App Attribution

import requests

# Register your application
response = requests.post(
    "https://api.parrotrouter.com/v1/apps",
    headers={
        "Authorization": "Bearer your-api-key",
        "Content-Type": "application/json"
    },
    json={
        "name": "AI Writing Assistant",
        "description": "Professional writing assistant powered by AI",
        "category": "productivity",
        "website": "https://writingassistant.ai",
        "logo_url": "https://writingassistant.ai/logo.png",
        "metadata": {
            "platform": ["web", "ios", "android"],
            "launch_date": "2024-01-15",
            "team_size": 5,
            "target_audience": "business_professionals"
        },
        "features": [
            "content_generation",
            "grammar_checking",
            "style_improvement",
            "translation"
        ],
        "models_used": [
            "gpt-4-turbo-preview",
            "gpt-3.5-turbo",
            "claude-3-opus"
        ],
        "revenue_share": {
            "enabled": True,
            "tier": "standard",  # standard: 10%, premium: 15%, enterprise: 20%
            "payout_method": "monthly_credit"  # or "bank_transfer"
        },
        "attribution": {
            "public_profile": True,
            "show_in_marketplace": True,
            "allow_reviews": True
        }
    }
)

app_data = response.json()
print(f"App ID: {app_data['app_id']}")
print(f"App Key: {app_data['app_key']}")
print(f"Dashboard URL: {app_data['dashboard_url']}")

# Verify app ownership
verification = requests.post(
    f"https://api.parrotrouter.com/v1/apps/{app_data['app_id']}/verify",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "method": "dns",  # or "file", "meta_tag"
        "record": f"parrotrouter-verify={app_data['verification_code']}"
    }
)

print(f"Verification status: {verification.json()['status']}")

# Get app analytics
analytics = requests.get(
    f"https://api.parrotrouter.com/v1/apps/{app_id}/analytics",
    headers={"Authorization": "Bearer your-api-key"},
    params={
        "period": "30d",
        "granularity": "day"
    }
).json()

print(f"Total requests: {analytics['total_requests']}")
print(f"Unique users: {analytics['unique_users']}")
print(f"Total cost: $" + str(analytics['total_cost']))
print(f"Revenue share earned: $" + str(analytics['revenue_share']))

# Model usage breakdown
print("\nModel Usage:")
for model in analytics['models']:
    print(f"  {model['name']}:")
    print(f"    Requests: {model['requests']}")
    print(f"    Cost: $" + str(model['cost']))
    print(f"    Avg tokens: {model['avg_tokens']}")

# Feature usage
print("\nFeature Usage:")
for feature in analytics['features']:
    print(f"  {feature['name']}: {feature['usage_count']} uses")

# User segments
print("\nUser Segments:")
for segment in analytics['segments']:
    print(f"  {segment['name']}: {segment['users']} users ($" + str(segment['revenue']) + ")")

# Geographic distribution
print("\nGeographic Distribution:")
for country in analytics['countries'][:5]:
    print(f"  {country['name']}: {country['percentage']}%")

# Export detailed reports
report = requests.post(
    f"https://api.parrotrouter.com/v1/apps/{app_id}/reports",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "type": "detailed_usage",
        "period": "last_month",
        "format": "csv",  # or "json", "excel"
        "include": [
            "user_cohorts",
            "feature_adoption",
            "model_performance",
            "cost_analysis"
        ]
    }
)

# Update marketplace listing
listing = requests.put(
    f"https://api.parrotrouter.com/v1/apps/{app_id}/marketplace",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "listing": {
            "title": "AI Writing Assistant Pro",
            "tagline": "Write better content 10x faster with AI",
            "description": "Our AI Writing Assistant helps professionals create high-quality content in minutes. Features include: Blog post generation, Email templates, Social media content, SEO optimization, 25+ languages supported",
            "screenshots": [
                "https://writingassistant.ai/screenshots/dashboard.png",
                "https://writingassistant.ai/screenshots/editor.png",
                "https://writingassistant.ai/screenshots/results.png"
            ],
            "video_url": "https://youtube.com/watch?v=demo",
            "categories": ["productivity", "writing", "business"],
            "tags": ["ai-writer", "content-creation", "seo", "multilingual"],
            "pricing": {
                "model": "freemium",
                "free_tier": "10 documents/month",
                "paid_tiers": [
                    {"name": "Pro", "price": "$29/month", "features": "Unlimited docs"},
                    {"name": "Team", "price": "$99/month", "features": "5 users + API"}
                ]
            },
            "integration_guides": [
                {
                    "title": "Quick Start Guide",
                    "url": "https://docs.writingassistant.ai/quickstart"
                },
                {
                    "title": "API Documentation",
                    "url": "https://docs.writingassistant.ai/api"
                }
            ]
        },
        "badges": {
            "verified": True,
            "top_rated": True,
            "editors_choice": False,
            "trending": True
        },
        "social_proof": {
            "testimonials": [
                {
                    "author": "Sarah Chen",
                    "role": "Content Manager",
                    "company": "TechCorp",
                    "text": "This app saved us 20 hours per week on content creation!"
                }
            ],
            "stats": {
                "users": 50000,
                "documents_created": 1000000,
                "time_saved_hours": 500000
            }
        }
    }
)

# Get marketplace performance
performance = requests.get(
    f"https://api.parrotrouter.com/v1/apps/{app_id}/marketplace/stats",
    headers={"Authorization": "Bearer your-api-key"}
).json()

print(f"Marketplace views: {performance['views']}")
print(f"Click-through rate: {performance['ctr']}%")
print(f"Conversion rate: {performance['conversion_rate']}%")
print(f"Average rating: {performance['rating']}/5 ({performance['reviews']} reviews)")
print(f"Marketplace rank: #{performance['rank']} in {performance['category']}")

# Track user lifecycle
class UserTracker:
    def __init__(self, app_id, api_key):
        self.app_id = app_id
        self.api_key = api_key
        self.client = OpenAI(
            base_url="https://api.parrotrouter.com/v1",
            api_key=api_key
        )
    
    def track_user_signup(self, user_id, metadata):
        requests.post(
            "https://api.parrotrouter.com/v1/apps/events",
            headers={"Authorization": f"Bearer {self.api_key}"},
            json={
                "app_id": self.app_id,
                "event": "user_signup",
                "user_id": user_id,
                "metadata": metadata
            }
        )
    
    def track_feature_usage(self, user_id, feature, value=None):
        return self.client.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{"role": "user", "content": "..."}],
            extra_headers={
                "X-App-ID": self.app_id,
                "X-User-ID": user_id,
                "X-Feature": feature,
                "X-Feature-Value": str(value) if value else None
            }
        )
    
    def get_user_analytics(self, user_id):
        return requests.get(
            f"https://api.parrotrouter.com/v1/apps/{self.app_id}/users/{user_id}",
            headers={"Authorization": f"Bearer {self.api_key}"}
        ).json()

# Usage
tracker = UserTracker("app_abc123", "your-api-key")

# Track signup
tracker.track_user_signup("user_123", {
    "plan": "free",
    "source": "organic",
    "referrer": "blog_post"
})

# Track feature usage with AI request
response = tracker.track_feature_usage(
    "user_123", 
    "blog_writer", 
    {"word_count": 1000}
)

# Get user insights
insights = tracker.get_user_analytics("user_123")
print(f"User lifetime value: $" + str(insights['ltv']))
print(f"Features used: {insights['features_used']}")
print(f"Retention probability: {insights['retention_probability']}%")