Primera versión del backend

backend/app/schemas/__init__.py

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+"""API schema package."""

backend/app/schemas/exam.py

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+import uuid
+from datetime import datetime
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+
+from app.models.exam import Difficulty, ExportFormat, QuestionType
+
+
+class QuestionTypeSettings(BaseModel):
+    type: QuestionType
+    count: int = Field(ge=1, le=200)
+    options_count: int | None = Field(default=None, ge=2, le=8)
+    multiple_correct: bool = False
+    score: float = Field(default=1.0, ge=0.0, le=100.0)
+    penalty: float = Field(default=0.0, ge=0.0, le=100.0)
+
+
+class ExamSettings(BaseModel):
+    question_types: list[QuestionTypeSettings] = Field(min_length=1, max_length=20)
+    shuffle_questions: bool = True
+    shuffle_answers: bool = True
+    include_feedback: bool = True
+
+
+class DifficultyProfile(BaseModel):
+    easy: int = Field(default=0, ge=0, le=500)
+    medium: int = Field(default=0, ge=0, le=500)
+    hard: int = Field(default=0, ge=0, le=500)
+    very_hard: int = Field(default=0, ge=0, le=500)
+
+    @model_validator(mode="after")
+    def require_at_least_one_question(self) -> "DifficultyProfile":
+        if self.easy + self.medium + self.hard + self.very_hard <= 0:
+            raise ValueError("At least one difficulty bucket must contain questions")
+        return self
+
+
+class ExamTemplateCreate(BaseModel):
+    title: str = Field(min_length=3, max_length=200)
+    subject: str = Field(min_length=2, max_length=200)
+    educational_level: str = Field(min_length=2, max_length=120)
+    language: str = Field(default="es", min_length=2, max_length=20)
+    settings: ExamSettings
+    difficulty_profile: DifficultyProfile
+
+
+class ExamTemplateRead(ExamTemplateCreate):
+    id: uuid.UUID
+    created_at: datetime
+    updated_at: datetime
+    question_count: int = 0
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class MatchingPair(BaseModel):
+    prompt: str = Field(min_length=1, max_length=1_000)
+    answer: str = Field(min_length=1, max_length=1_000)
+
+
+class QuestionCreate(BaseModel):
+    question_type: QuestionType
+    statement: str = Field(min_length=3, max_length=8_000)
+    correct_answers: list[str] = Field(min_length=1, max_length=20)
+    wrong_answers: list[str] = Field(default_factory=list, max_length=20)
+    matching_pairs: list[MatchingPair] = Field(default_factory=list, max_length=50)
+    difficulty: Difficulty = Difficulty.MEDIUM
+    score: float = Field(default=1.0, ge=0.0, le=100.0)
+    penalty: float = Field(default=0.0, ge=0.0, le=100.0)
+    options: dict[str, object] = Field(default_factory=dict)
+
+    @field_validator("correct_answers", "wrong_answers")
+    @classmethod
+    def strip_answers(cls, value: list[str]) -> list[str]:
+        return [answer.strip() for answer in value if answer.strip()]
+
+    @model_validator(mode="after")
+    def validate_question_payload(self) -> "QuestionCreate":
+        if self.question_type == QuestionType.MULTICHOICE and not self.wrong_answers:
+            raise ValueError("Multichoice questions require wrong_answers")
+        if self.question_type == QuestionType.TRUE_FALSE:
+            accepted = {"true", "false", "verdadero", "falso"}
+            if self.correct_answers[0].lower() not in accepted:
+                raise ValueError("True/false questions require a true or false correct answer")
+        if self.question_type == QuestionType.MATCHING and not self.matching_pairs:
+            raise ValueError("Matching questions require matching_pairs")
+        return self
+
+
+class QuestionRead(QuestionCreate):
+    id: uuid.UUID
+    template_id: uuid.UUID
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class PromptResponse(BaseModel):
+    template_id: uuid.UUID
+    prompt: str
+    expected_format: Literal["json"] = "json"
+
+
+class BuildPromptRequest(BaseModel):
+    topic_prompt: str = Field(min_length=5, max_length=4_000)
+
+
+class GenerateExamRequest(BaseModel):
+    template_id: uuid.UUID
+    topic_prompt: str = Field(min_length=5, max_length=4_000)
+
+
+class ParseRequest(BaseModel):
+    raw_output: str = Field(min_length=5, max_length=200_000)
+    input_format: Literal["json", "txt"]
+    template_id: uuid.UUID
+
+
+class ParsedQuestionsResponse(BaseModel):
+    questions: list[QuestionRead]
+
+
+class ExportResponse(BaseModel):
+    template_id: uuid.UUID
+    format: ExportFormat
+    content: str