# TutorX MCP Server
from mcp.server.fastmcp import FastMCP
import json
from typing import List, Dict, Any, Optional
from datetime import datetime

# Import utility functions
from utils.multimodal import (
    process_text_query,
    process_voice_input,
    process_handwriting,
    generate_speech_response
)
from utils.assessment import (
    generate_question,
    evaluate_student_answer,
    generate_performance_analytics,
    detect_plagiarism
)

# Create the TutorX MCP server
mcp = FastMCP("TutorX")

# ------------------ Core Features ------------------

# Adaptive Learning Engine
@mcp.tool()
def assess_skill(student_id: str, concept_id: str) -> Dict[str, Any]:
    """
    Assess student's skill level on a specific concept
    
    Args:
        student_id: The unique identifier for the student
        concept_id: The concept to assess
        
    Returns:
        Dictionary containing skill level and recommendations
    """
    # Simulated skill assessment
    return {
        "student_id": student_id,
        "concept_id": concept_id,
        "skill_level": 0.75,
        "confidence": 0.85,
        "recommendations": [
            "Practice more complex problems",
            "Review related concept: algebra_linear_equations"
        ],
        "timestamp": datetime.now().isoformat()
    }

@mcp.resource("concept-graph://")
def get_concept_graph() -> Dict[str, Any]:
    """Get the full knowledge concept graph"""
    return {
        "nodes": [
            {"id": "math_algebra_basics", "name": "Algebra Basics", "difficulty": 1},
            {"id": "math_algebra_linear_equations", "name": "Linear Equations", "difficulty": 2},
            {"id": "math_algebra_quadratic_equations", "name": "Quadratic Equations", "difficulty": 3},
        ],
        "edges": [
            {"from": "math_algebra_basics", "to": "math_algebra_linear_equations", "weight": 1.0},
            {"from": "math_algebra_linear_equations", "to": "math_algebra_quadratic_equations", "weight": 0.8},
        ]
    }

@mcp.resource("learning-path://{student_id}")
def get_learning_path(student_id: str) -> Dict[str, Any]:
    """Get personalized learning path for a student"""
    return {
        "student_id": student_id,
        "current_concepts": ["math_algebra_linear_equations"],
        "recommended_next": ["math_algebra_quadratic_equations"],
        "mastered": ["math_algebra_basics"],
        "estimated_completion_time": "2 weeks"
    }

# Assessment Suite
@mcp.tool()
def generate_quiz(concept_ids: List[str], difficulty: int = 2) -> Dict[str, Any]:
    """
    Generate a quiz based on specified concepts and difficulty
    
    Args:
        concept_ids: List of concept IDs to include in the quiz
        difficulty: Difficulty level from 1-5
        
    Returns:
        Quiz object with questions and answers
    """
    return {
        "quiz_id": "q12345",
        "concept_ids": concept_ids,
        "difficulty": difficulty,
        "questions": [
            {
                "id": "q1",
                "text": "Solve for x: 2x + 3 = 7",
                "type": "algebraic_equation",
                "answer": "x = 2",
                "solution_steps": [
                    "2x + 3 = 7",
                    "2x = 7 - 3",
                    "2x = 4",
                    "x = 4/2 = 2"
                ]
            }
        ]
    }

# Feedback System
@mcp.tool()
def analyze_error_patterns(student_id: str, concept_id: str) -> Dict[str, Any]:
    """
    Analyze common error patterns for a student on a specific concept
    
    Args:
        student_id: The student's unique identifier
        concept_id: The concept to analyze
        
    Returns:
        Error pattern analysis
    """
    return {
        "student_id": student_id,
        "concept_id": concept_id,
        "common_errors": [
            {
                "type": "sign_error",
                "frequency": 0.65,
                "example": "2x - 3 = 5 → 2x = 5 - 3 → 2x = 2 → x = 1 (should be x = 4)"
            },
            {
                "type": "arithmetic_error",
                "frequency": 0.35,
                "example": "2x = 8 → x = 8/2 = 3 (should be x = 4)"
            }
        ],
        "recommendations": [
            "Practice more sign manipulation problems",
            "Review basic arithmetic operations"
        ]
    }

# ------------------ Advanced Features ------------------

# Neurological Engagement Monitor
@mcp.tool()
def analyze_cognitive_state(eeg_data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Analyze EEG data to determine cognitive state
    
    Args:
        eeg_data: Raw or processed EEG data
        
    Returns:
        Analysis of cognitive state
    """
    return {
        "attention_level": 0.82,
        "cognitive_load": 0.65,
        "stress_level": 0.25,
        "recommendations": [
            "Student is engaged but approaching cognitive overload",
            "Consider simplifying next problems slightly"
        ],
        "timestamp": datetime.now().isoformat()
    }

