model_selector.py

#!/usr/bin/env python3
"""
Model Selection Helper for LLM API

This script helps users choose the right model based on their requirements.
"""

import os
import sys
from typing import Dict, List, Any

# Model configurations (same as in llm_manager.py)
MODEL_CONFIGS = {
    "phi-2": {
        "name": "microsoft/phi-2",
        "type": "transformers",
        "context_window": 2048,
        "prompt_format": "phi",
        "description": "Microsoft Phi-2 (2.7B) - Excellent reasoning and coding",
        "size_mb": 1700,
        "speed_rating": 9,
        "quality_rating": 9,
        "stop_sequences": ["<|endoftext|>", "Human:", "Assistant:"],
        "parameters": "2.7B"
    },
    "tinyllama": {
        "name": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        "type": "transformers", 
        "context_window": 2048,
        "prompt_format": "llama",
        "description": "TinyLlama 1.1B - Ultra-lightweight and fast",
        "size_mb": 700,
        "speed_rating": 10,
        "quality_rating": 7,
        "stop_sequences": ["[INST]", "[/INST]", "</s>"],
        "parameters": "1.1B"
    },
    "qwen2.5-3b": {
        "name": "Qwen/Qwen2.5-3B-Instruct",
        "type": "transformers",
        "context_window": 32768,
        "prompt_format": "qwen",
        "description": "Qwen2.5 3B - Excellent multilingual support",
        "size_mb": 2000,
        "speed_rating": 8,
        "quality_rating": 8,
        "stop_sequences": ["<|endoftext|>", "<|im_end|>"],
        "parameters": "3B"
    },
    "gemma-2b": {
        "name": "google/gemma-2b-it",
        "type": "transformers",
        "context_window": 8192,
        "prompt_format": "gemma",
        "description": "Google Gemma 2B - Good balance of speed and quality",
        "size_mb": 1500,
        "speed_rating": 8,
        "quality_rating": 7,
        "stop_sequences": ["<end_of_turn>", "<start_of_turn>"],
        "parameters": "2B"
    },
    "llama-2-7b": {
        "name": "models/llama-2-7b-chat.gguf",
        "type": "llama_cpp",
        "context_window": 4096,
        "prompt_format": "llama",
        "description": "LLaMA 2 7B Chat - Balanced performance",
        "size_mb": 4000,
        "speed_rating": 6,
        "quality_rating": 8,
        "stop_sequences": ["[INST]", "[/INST]", "</s>"],
        "parameters": "7B"
    },
    "mistral-7b": {
        "name": "mistralai/Mistral-7B-Instruct-v0.2",
        "type": "transformers",
        "context_window": 32768,
        "prompt_format": "mistral",
        "description": "Mistral 7B - Excellent performance",
        "size_mb": 4000,
        "speed_rating": 6,
        "quality_rating": 9,
        "stop_sequences": ["</s>", "[INST]", "[/INST]"],
        "parameters": "7B"
    },
    "llama-2-13b": {
        "name": "models/llama-2-13b-chat.gguf",
        "type": "llama_cpp",
        "context_window": 4096,
        "prompt_format": "llama",
        "description": "LLaMA 2 13B Chat - High quality",
        "size_mb": 8000,
        "speed_rating": 4,
        "quality_rating": 9,
        "stop_sequences": ["[INST]", "[/INST]", "</s>"],
        "parameters": "13B"
    }
}


def print_model_table():
    """Print a formatted table of all available models."""
    print("\n🚀 Available Models:")
    print("=" * 120)
    print(f"{'Model ID':<15} {'Parameters':<10} {'Size (MB)':<10} {'Speed':<6} {'Quality':<8} {'Type':<12} {'Context':<8}")
    print("-" * 120)
    
    for model_id, config in MODEL_CONFIGS.items():
        print(f"{model_id:<15} {config['parameters']:<10} {config['size_mb']:<10} "
              f"{config['speed_rating']:<6} {config['quality_rating']:<8} "
              f"{config['type']:<12} {config['context_window']:<8}")
    
