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

# 1. Student Level Detection
class DiagnosticQuestion(BaseModel):
    id: str
    question: str
    options: List[str]
    correct_answer_index: int
    category: Literal["concept", "memory", "diagram", "answer_writing", "problem_solving"]

class DiagnosticQuiz(BaseModel):
    id: str
    document_id: str
    questions: List[DiagnosticQuestion]

class DiagnosticAnswer(BaseModel):
    question_id: str
    answer_index: int

class DiagnosticResultRequest(BaseModel):
    document_id: str
    answers: List[DiagnosticAnswer]

class DiagnosticResultResponse(BaseModel):
    student_level: Literal["beginner", "intermediate", "advanced"]
    weak_areas: List[str] # concept, memory, etc.
    analysis: str # Why this level?

# 2. What To Study Engine
class TopicItem(BaseModel):
    topic: str
    reason: str # Trust layer: "based on PYQ", "based on source"

class WhatToStudyResponse(BaseModel):
    must_study_topics: List[TopicItem]
    high_weightage_topics: List[TopicItem]
    repeated_pyq_topics: List[TopicItem]
    low_priority_topics: List[TopicItem]
    skip_for_now_topics: List[TopicItem]
    revision_keywords: List[str]
    easy_marks: List[TopicItem]
    danger_areas: List[TopicItem]
    study_order: List[str]

# 3. Personal Study Path Engine
class StudyTimeBlock(BaseModel):
    time_block: str # e.g., "00:00 - 00:30"
    topic: str
    reason: str
    task: str
    output_expected: str
    revision_checkpoint: str

class StudyPathResponse(BaseModel):
    plan_type: Literal["1h", "3h", "5h", "7d", "30d"]
    blocks: List[StudyTimeBlock]
    total_estimated_coverage: str

# 4. Exam Intelligence Output
class MarkWiseAnswer(BaseModel):
    question: str
    marks: int
    answer_structure: str
    key_points: List[str]
    diagram_suggested: bool

class ExamIntelligenceResponse(BaseModel):
    chapter: str
    topic_importance: str # High/Medium/Low
    pyq_pattern: str
    likely_question_types: List[str]
    answers: List[MarkWiseAnswer]
    common_mistakes: List[str]
    keywords_to_underline: List[str]

# 5. Video Tutor Brain (Extension)
class VideoScene(BaseModel):
    scene_number: int
    label: str
    script_text: str = ""  # renamed from 'copy' which shadows BaseModel.copy
    visual_suggestion: str

class VideoPlanResponse(BaseModel):
    title: str
    duration_type: Literal["quick_concept", "exam_focus", "deep_masterclass", "full_chapter_war_mode"]
    style: Literal["normal_teacher", "visual_tutor", "anime_tutor", "malayalam_english_tutor", "exam_war_mode"]
    hook: str
    analogy: str
    sections: List[VideoScene]
    mini_quiz: List[DiagnosticQuestion]
    recap: str