src/utils/model_registry.py

import dspy
import os

# Model providers
PROVIDERS = {
    "openai": "OpenAI",
    "anthropic": "Anthropic",
    "groq": "GROQ",
    "gemini": "Google Gemini"
}
max_tokens = int(os.getenv("MAX_TOKENS", 6000))

# Clamp temperature to valid range (0..1) for all models
default_temperature = min(1.0, max(0.0, float(os.getenv("TEMPERATURE", "1.0"))))

small_lm = dspy.LM('openai/gpt-4o-mini',max_tokens=300,api_key=os.getenv("OPENAI_API_KEY"), cache=False)

mid_lm = dspy.LM('openai/gpt-4o-mini',max_tokens=2100,api_key=os.getenv("OPENAI_API_KEY"), cache=False)

gpt_4o_mini = dspy.LM('openai/gpt-4o-mini',max_tokens=4000,api_key=os.getenv("OPENAI_API_KEY"), cache=False)


# Create model API objects
# OpenAI models
gpt_5_mini = dspy.LM(
    model="openai/gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=default_temperature,
    max_tokens= 16_000,
    # max_completion_tokens=max_tokens,
    cache=False
)

gpt_5 = dspy.LM(
    model="openai/gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=default_temperature,
    max_tokens= 16_000,
    # max_completion_tokens=max_tokens,  # Use max_completion_tokens for gpt-5
    cache=False
)

gpt_5_nano = dspy.LM(
    model="openai/gpt-5-nano",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=default_temperature,
    max_tokens= 16_000,
    # max_completion_tokens=max_tokens,
    cache=False
)

o1 = dspy.LM(
    model="openai/o1-preview",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=1,
    max_tokens=20_000,  # Use max_completion_tokens for o1
    cache=False
)

o1_pro = dspy.LM(
    model="openai/o1-pro",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=1,
    max_tokens=20_000,  # Use max_completion_tokens for o1-pro
    cache=False
)

o1_mini = dspy.LM(
    model="openai/o1-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=1,
    max_tokens=20_000,
    cache=False
)

o3 = dspy.LM(
    model="openai/o3-2025-04-16",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=default_temperature,
    max_tokens=20_000,
    cache=False
)

o3_mini = dspy.LM(
    model="openai/o3-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=default_temperature,
    max_tokens=20_000,
    cache=False
)

claude_4_5_sonnet_latest = dspy.LM(
    model="anthropic/claude-sonnet-4-5-20250929",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
) 

# Anthropic models
claude_3_7_sonnet_latest = dspy.LM(
    model="anthropic/claude-3-7-sonnet-latest",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

claude_sonnet_4_20250514 = dspy.LM(
    model="anthropic/claude-sonnet-4-20250514",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

claude_3_opus_latest = dspy.LM(
    model="anthropic/claude-3-opus-latest",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

claude_opus_4_20250514 = dspy.LM(
    model="anthropic/claude-opus-4-20250514",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

claude_opus_4_1 = dspy.LM(
    model="anthropic/claude-opus-4-1",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

# Groq models
deepseek_r1_distill_llama_70b = dspy.LM(
    model="groq/deepseek-r1-distill-llama-70b",
    api_key=os.getenv("GROQ_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

gpt_oss_120B = dspy.LM(
    model="groq/gpt-oss-120B",
    api_key=os.getenv("GROQ_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

gpt_oss_20B = dspy.LM(
    model="groq/gpt-oss-20B",
    api_key=os.getenv("GROQ_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

gemini_2_5_pro_preview_03_25 = dspy.LM(
    model="gemini/gemini-2.5-pro-preview-03-25",
    api_key=os.getenv("GEMINI_API_KEY"),
    temperature=default_temperature,
    max_tokens=max_tokens,
    cache=False
)

