from pydantic import BaseModel, Field
from typing import Optional, List
from enum import Enum


class HFInferenceModel(str, Enum):
    """Generator models for content creation."""
    QWEN_7B = "Qwen/Qwen2.5-7B-Instruct"
    QWEN_72B = "Qwen/Qwen2.5-72B-Instruct"
    GPT_OSS_120B = "openai/gpt-oss-120b"


class HFJudgeModel(str, Enum):
    """Judge models for quality scoring."""
    QWEN_7B = "Qwen/Qwen2.5-7B-Instruct"
    GLM_5 = "z-ai/glm-5"


class HFInferenceProvider(str, Enum):
    CEREBRAS = "cerebras"
    TOGETHER = "together"


class GenerateRequest(BaseModel):
    num_records: int = Field(default=10, ge=1, le=100, description="Number of records to generate")
    seed_dataset: str = Field(..., description="HF Hub dataset ID for seed data")
    model: HFInferenceModel = Field(default=HFInferenceModel.QWEN_7B, description="HF model for generation")
    provider: HFInferenceProvider = Field(default=HFInferenceProvider.TOGETHER, description="HF Inference provider")
    judge_model: HFJudgeModel = Field(default=HFJudgeModel.QWEN_7B, description="Judge model for quality scoring")
    judge_provider: HFInferenceProvider = Field(default=HFInferenceProvider.TOGETHER, description="Judge model provider")
    temperature: float = Field(default=0.7, ge=0.0, le=1.5, description="Sampling temperature")
    use_judge: bool = Field(default=True, description="Enable LLM Judge scoring")


class LoadSeedsRequest(BaseModel):
    seed_dataset: str = Field(..., description="HF Hub dataset ID for seed data")


class GeneratedRecord(BaseModel):
    instruction: str = Field(..., description="Generated question/instruction")
    output: str = Field(..., description="Generated answer/response")
    topic: str = Field(..., description="Topic of the Q&A")
    difficulty: str = Field(..., description="Difficulty level")
    quality_score: int = Field(default=7, description="Initial quality estimate")
    model: str = Field(..., description="Model used for generation")
    provider: str = Field(..., description="Provider used")


class GenerateResponse(BaseModel):
    success: bool
    data: Optional[List[GeneratedRecord]] = None
    record_count: int = 0
    seeds_used: int = 0
    error: Optional[str] = None


class LoadSeedsResponse(BaseModel):
    success: bool
    loaded_count: int = 0
    dataset_id: Optional[str] = None
    error: Optional[str] = None


class HealthResponse(BaseModel):
    status: str
    model: str
    provider: str
    judge_model: str
    judge_provider: str
    seeds_loaded: bool