# Cross-Institutional Knowledge Fusion
@mcp.resource("curriculum-standards://{country_code}")
def get_curriculum_standards(country_code: str) -> Dict[str, Any]:
    """Get curriculum standards for a specific country"""
    standards = {
        "us": {
            "name": "Common Core State Standards",
            "math_standards": {
                "algebra_1": [
                    "CCSS.Math.Content.HSA.CED.A.1",
                    "CCSS.Math.Content.HSA.CED.A.2"
                ]
            }
        },
        "uk": {
            "name": "National Curriculum",
            "math_standards": {
                "algebra_1": [
                    "KS3.Algebra.1",
                    "KS3.Algebra.2"
                ]
            }
        }
    }
    
    return standards.get(country_code.lower(), {"error": "Country code not found"})

@mcp.tool()
def align_content_to_standard(content_id: str, standard_id: str) -> Dict[str, Any]:
    """
    Align educational content to a specific curriculum standard
    
    Args:
        content_id: The ID of the content to align
        standard_id: The curriculum standard ID
        
    Returns:
        Alignment information and recommendations
    """
    return {
        "content_id": content_id,
        "standard_id": standard_id,
        "alignment_score": 0.85,
        "gaps": [
            "Missing coverage of polynomial division",
            "Should include more word problems"
        ],
        "recommendations": [
            "Add 2-3 examples of polynomial division",
            "Convert 30% of problems to word problems"
        ]
    }

# Automated Lesson Authoring
@mcp.tool()
def generate_lesson(topic: str, grade_level: int, duration_minutes: int = 45) -> Dict[str, Any]:
    """
    Generate a complete lesson plan on a topic
    
    Args:
        topic: The lesson topic
        grade_level: Target grade level (K-12)
        duration_minutes: Lesson duration in minutes
        
    Returns:
        Complete lesson plan
    """
    return {
        "topic": topic,
        "grade_level": grade_level,
        "duration_minutes": duration_minutes,
        "objectives": [
            "Students will be able to solve linear equations in one variable",
            "Students will be able to check their solutions"
        ],
        "materials": [
            "Whiteboard/projector",
            "Handouts with practice problems",
            "Graphing calculators (optional)"
        ],
        "activities": [
            {
                "name": "Warm-up",
                "duration_minutes": 5,
                "description": "Review of pre-algebra concepts needed for today's lesson"
            },
            {
                "name": "Direct Instruction",
                "duration_minutes": 15,
                "description": "Teacher demonstrates solving linear equations step by step"
            },
            {
                "name": "Guided Practice",
                "duration_minutes": 10,
                "description": "Students solve problems with teacher guidance"
            },
            {
                "name": "Independent Practice",
                "duration_minutes": 10,
                "description": "Students solve problems independently"
            },
            {
                "name": "Closure",
                "duration_minutes": 5,
                "description": "Review key concepts and preview next lesson"
            }
        ],
        "assessment": {
            "formative": "Teacher observation during guided and independent practice",
            "summative": "Exit ticket with 3 problems to solve"
        },
        "differentiation": {
            "struggling": "Provide equation-solving steps reference sheet",
            "advanced": "Offer multi-step equations with fractions and decimals"
        }
    }

# ------------------ User Experience Features ------------------

@mcp.resource("student-dashboard://{student_id}")
def get_student_dashboard(student_id: str) -> Dict[str, Any]:
    """Get dashboard data for a specific student"""
    return {
        "student_id": student_id,
        "knowledge_map": {
            "mastery_percentage": 68,
            "concepts_mastered": 42,
            "concepts_in_progress": 15,
            "concepts_not_started": 25
        },
        "recent_activity": [
            {
                "timestamp": "2025-06-06T15:30:00Z",
                "activity_type": "quiz",
                "description": "Algebra Quiz #3",
                "performance": "85%"
            },
            {
                "timestamp": "2025-06-05T13:45:00Z",
                "activity_type": "lesson",
                "description": "Quadratic Equations Introduction",
                "duration_minutes": 32
            }
        ],
        "recommendations": [
            "Complete Factor Polynomials practice set",
            "Review Linear Systems interactive module"
        ]
    }

@mcp.tool()
def get_accessibility_settings(student_id: str) -> Dict[str, Any]:
    """
    Get accessibility settings for a student
    
    Args:
        student_id: The student's unique identifier
        
    Returns:
        Accessibility settings
    """
    return {
        "student_id": student_id,
        "text_to_speech_enabled": True,
        "font_size": "large",
        "high_contrast_mode": False,
        "screen_reader_compatible": True,
        "keyboard_navigation_enabled": True
    }

@mcp.tool()
def update_accessibility_settings(student_id: str, settings: Dict[str, Any]) -> Dict[str, Any]:
    """
    Update accessibility settings for a student
    