MODEL_OBJECTS = {
    # OpenAI models
    "gpt-4o-mini":gpt_4o_mini,
    "gpt-5-mini": gpt_5_mini,
    "gpt-5": gpt_5,
    "gpt-5-nano": gpt_5_nano,
    "o1": o1,
    "o1-pro": o1_pro,
    "o1-mini": o1_mini,
    "o3": o3,
    "o3-mini": o3_mini,
    
    # Anthropic models
    "claude-3-7-sonnet-latest": claude_3_7_sonnet_latest,
    "claude-sonnet-4-20250514": claude_sonnet_4_20250514,
    "claude-sonnet-4-5-20250929": claude_4_5_sonnet_latest,
    "claude-3-opus-latest": claude_3_opus_latest,
    "claude-opus-4-20250514": claude_opus_4_20250514,
    "claude-opus-4-1": claude_opus_4_1,
    
    # Groq models
    "deepseek-r1-distill-llama-70b": deepseek_r1_distill_llama_70b,
    "gpt-oss-120B": gpt_oss_120B,
    "gpt-oss-20B": gpt_oss_20B,
    
    # Gemini models
    "gemini-2.5-pro-preview-03-25": gemini_2_5_pro_preview_03_25
}


# Model registry dictionary for easy access

def get_model_object(model_name: str):
    """Get model object by name"""
    return MODEL_OBJECTS.get(model_name, gpt_5_mini)  # Default to gpt-5-mini


# Get max tokens from environment
max_tokens = int(os.getenv("MAX_TOKENS", 6000))

# Tiers based on cost per 1K tokens
MODEL_TIERS = {
    "tier1": {
        "name": "Basic",
        "credits": 1,
        "models": [
            "gpt-oss-20B"
        ]
    },
    "tier2": {
        "name": "Standard",
        "credits": 3,
        "models": [
            "o1-mini",
            "o3-mini",
            "gpt-5-nano"   # Added
        ]
    },
    "tier3": {
        "name": "Premium",
        "credits": 5,
        "models": [
            "o3",
            "claude-3-7-sonnet-latest",
            "claude-sonnet-4-20250514",
            "deepseek-r1-distill-llama-70b",
            "gpt-oss-120B",
            "gemini-2.5-pro-preview-03-25",
            "gpt-5-mini"   # Added
        ]
    },
    "tier4": {
        "name": "Premium Plus",
        "credits": 20,
        "models": [
            "gpt-4.5-preview",
            "o1",
            "o1-pro",
            "claude-3-opus-latest",
            "claude-opus-4-20250514",
            "claude-sonnet-4-5-20250929",
            "gpt-5",
            "claude-opus-4-1"
        ]
    }
}

# Model metadata (display name, context window, etc.)
MODEL_METADATA = {
    # OpenAI
    "o1": {"display_name": "o1", "context_window": 128000},
    "o1-pro": {"display_name": "o1 Pro", "context_window": 128000},
    "o1-mini": {"display_name": "o1 Mini", "context_window": 128000},
    "o3": {"display_name": "o3", "context_window": 128000},
    "o3-mini": {"display_name": "o3 Mini", "context_window": 128000},
    "gpt-5": {"display_name": "GPT-5", "context_window": 400000},
    "gpt-5-mini": {"display_name": "GPT-5 Mini", "context_window": 150000},  # estimated
    "gpt-5-nano": {"display_name": "GPT-5 Nano", "context_window": 64000},    # estimated

    # Anthropic
    "claude-3-opus-latest": {"display_name": "Claude 3 Opus", "context_window": 200000},
    "claude-3-7-sonnet-latest": {"display_name": "Claude 3.7 Sonnet", "context_window": 200000},
    "claude-opus-4-1": {"display_name": "Claude Opus 4.1", "context_window": 200000},
    "claude-sonnet-4-5-20250929": {"display_name": "Claude Sonnet 4.5", "context_window": 200000},

    # GROQ
    "deepseek-r1-distill-llama-70b": {"display_name": "DeepSeek R1 Distill Llama 70b", "context_window": 32768},
    "gpt-oss-120B": {"display_name": "OpenAI gpt oss 120B", "context_window": 128000},
    "gpt-oss-20B": {"display_name": "OpenAI gpt oss 20B", "context_window": 128000},

