app/schemas.py

"""Pydantic schemas untuk request/response."""
from typing import List, Dict, Optional
from pydantic import BaseModel


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


# === Shared request ===

class TextItemsRequest(BaseModel):
    items: List[TextItem]


# === Sentiment ===

class SentimentRequest(BaseModel):
    items: List[TextItem]


class SentimentResult(BaseModel):
    id: int
    sentiment: str
    score: float
    confidence: float


class SentimentResponse(BaseModel):
    results: List[SentimentResult]
    model_mode: str


# === Summarize ===

class SummarizeRequest(BaseModel):
    text: str
    sentences: int = 3


class SummarizeResponse(BaseModel):
    summary: str
    sentences: List[str]


# === Topics ===

class TopicRequest(BaseModel):
    items: List[TextItem]
    num_topics: int = 8


class TopicCluster(BaseModel):
    topic_id: int
    label: str
    keywords: List[str]
    article_ids: List[int]
    size: int


class TopicResponse(BaseModel):
    topics: List[TopicCluster]
    model_mode: str


# === Similarity ===

class SimilarityRequest(BaseModel):
    items: List[TextItem]
    threshold: float = 0.3


class SimilarityPair(BaseModel):
    id_a: int
    id_b: int
    score: float


class SimilarityResponse(BaseModel):
    pairs: List[SimilarityPair]


# === Emotion ===

class EmotionResult(BaseModel):
    id: int
    emotions: Dict[str, float]
    dominant_emotion: str
    dominant_score: float


class EmotionResponse(BaseModel):
    results: List[EmotionResult]


# === Framing ===

class FramingResult(BaseModel):
    id: int
    frames: Dict[str, float]
    dominant_frame: str
    score: float


class FramingResponse(BaseModel):
    results: List[FramingResult]


# === Fake Score ===

class FakeScoreResult(BaseModel):
    id: int
    score: int
    level: str
    reasons: List[str]


class FakeScoreResponse(BaseModel):
    results: List[FakeScoreResult]


# === Opinion vs Fact ===

class OpinionFactResult(BaseModel):
    id: int
    classification: str
    opinion_pct: int
    fact_pct: int
    confidence: float


class OpinionFactResponse(BaseModel):
    results: List[OpinionFactResult]


# === Keywords ===

class KeywordItem(BaseModel):
    keyword: str
    score: float
    count: int


class KeywordsResult(BaseModel):
    id: int
    keywords: List[KeywordItem]


class KeywordsResponse(BaseModel):
    results: List[KeywordsResult]


# === NER ===

class EntitiesMap(BaseModel):
    persons: List[str]
    organizations: List[str]
    locations: List[str]


class NerResult(BaseModel):
    id: int
    entities: EntitiesMap


class NerResponse(BaseModel):
    results: List[NerResult]


# === Digest ===

class DigestRequest(BaseModel):
    items: List[TextItem]
    project_name: str = ""


class DigestTopicItem(BaseModel):
    topic: str
    count: int


class DigestResponse(BaseModel):
    summary: str
    top_topics: List[DigestTopicItem]
    key_titles: List[str]
    article_count: int