# models.py
from dataclasses import dataclass
from typing import List, Optional

@dataclass
class ModelInfo:
    """
    Represents metadata for an inference model.

    Attributes:
        name: Human-readable name of the model.
        id: Unique model identifier (HF/externally routed).
        description: Short description of the model's capabilities.
        default_provider: Preferred inference provider ("auto", "groq", "openai", "gemini", "fireworks").
    """
    name: str
    id: str
    description: str
    default_provider: str = "auto"

# Registry of supported models
AVAILABLE_MODELS: List[ModelInfo] = [
    ModelInfo(
        name="Moonshot Kimi-K2",
        id="moonshotai/Kimi-K2-Instruct",
        description="Moonshot AI Kimi-K2-Instruct model for code generation and general tasks",
        default_provider="groq"
    ),
    ModelInfo(
        name="DeepSeek V3",
        id="deepseek-ai/DeepSeek-V3-0324",
        description="DeepSeek V3 model for code generation",
    ),
    ModelInfo(
        name="DeepSeek R1",
        id="deepseek-ai/DeepSeek-R1-0528",
        description="DeepSeek R1 model for code generation",
    ),
    ModelInfo(
        name="ERNIE-4.5-VL",
        id="baidu/ERNIE-4.5-VL-424B-A47B-Base-PT",
        description="ERNIE-4.5-VL model for multimodal code generation with image support",
    ),
    ModelInfo(
        name="MiniMax M1",
        id="MiniMaxAI/MiniMax-M1-80k",
        description="MiniMax M1 model for code generation and general tasks",
    ),
    ModelInfo(
        name="Qwen3-235B-A22B",
        id="Qwen/Qwen3-235B-A22B",
        description="Qwen3-235B-A22B model for code generation and general tasks",
    ),
    ModelInfo(
        name="SmolLM3-3B",
        id="HuggingFaceTB/SmolLM3-3B",
        description="SmolLM3-3B model for code generation and general tasks",
    ),
    ModelInfo(
        name="GLM-4.1V-9B-Thinking",
        id="THUDM/GLM-4.1V-9B-Thinking",
        description="GLM-4.1V-9B-Thinking model for multimodal code generation with image support",
    ),
    ModelInfo(
        name="OpenAI GPT-4",
        id="openai/gpt-4",
        description="OpenAI GPT-4 model via HF Inference Providers",
        default_provider="openai"
    ),
    ModelInfo(
        name="Gemini Pro",
        id="gemini/pro",
        description="Google Gemini Pro model via HF Inference Providers",
        default_provider="gemini"
    ),
    ModelInfo(
        name="Fireworks AI",
        id="fireworks-ai/fireworks-v1",
        description="Fireworks AI model via HF Inference Providers",
        default_provider="fireworks"
    ),
]


def find_model(identifier: str) -> Optional[ModelInfo]:
    """
    Lookup a model by its human name or identifier.

    Args:
        identifier: ModelInfo.name (case-insensitive) or ModelInfo.id
    Returns:
        The matching ModelInfo or None if not found.
    """
    identifier_lower = identifier.lower()
    for model in AVAILABLE_MODELS:
        if model.id == identifier or model.name.lower() == identifier_lower:
            return model
    return None


# inference.py
from typing import List, Dict
from hf_client import get_inference_client


def chat_completion(
    model_id: str,
    messages: List[Dict[str, str]],
    provider: str = None,
    max_tokens: int = 4096
) -> str:
    """
    Send a chat completion request to the appropriate inference provider.

    Args:
        model_id: The model identifier to use.
        messages: A list of OpenAI-style {'role','content'} messages.
        provider: Optional override for provider; uses model default if None.
        max_tokens: Maximum tokens to generate.

    Returns:
        The assistant's response content.
    """
    # Initialize client (provider resolution inside)
    client = get_inference_client(model_id, provider or "auto")
    response = client.chat.completions.create(
        model=model_id,
        messages=messages,
        max_tokens=max_tokens
    )
    # Extract and return first choice content
    return response.choices[0].message.content


def stream_chat_completion(
    model_id: str,
    messages: List[Dict[str, str]],
    provider: str = None,
    max_tokens: int = 4096
):
    """
    Generator for streaming chat completions.
    Yields partial message chunks as strings.
    """
    client = get_inference_client(model_id, provider or "auto")
    stream = client.chat.completions.create(
        model=model_id,
        messages=messages,
        max_tokens=max_tokens,
        stream=True
    )
    for chunk in stream:
        delta = getattr(chunk.choices[0].delta, "content", None)
        if delta:
            yield delta