import os, warnings
from dataclasses import dataclass

# Load secrets if environment variables are not set
from utils.load_secrets import load_secrets

if not os.getenv("NEBIUS_API_KEY") or not os.getenv("NEBIUS_MODEL"):
    load_secrets("tests/secrets/creds.py")

# =========================
#        MOCK PROJECTS
# =========================

MOCK_PROJECTS: dict[str, str] = {
    "go-rssagg": """# GO-RSSAGG

## Project Description
RSS aggregator backend written in Go. Features REST API and basic authentication.

## Features
- User authentication and account management
- Fetch and parse RSS feeds
- Store feed content in database
- REST API to access feeds
- Follow/unfollow feed functionality
- Mark posts as read/unread

## Tech Stack
- Go for backend
- PostgreSQL for database
- RESTful API endpoints
- JWT for authentication
""",
    "rust-chess-pipeline": """# RUST CHESS PIPELINE

## Project Description
Data Pipeline that extracts chess match metrics and match annotations from Excel files, using AWS Lambda and Step Functions written in Rust.

## Features
- Parse Excel files containing chess match data
- Extract player statistics, game metadata, and move annotations
- Calculate performance metrics and ELO adjustments
- Store results in data warehouse
- Generate analytical reports

## Tech Stack
- Rust for core processing logic
- AWS Lambda for serverless compute
- AWS Step Functions for orchestration
- Amazon S3 for storage
- AWS Glue for ETL processing
""",
    "python-ml-forecasting": """# PYTHON ML FORECASTING

## Project Description
Machine learning service for time-series forecasting of inventory demands, with API endpoints for integration with existing systems.

## Features
- Historical data ingestion and preprocessing
- Feature engineering for time-series data
- Multiple forecasting models (ARIMA, Prophet, LSTM)
- Model selection and hyperparameter optimization
- REST API for predictions and model management
- Visualization of forecasts and confidence intervals

## Tech Stack
- Python for core functionality
- FastAPI for REST endpoints
- PyTorch and scikit-learn for ML models
- PostgreSQL for metadata storage
- Docker for containerization
""",
}

# =========================
#        AGENTS CONFIG
# =========================
@dataclass
class AgentsConfig:
    """Global configuration for all agents"""

    # Model settings
    nebius_api_key: str
    nebius_model: str

    # Prompt templates
    task_splitter_prompt: str = "Split the following task into an accurate and concise tree of required subtasks:\n{{query}}\n\nAim for 3 to 15 subtasks.\n\nYour output must be a markdown bullet list, with no additional comments.\n\n"
    task_evaluator_prompt: str = "Evaluate the elapsed time, in 30 minute units, for a competent human to complete the following task:\n{{query}}\n\nYour output must be a one integer, with no additional comments.\n\n"
    task_deps_matcher_prompt: str = "Given the following task:\n{{task}}\n\nAnd these available skills:\n{{skills}}\n\nIn this context:\n{{context}}\n\nSelect the most appropriate skill to complete this task. Return only the skill name as a string, with no additional comments or formatting.\n\n"

    # LLM settings
    timeout: int = 30
    max_retries: int = 3
    verify_ssl: bool = True
    request_timeout: int = 30
    max_tokens: int = 1024
    temperature: float = 0.1
    workflow_timeout: int = 300  # 5 minutes for workflow timeout

    def __post_init__(self):
        """Validate required configuration"""
        if not self.nebius_model or not self.nebius_api_key:
            if self.nebius_model == "dev-model" and self.nebius_api_key == "dev-key":
                warnings.warn(
                    "Using development defaults for NEBIUS_MODEL and NEBIUS_API_KEY"
                )

            else:
                raise ValueError(
                    "NEBIUS_MODEL and NEBIUS_API_KEY environment variables must be set"
                )


# Global configuration instance
# For development environments where env vars might not be set, use defaults
AGENTS_CONFIG = AgentsConfig(
    nebius_api_key=os.getenv("NEBIUS_API_KEY", "dev-key"),
    nebius_model=os.getenv("NEBIUS_MODEL", "dev-model"),
)