji_score']
        })
        
    except Exception as e:
        return JsonResponse({
            'success': False,
            'error': str(e)
        })

# Example usage in Flask:
from flask import Flask, request, jsonify

@app.route('/analyze-session', methods=['POST'])
def analyze_session():
    data = request.get_json()
    
    try:
        result = analyze_user_session(data)
        return jsonify({
            'success': True,
            'trust_score': result['emoji_score'],
            'confidence': result['confidence']
        })
    except Exception as e:
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

 $sessionData['user_id'],
        'session_id' => $sessionData['session_id'],
        'platform' => $sessionData['platform'] ?? 'web',
        'session_duration' => $sessionData['duration'] ?? 0,
        'interactions' => $sessionData['interactions'] ?? [],
        'content_type' => $sessionData['content_type'] ?? 'general',
        'context' => $sessionData['context'] ?? 'discovery_mode'
    ]);
    
    $ch = curl_init('https://ads.emojihouse.com/ml/analyze');
    
    curl_setopt($ch, CURLOPT_POST, true);
    curl_setopt($ch, CURLOPT_POSTFIELDS, $postData);
    curl_setopt($ch, CURLOPT_HTTPHEADER, [
        'Content-Type: application/json'
    ]);
    curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
    curl_setopt($ch, CURLOPT_TIMEOUT, 10);
    curl_setopt($ch, CURLOPT_FOLLOWLOCATION, true);
    
    $response = curl_exec($ch);
    $httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
    $error = curl_error($ch);
    curl_close($ch);
    
    if ($error) {
        throw new Exception("cURL Error: " . $error);
    }
    
    if ($httpCode !== 200) {
        throw new Exception("API Error: HTTP $httpCode");
    }
    
    $result = json_decode($response, true);
    
    if (!$result) {
        throw new Exception("Invalid JSON response");
    }
    
    echo "ML Analysis Result:\n";
    echo "Trust Score: " . number_format($result['emoji_score'], 3) . "\n";
    echo "Confidence: " . number_format($result['confidence'], 3) . "\n";
    echo "Trust Level: " . $result['trust_level'] . "\n";
    echo "Processing Time: " . ($result['total_processing_ms'] ?? 0) . "ms\n";
    
    // Revenue is automatically attributed to your account!
    
    return $result;
}

// Example usage in Laravel Controller:
class UserActivityController extends Controller {
    public function analyzeSession(Request $request) {
        $sessionData = [
            'user_id' => 'user_' . $request->user()->id,
            'session_id' => 'sess_' . time(),
            'platform' => 'web',
            'duration' => 180,
            'interactions' => ['scroll', 'click', 'share'],
            'content_type' => 'product_page',
            'context' => 'shopping_browse'
        ];
        
        try {
            $result = analyzeUserSession($sessionData);
            
            return response()->json([
                'success' => true,
                'trust_score' => $result['emoji_score'],
                'confidence' => $result['confidence']
            ]);
            
        } catch (Exception $e) {
            return response()->json([
                'success' => false,
                'error' => $e->getMessage()
            ], 500);
        }
    }
}

// Simple PHP usage:
$sessionData = [
    'user_id' => 'user_12345',
    'session_id' => 'sess_' . time(),
    'platform' => 'web',
    'duration' => 120,
    'interactions' => ['click', 'scroll'],
    'content_type' => 'article',
    'context' => 'reading_mode'
];

try {
    $result = analyzeUserSession($sessionData);
    echo "Analysis complete! Revenue tracking is now active.\n";
} catch (Exception $e) {
    echo "Error: " . $e->getMessage() . "\n";
}

?>

# Test with curl (replace with your data)
curl -X POST https://ads.emojihouse.com/ml/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "test_user_123",
    "session_id": "test_session_456",
    "platform": "web",
    "session_duration": 120,
    "interactions": ["click", "scroll"],
    "content_type": "general",
    "context": "testing"
  }'