File size: 4,425 Bytes
72f802a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
"""
Configuration management for Product Feature Validation Agent
Handles API keys, environment settings, and deployment configurations
"""
import os
from typing import Dict, Any, Optional
from dataclasses import dataclass
import logging
logger = logging.getLogger(__name__)
@dataclass
class APIConfig:
"""API configuration settings"""
anthropic_api_key: Optional[str] = None
modal_token: Optional[str] = None
newsapi_key: Optional[str] = None
reddit_client_id: Optional[str] = None
reddit_client_secret: Optional[str] = None
@dataclass
class ProcessingConfig:
"""Processing configuration settings"""
max_reviews_per_app: int = 50
max_reddit_posts: int = 50
max_news_articles: int = 50
sentiment_batch_size: int = 32
modal_gpu_type: str = "T4"
claude_model: str = "claude-3.5-sonnet-20241022"
max_tokens: int = 2000
@dataclass
class HackathonConfig:
"""Hackathon-specific configuration"""
project_name: str = "product-feature-agent"
team_name: str = "IYKYKnow.ai"
track: str = "mcp-server-track"
gradio_server_port: int = 7860
mcp_server_enabled: bool = True
class ConfigManager:
"""Manages application configuration"""
def __init__(self):
self.api_config = self._load_api_config()
self.processing_config = ProcessingConfig()
self.hackathon_config = HackathonConfig()
def _load_api_config(self) -> APIConfig:
"""Load API configuration from environment variables"""
return APIConfig(
anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
modal_token=os.getenv("MODAL_TOKEN"),
newsapi_key=os.getenv("NEWSAPI_KEY"),
reddit_client_id=os.getenv("REDDIT_CLIENT_ID"),
reddit_client_secret=os.getenv("REDDIT_CLIENT_SECRET")
)
def is_production_ready(self) -> bool:
"""Check if all required API keys are configured"""
required_keys = [
self.api_config.anthropic_api_key,
self.api_config.modal_token,
self.api_config.newsapi_key
]
return all(key is not None for key in required_keys)
def get_demo_mode_status(self) -> Dict[str, bool]:
"""Get status of which services are in demo mode"""
return {
"anthropic": self.api_config.anthropic_api_key is None,
"modal": self.api_config.modal_token is None,
"newsapi": self.api_config.newsapi_key is None,
"reddit": self.api_config.reddit_client_id is None
}
def get_cost_estimates(self) -> Dict[str, Any]:
"""Get estimated costs for different operations"""
return {
"modal_labs": {
"t4_gpu_per_hour": 0.60,
"credits_available": 250,
"estimated_usage": 15.77,
"efficiency_percentage": 6.3
},
"anthropic": {
"credits_available": 25,
"batch_discount": 0.50,
"estimated_tokens": 125000,
"cost_per_1k_tokens": 0.003
},
"newsapi": {
"free_tier_requests": 1000,
"estimated_usage": 200
}
}
# Global configuration instance
config = ConfigManager()
def get_config() -> ConfigManager:
"""Get the global configuration instance"""
return config
def log_configuration_status():
"""Log current configuration status"""
demo_status = config.get_demo_mode_status()
demo_services = [service for service, is_demo in demo_status.items() if is_demo]
if config.is_production_ready():
logger.info("All API keys configured - running in production mode")
else:
logger.warning(f"Missing API keys: {demo_services} - running in demo mode")
cost_estimates = config.get_cost_estimates()
logger.info(f"Estimated Modal Labs usage: ${cost_estimates['modal_labs']['estimated_usage']}")
logger.info(f"Estimated Anthropic usage: {cost_estimates['anthropic']['estimated_tokens']} tokens")
if __name__ == "__main__":
# Test configuration
log_configuration_status()
print(f"Production ready: {config.is_production_ready()}")
print(f"Demo mode services: {config.get_demo_mode_status()}") |