gpt4free/g4f/Provider/DeepInfraChat.py

from __future__ import annotations

import random
from .template import OpenaiTemplate
from ..errors import ModelNotFoundError
from .. import debug


class DeepInfraChat(OpenaiTemplate):
    parent = "DeepInfra"
    url = "https://deepinfra.com/chat"
    login_url = "https://deepinfra.com/dash/api_keys"
    api_base = "https://api.deepinfra.com/v1/openai"
    api_endpoint = "https://api.deepinfra.com/v1/openai/chat/completions"
    working = True

    default_model = 'deepseek-ai/DeepSeek-V3-0324'
    default_vision_model = 'microsoft/Phi-4-multimodal-instruct'
    vision_models = [
        default_vision_model,
        'meta-llama/Llama-3.2-90B-Vision-Instruct'
    ]
    models = [
        # cognitivecomputations
        'cognitivecomputations/dolphin-2.6-mixtral-8x7b',
        'cognitivecomputations/dolphin-2.9.1-llama-3-70b',

        # deepinfra
        'deepinfra/airoboros-70b',

        # deepseek-ai
        default_model,
        'deepseek-ai/DeepSeek-V3-0324-Turbo',

        'deepseek-ai/DeepSeek-R1-0528-Turbo',
        'deepseek-ai/DeepSeek-R1-0528',
        
        'deepseek-ai/DeepSeek-Prover-V2-671B',
        
        'deepseek-ai/DeepSeek-V3',
        
        'deepseek-ai/DeepSeek-R1',
        'deepseek-ai/DeepSeek-R1-Turbo',
        'deepseek-ai/DeepSeek-R1-Distill-Llama-70B',
        'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B',

        # google (gemma)
        'google/gemma-1.1-7b-it',
        'google/gemma-2-9b-it',
        'google/gemma-2-27b-it',
        'google/gemma-3-4b-it',
        'google/gemma-3-12b-it',
        'google/gemma-3-27b-it',
        
        # google (codegemma)
        'google/codegemma-7b-it',

        # lizpreciatior
        'lizpreciatior/lzlv_70b_fp16_hf',

        # meta-llama
        'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8',
        'meta-llama/Llama-4-Scout-17B-16E-Instruct',
        'meta-llama/Meta-Llama-3.1-8B-Instruct',
        'meta-llama/Llama-3.3-70B-Instruct-Turbo',
        'meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo',

        # microsoft 
        'microsoft/phi-4-reasoning-plus',
        'microsoft/phi-4',  
              
        'microsoft/WizardLM-2-8x22B',
        'microsoft/WizardLM-2-7B',

        # mistralai
        'mistralai/Mistral-Small-3.1-24B-Instruct-2503',

        # Qwen
        'Qwen/Qwen3-235B-A22B',
        'Qwen/Qwen3-30B-A3B',
        'Qwen/Qwen3-32B',
        'Qwen/Qwen3-14B',
        'Qwen/QwQ-32B',
    ] + vision_models

    model_aliases = {
        # cognitivecomputations
        "dolphin-2.6": "cognitivecomputations/dolphin-2.6-mixtral-8x7b",
        "dolphin-2.9": "cognitivecomputations/dolphin-2.9.1-llama-3-70b",

        # deepinfra
        "airoboros-70b": "deepinfra/airoboros-70b",

        # deepseek-ai
        "deepseek-prover-v2": "deepseek-ai/DeepSeek-Prover-V2-671B",
        "deepseek-prover-v2-671b": "deepseek-ai/DeepSeek-Prover-V2-671B",
        "deepseek-r1": ["deepseek-ai/DeepSeek-R1", "deepseek-ai/DeepSeek-R1-0528"],
        "deepseek-r1-0528": "deepseek-ai/DeepSeek-R1-0528",
        "deepseek-r1-0528-turbo": "deepseek-ai/DeepSeek-R1-0528-Turbo",
        "deepseek-r1-distill-llama-70b": "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
        "deepseek-r1-distill-qwen-32b": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
        "deepseek-r1-turbo": "deepseek-ai/DeepSeek-R1-Turbo",
        "deepseek-v3": ["deepseek-ai/DeepSeek-V3", "deepseek-ai/DeepSeek-V3-0324"],
        "deepseek-v3-0324": "deepseek-ai/DeepSeek-V3-0324",
        "deepseek-v3-0324-turbo": "deepseek-ai/DeepSeek-V3-0324-Turbo",

        # google
        "codegemma-7b": "google/codegemma-7b-it",
        "gemma-1.1-7b": "google/gemma-1.1-7b-it",
        "gemma-2-27b": "google/gemma-2-27b-it",
        "gemma-2-9b": "google/gemma-2-9b-it",
        "gemma-3-4b": "google/gemma-3-4b-it",
        "gemma-3-12b": "google/gemma-3-12b-it",
        "gemma-3-27b": "google/gemma-3-27b-it",

        # lizpreciatior
        "lzlv-70b": "lizpreciatior/lzlv_70b_fp16_hf",

        # meta-llama
        "llama-3.1-8b": "meta-llama/Meta-Llama-3.1-8B-Instruct",
        "llama-3.2-90b": "meta-llama/Llama-3.2-90B-Vision-Instruct",
        "llama-3.3-70b": "meta-llama/Llama-3.3-70B-Instruct",
        "llama-4-maverick": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
        "llama-4-scout": "meta-llama/Llama-4-Scout-17B-16E-Instruct",

        # microsoft
        "phi-4": "microsoft/phi-4",
        "phi-4-multimodal": default_vision_model,
        "phi-4-reasoning-plus": "microsoft/phi-4-reasoning-plus",
        "wizardlm-2-7b": "microsoft/WizardLM-2-7B",
        "wizardlm-2-8x22b": "microsoft/WizardLM-2-8x22B",

        # mistralai
        "mistral-small-3.1-24b": "mistralai/Mistral-Small-3.1-24B-Instruct-2503",

        # Qwen
        "qwen-3-14b": "Qwen/Qwen3-14B",
        "qwen-3-30b": "Qwen/Qwen3-30B-A3B",
        "qwen-3-32b": "Qwen/Qwen3-32B",
        "qwen-3-235b": "Qwen/Qwen3-235B-A22B",
        "qwq-32b": "Qwen/QwQ-32B",
    }

    @classmethod
    def get_model(cls, model: str, **kwargs) -> str:
        """Get the internal model name from the user-provided model name."""
        # kwargs can contain api_key, api_base, etc. but we don't need them for model selection
        if not model:
            return cls.default_model
        
        # Check if the model exists directly in our models list
        if model in cls.models:
            return model
        
        # Check if there's an alias for this model
        if model in cls.model_aliases:
            alias = cls.model_aliases[model]
            # If the alias is a list, randomly select one of the options
            if isinstance(alias, list):
                import random
                selected_model = random.choice(alias)
                debug.log(f"DeepInfraChat: Selected model '{selected_model}' from alias '{model}'")
                return selected_model
            debug.log(f"DeepInfraChat: Using model '{alias}' for alias '{model}'")
            return alias
        
        raise ModelNotFoundError(f"Model {model} not found")