    # Gemini
    "gemini-2.5-pro-preview-03-25": {"display_name": "Gemini 2.5 Pro", "context_window": 1000000},
}

MODEL_COSTS = {
    "openai": {
        "o1": {"input": 0.015, "output": 0.06},  
        "o1-pro": {"input": 0.015, "output": 0.6},
        "o1-mini": {"input": 0.00011, "output": 0.00044}, 
        "o3": {"input": 0.002, "output": 0.008},
        "o3-mini": {"input": 0.00011, "output": 0.00044},
        "gpt-5": {"input": 0.00125, "output": 0.01},         # updated real cost
        "gpt-5-mini": {"input": 0.00025, "output": 0.002},   # updated real cost
        "gpt-5-nano": {"input": 0.00005, "output": 0.0004},  # updated real cost
    },
    "anthropic": {
        "claude-3-7-sonnet-latest": {"input": 0.003, "output": 0.015},   
        "claude-sonnet-4-20250514": {"input": 0.003, "output": 0.015},
        "claude-3-opus-latest": {"input": 0.015, "output": 0.075},  
        "claude-opus-4-20250514": {"input": 0.015, "output": 0.075},
        "claude-opus-4-1": {"input": 0.015, "output": 0.075},
        "claude-sonnet-4-5-20250929": {"input": 0.015, "output": 0.075},   # approximate placeholder
    },
    "groq": {
        "deepseek-r1-distill-llama-70b": {"input": 0.00075, "output": 0.00099},
        "gpt-oss-120B": {"input": 0.00075, "output": 0.00099},
        "gpt-oss-20B": {"input": 0.00075, "output": 0.00099}
    },
    "gemini": {
        "gemini-2.5-pro-preview-03-25": {"input": 0.00015, "output": 0.001}
    }
}

# Helper functions

def get_provider_for_model(model_name):
    """Determine the provider based on model name"""
    if not model_name:
        return "Unknown"
        
    model_name = model_name.lower()
    return next((provider for provider, models in MODEL_COSTS.items() 
                if any(model_name in model for model in models)), "Unknown")

def get_model_tier(model_name):
    """Get the tier of a model"""
    for tier_id, tier_info in MODEL_TIERS.items():
        if model_name in tier_info["models"]:
            return tier_id
    return "tier1"  # Default to tier1 if not found

def calculate_cost(model_name, input_tokens, output_tokens):
    """Calculate the cost for using the model based on tokens"""
    if not model_name:
        return 0
        
    # Convert tokens to thousands
    input_tokens_in_thousands = input_tokens / 1000
    output_tokens_in_thousands = output_tokens / 1000
    
    # Get model provider
    model_provider = get_provider_for_model(model_name)
    
    # Handle case where model is not found
    if model_provider == "Unknown" or model_name not in MODEL_COSTS.get(model_provider, {}):
        return 0
        
    return (input_tokens_in_thousands * MODEL_COSTS[model_provider][model_name]["input"] + 
            output_tokens_in_thousands * MODEL_COSTS[model_provider][model_name]["output"])

def get_credit_cost(model_name):
    """Get the credit cost for a model"""
    tier_id = get_model_tier(model_name)
    return MODEL_TIERS[tier_id]["credits"]

def get_display_name(model_name):
    """Get the display name for a model"""
    return MODEL_METADATA.get(model_name, {}).get("display_name", model_name)

def get_context_window(model_name):
    """Get the context window size for a model"""
    return MODEL_METADATA.get(model_name, {}).get("context_window", 4096)

def get_all_models_for_provider(provider):
    """Get all models for a specific provider"""
    if provider not in MODEL_COSTS:
        return []
    return list(MODEL_COSTS[provider].keys())

def get_models_by_tier(tier_id):
    """Get all models for a specific tier"""
    return MODEL_TIERS.get(tier_id, {}).get("models", [])