cursor-lens/schemas.py at main · EbookHun/cursor-lens · GitHub

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from datetime import datetime
from typing import Literal

from pydantic import BaseModel, ConfigDict, Field, field_validator


class StrictModel(BaseModel):
    model_config = ConfigDict(extra="forbid")


class CommunityPost(StrictModel):
    post_id: str = Field(min_length=1, max_length=100)
    subreddit: str = Field(min_length=1, max_length=100)
    title: str = Field(min_length=1, max_length=500)
    body: str = Field(max_length=20_000)
    author_karma: int | None = None
    author_account_age_days: int | None = Field(default=None, ge=0)
    posting_frequency: str | None = Field(default=None, min_length=1, max_length=100)
    upvote_ratio: float | None = Field(default=None, ge=0, le=1)
    score: int
    community_flags: int | None = Field(default=None, ge=0)
    num_comments: int | None = Field(default=None, ge=0)
    url: str | None = None
    created_at: datetime

    @field_validator("created_at")
    @classmethod
    def require_timezone(cls, value):
        if value.tzinfo is None:
            raise ValueError("created_at must include a timezone")
        return value


class ClassifierOutput(StrictModel):
    engagement_score: float = Field(ge=0, le=1)
    engagement_tier: Literal["low", "medium", "high"]
    score_signal: float = Field(ge=0, le=1)
    comment_signal: float = Field(ge=0, le=1)
    reasoning: str = Field(min_length=1, max_length=2_000)


class ClassificationResult(ClassifierOutput):
    post_id: str


class PolicyResult(StrictModel):
    post_id: str
    status: Literal["pass", "review", "reject"]
    passes_filter: bool
    filter_reason: str
    policy_citation: Literal["Policy 1.1", "Policy 1.2", "Policy 1.3"]
    confidence: Literal["high", "medium", "low"]


class TrendCandidate(StrictModel):
    topic: str
    post_ids: list[str]
    representative_excerpt: str
    sentiment: Literal["positive", "negative", "neutral", "mixed"]


class TrendCandidateOutput(StrictModel):
    topic_clusters: list[TrendCandidate]
    dominant_theme: str


class TopicCluster(TrendCandidate):
    post_count: int = Field(ge=0)
    support_share: float = Field(ge=0, le=1)
    active_week_buckets: list[int]
    recurrence_ratio: float = Field(ge=0, le=1)
    status: Literal["signal", "trend"]


class TrendOutput(StrictModel):
    topic_clusters: list[TopicCluster]
    dominant_theme: str
    confirmed_trend_count: int = Field(ge=0)
    emerging_flag: bool


class Alert(StrictModel):
    alert_type: str
    severity: Literal["low", "medium", "high"]
    summary: str
    recommended_action: str


class LensOutput(StrictModel):
    community_health_index: float = Field(ge=0, le=100)
    engagement_weighted_sentiment: float = Field(ge=-1, le=1)
    dominant_narrative: str
    signal_quality_score: float = Field(ge=0, le=1)
    alerts: list[Alert]
    cmo_briefing: str


class AppealRequest(StrictModel):
    post_id: str = Field(min_length=1, max_length=100)
    appellant: str = Field(min_length=1, max_length=100)
    rationale: str = Field(min_length=1, max_length=2_000)


class AppealRecord(AppealRequest):
    appeal_id: str = Field(min_length=1, max_length=100)
    policy_status_at_submission: Literal["pass", "review", "reject"]
    review_status: Literal["pending", "accepted", "denied"] = "pending"