    print("=" * 120)


def print_model_details(model_id: str):
    """Print detailed information about a specific model."""
    if model_id not in MODEL_CONFIGS:
        print(f"❌ Model '{model_id}' not found!")
        return
    
    config = MODEL_CONFIGS[model_id]
    print(f"\n📋 Model Details: {model_id}")
    print("=" * 50)
    print(f"Description: {config['description']}")
    print(f"Parameters: {config['parameters']}")
    print(f"Size: {config['size_mb']} MB")
    print(f"Speed Rating: {config['speed_rating']}/10")
    print(f"Quality Rating: {config['quality_rating']}/10")
    print(f"Type: {config['type']}")
    print(f"Context Window: {config['context_window']} tokens")
    print(f"Prompt Format: {config['prompt_format']}")
    print(f"Stop Sequences: {config['stop_sequences']}")


def get_recommendations(use_case: str = "general") -> List[str]:
    """Get model recommendations based on use case."""
    recommendations = {
        "speed": ["tinyllama", "phi-2", "gemma-2b"],
        "quality": ["mistral-7b", "llama-2-13b", "qwen2.5-3b"],
        "balanced": ["phi-2", "qwen2.5-3b", "llama-2-7b"],
        "coding": ["phi-2", "qwen2.5-3b", "mistral-7b"],
        "multilingual": ["qwen2.5-3b", "mistral-7b", "llama-2-7b"],
        "general": ["phi-2", "qwen2.5-3b", "llama-2-7b"]
    }
    
    return recommendations.get(use_case, recommendations["general"])


def print_recommendations(use_case: str = "general"):
    """Print model recommendations for a specific use case."""
    recs = get_recommendations(use_case)
    print(f"\n🎯 Recommendations for {use_case} use case:")
    print("=" * 50)
    
    for i, model_id in enumerate(recs, 1):
        config = MODEL_CONFIGS[model_id]
        print(f"{i}. {model_id} ({config['parameters']}) - {config['description']}")
        print(f"   Speed: {config['speed_rating']}/10, Quality: {config['quality_rating']}/10, Size: {config['size_mb']}MB")


def main():
    """Main function to handle command line arguments."""
    if len(sys.argv) == 1:
        # No arguments - show help
        print("""
🎯 LLM Model Selector

Usage:
  python model_selector.py list                    # List all models
  python model_selector.py details <model_id>      # Show model details
  python model_selector.py recommend <use_case>    # Get recommendations
  python model_selector.py set <model_id>          # Set model for API

Use cases:
  speed, quality, balanced, coding, multilingual, general

Examples:
  python model_selector.py list
  python model_selector.py details phi-2
  python model_selector.py recommend coding
  python model_selector.py set phi-2
        """)
        return

    command = sys.argv[1].lower()

    if command == "list":
        print_model_table()
    
    elif command == "details" and len(sys.argv) == 3:
        model_id = sys.argv[2]
        print_model_details(model_id)
    
    elif command == "recommend" and len(sys.argv) == 3:
        use_case = sys.argv[2]
        print_recommendations(use_case)
    
    elif command == "set" and len(sys.argv) == 3:
        model_id = sys.argv[2]
        if model_id in MODEL_CONFIGS:
            # Set environment variable
            os.environ["MODEL_NAME"] = model_id
            print(f"✅ Model set to: {model_id}")
            print(f"📋 Run: export MODEL_NAME={model_id}")
            print(f"🚀 Or start server with: MODEL_NAME={model_id} uvicorn app.main:app --reload")
        else:
            print(f"❌ Model '{model_id}' not found!")
            print("Use 'python model_selector.py list' to see available models")
    
    else:
        print("❌ Invalid command. Use 'python model_selector.py' for help.")


if __name__ == "__main__":
    main()