models.py

from pydantic import BaseModel, Field
from typing import List, Dict, Optional, Any


class CompetitorText(BaseModel):
    id: int
    text: str

class AnalysisRequest(BaseModel):
    target_text: str                # Текст пользователя
    competitors: List[str]          # Список текстов конкурентов
    keywords: List[str]             # Список ключевых фраз (сырых)
    language: str = "en"            # en, ru, de, es, it, pl, pt
    target_title: str = ""          # Title пользователя
    competitor_titles: List[str] = Field(default_factory=list)  # Title конкурентов

class AnalysisResponse(BaseModel):
    ngram_stats: dict               # Статистика униграм/биграм
    bm25_recommendations: List[dict] # Рекомендации "добавить/убрать"
    bert_analysis: Dict[str, Any]   # Векторный анализ
    word_counts: Dict[str, Any]     # {'target': 500, 'competitors': [600, 450], 'avg': 525}
    title_analysis: Dict[str, Any] = Field(default_factory=dict)  # Анализ Title


class SemanticAnalyzeRequest(BaseModel):
    text: str
    competitors: List[str] = Field(default_factory=list)
    language: str = "ru"
    threshold: int = 50
    compression_ratio: float = 0.1


class SemanticAnalyzeResponse(BaseModel):
    target: Dict[str, Any]
    competitors: List[Dict[str, Any]]
    comparison: Dict[str, Any]


class SemanticSearchRequest(BaseModel):
    query_text: str
    text: str
    language: str = "ru"
    top_n: int = 20


class SemanticSearchResponse(BaseModel):
    results: List[Dict[str, Any]]


class UrlFetchRequest(BaseModel):
    url: str
    user_agent: str = "chrome_desktop"
    timeout_seconds: int = 15


class UrlFetchResponse(BaseModel):
    ok: bool = False
    url: str = ""
    final_url: str = ""
    status_code: int = 0
    user_agent_key: str = ""
    user_agent_value: str = ""
    title: str = ""
    text: str = ""
    error: str = ""


class UserAgentInfo(BaseModel):
    key: str
    name: str
    value: str


class UserAgentsResponse(BaseModel):
    user_agents: List[UserAgentInfo] = Field(default_factory=list)


class OptimizerRequest(BaseModel):
    target_text: str
    competitors: List[str] = Field(default_factory=list)
    keywords: List[str] = Field(default_factory=list)
    language: str = "en"
    target_title: str = ""
    competitor_titles: List[str] = Field(default_factory=list)

    api_key: str
    api_base_url: str = "https://api.deepseek.com/v1"
    model: str = "deepseek-chat"

    max_iterations: int = 2
    candidates_per_iteration: int = 2
    temperature: float = 0.25
    optimization_mode: str = "balanced"


class OptimizerResponse(BaseModel):
    ok: bool = True
    optimized_text: str = ""
    baseline_metrics: Dict[str, Any] = Field(default_factory=dict)
    final_metrics: Dict[str, Any] = Field(default_factory=dict)
    iterations: List[Dict[str, Any]] = Field(default_factory=list)
    applied_changes: int = 0
    optimization_mode: str = "balanced"
    error: str = ""