app/services/intelligence_brain.py

from typing import Any, List, Optional, Literal
from app.services.ai_provider import BaseAIProvider
from app.schemas.intelligence import (
    DiagnosticQuiz, 
    DiagnosticResultResponse, 
    WhatToStudyResponse, 
    StudyPathResponse, 
    ExamIntelligenceResponse,
    VideoPlanResponse
)

class ExamIntelligenceBrain:
    def __init__(self, provider: BaseAIProvider):
        self.provider = provider

    def generate_diagnostic_quiz(
        self, 
        context: str, 
        document_id: str,
        language: str = "English"
    ) -> dict[str, Any]:
        task = (
            "Generate a diagnostic study quiz with 5-7 questions to assess a student's level. "
            "Questions must cover: concept understanding, memory (definitions), diagram recall, "
            "answer writing logic, and problem solving. Each question must have 4 options. "
            "Assign a category to each question from: concept, memory, diagram, answer_writing, problem_solving."
        )
        return self.provider.generate_json(
            task=task,
            context=context,
            language=language,
            metadata={"document_id": document_id},
            response_schema=DiagnosticQuiz,
        )

    def analyze_diagnostic_results(
        self,
        results: dict[str, Any],
        language: str = "English"
    ) -> dict[str, Any]:
        # This one might be a simple logic if we want to save tokens, 
        # but let's use the Brain for deeper analysis.
        task = (
            "Analyze the following student diagnostic quiz results and classify their level. "
            "Calculate their accuracy per category (concept, memory, etc.) and identify specific weaknesses. "
            "Determine if they are 'beginner', 'intermediate', or 'advanced'. "
            "Explain why in the 'analysis' field."
        )
        return self.provider.generate_json(
            task=task,
            context=f"Results: {results}",
            language=language,
            response_schema=DiagnosticResultResponse,
        )

    def generate_what_to_study(
        self,
        context: str,
        student_level: str,
        goal: str,
        pyq_context: Optional[str] = None,
        language: str = "English"
    ) -> dict[str, Any]:
        task = (
            f"As an Exam Intelligence Brain, determine what a {student_level} student aiming for '{goal}' should study. "
            "Categorize topics into: must study, high weightage, repeated PYQ topics, low priority, and skip for now. "
            "Identify 'easy marks' (simple but high value) and 'danger areas' (frequent mistake spots). "
            "Provide a logical 'study_order'. "
            "For every topic, include a 'reason' explaining if it is based on the source, syllabus, or PYQ. "
            f"Available PYQ data: {pyq_context or 'Not available yet'}. "
            "If PYQ data is missing, prioritize based on source structure and typical board patterns."
        )
        return self.provider.generate_json(
            task=task,
            context=context,
            language=language,
            response_schema=WhatToStudyResponse,
        )

    def generate_study_path(
        self,
        context: str,
        plan_type: str,
        student_level: str,
        goal: str,
        language: str = "English"
    ) -> dict[str, Any]:
        task = (
            f"Create a high-density personal study path for a {plan_type} timeframe. "
            f"Target: {goal} for a {student_level} student. "
            "Break the plan into logical time blocks. Each block must have: "
            "time_block, topic, reason, task, output_expected, and revision_checkpoint. "
            "The plan must be realistic for the given time (1h, 3h, 5h, 7d, or 30d)."
        )
        return self.provider.generate_json(
            task=task,
            context=context,
            language=language,
            response_schema=StudyPathResponse,
        )

    def generate_exam_intelligence(
        self,
        context: str,
        chapter_name: str,
        language: str = "English"
    ) -> dict[str, Any]:
        task = (
            f"Generate deep exam intelligence for the chapter: {chapter_name}. "
            "Include topic importance, PYQ patterns, likely question types, and "
            "mark-wise structured answers (1, 2, 4, 6 marks). "
            "List common mistakes and keywords that must be underlined in the exam."
        )
        return self.provider.generate_json(
            task=task,
            context=context,
            language=language,
            response_schema=ExamIntelligenceResponse,
        )

    def generate_extended_video_plan(
        self,
        context: str,
        title: str,
        duration_type: str,
        style: str,
        language: str = "English"
    ) -> dict[str, Any]:
        task = (
            f"Create an extended video teaching plan for '{title}'. "
            f"Duration category: {duration_type}. Style: {style}. "
            "The plan must include: hook, real-life analogy, simple explanation, official terms, "
            "visual suggestions for every scene, PYQ connection, exam answer format, common mistakes, "
            "a 3-question mini-quiz, and a final recap."
        )
        return self.provider.generate_json(
            task=task,
            context=context,
            language=language,
            response_schema=VideoPlanResponse,
        )