    Args:
        student_id: The student's unique identifier
        settings: Dictionary of settings to update
        
    Returns:
        Updated accessibility settings
    """
    # In a real implementation, this would update a database
    return {
        "student_id": student_id,
        "text_to_speech_enabled": settings.get("text_to_speech_enabled", True),
        "font_size": settings.get("font_size", "large"),
        "high_contrast_mode": settings.get("high_contrast_mode", False),
        "screen_reader_compatible": settings.get("screen_reader_compatible", True),
        "keyboard_navigation_enabled": settings.get("keyboard_navigation_enabled", True),
        "updated_at": datetime.now().isoformat()
    }

# ------------------ Multi-Modal Interaction ------------------

@mcp.tool()
def text_interaction(query: str, student_id: str, session_context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
    """
    Process a text query from the student
    
    Args:
        query: The text query from the student
        student_id: The student's unique identifier
        session_context: Optional context about the current session
        
    Returns:
        Processed response
    """
    # Add student information to context
    context = session_context or {}
    context["student_id"] = student_id
    
    return process_text_query(query, context)

@mcp.tool()
def voice_interaction(audio_data_base64: str, student_id: str) -> Dict[str, Any]:
    """
    Process voice input from the student
    
    Args:
        audio_data_base64: Base64 encoded audio data
        student_id: The student's unique identifier
        
    Returns:
        Transcription and response
    """
    # Process voice input
    result = process_voice_input(audio_data_base64)
    
    # Process the transcription as a text query
    text_response = process_text_query(result["transcription"], {"student_id": student_id})
    
    # Generate speech response
    speech_response = generate_speech_response(
        text_response["response"],
        {"voice_id": "educational_tutor"}
    )
    
    # Combine results
    return {
        "input_transcription": result["transcription"],
        "input_confidence": result["confidence"],
        "detected_emotions": result.get("detected_emotions", {}),
        "text_response": text_response["response"],
        "speech_response": speech_response,
        "timestamp": datetime.now().isoformat()
    }

@mcp.tool()
def handwriting_recognition(image_data_base64: str, student_id: str) -> Dict[str, Any]:
    """
    Process handwritten input from the student
    
    Args:
        image_data_base64: Base64 encoded image data of handwriting
        student_id: The student's unique identifier
        
    Returns:
        Transcription and analysis
    """
    # Process handwriting input
    result = process_handwriting(image_data_base64)
    
    # If it's a math equation, solve it
    if result["detected_content_type"] == "math_equation":
        # In a real implementation, this would use a math engine to solve the equation
        # For demonstration, we'll provide a simulated solution
        if result["equation_type"] == "quadratic":
            solution = {
                "equation": result["transcription"],
                "solution_steps": [
                    "x^2 + 5x + 6 = 0",
                    "Factor: (x + 2)(x + 3) = 0",
                    "x + 2 = 0 or x + 3 = 0",
                    "x = -2 or x = -3"
                ],
                "solutions": [-2, -3]
            }
        else:
            solution = {
                "equation": result["transcription"],
                "note": "Solution not implemented for this equation type"
            }
    else:
        solution = None
    
    return {
        "transcription": result["transcription"],
        "confidence": result["confidence"],
        "detected_content_type": result["detected_content_type"],
        "solution": solution,
        "timestamp": datetime.now().isoformat()
    }

# ------------------ Advanced Assessment Tools ------------------

@mcp.tool()
def create_assessment(concept_ids: List[str], num_questions: int, difficulty: int = 3) -> Dict[str, Any]:
    """
    Create a complete assessment for given concepts
    
    Args:
        concept_ids: List of concept IDs to include
        num_questions: Number of questions to generate
        difficulty: Difficulty level (1-5)
        
    Returns:
        Complete assessment with questions
    """
    questions = []
    
    # Distribute questions evenly among concepts
    questions_per_concept = num_questions // len(concept_ids)
    extra_questions = num_questions % len(concept_ids)
    
    for i, concept_id in enumerate(concept_ids):
        # Determine how many questions for this concept
        concept_questions = questions_per_concept
        if i < extra_questions:
            concept_questions += 1
        
        # Generate questions for this concept
        for _ in range(concept_questions):
            questions.append(generate_question(concept_id, difficulty))
    
    return {
        "assessment_id": f"assessment_{datetime.now().strftime('%Y%m%d%H%M%S')}",
        "concept_ids": concept_ids,
        "difficulty": difficulty,
        "num_questions": len(questions),
        "questions": questions,
        "created_at": datetime.now().isoformat()
    }

@mcp.tool()
def grade_assessment(assessment_id: str, student_answers: Dict[str, str], questions: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Grade a completed assessment
    
    Args:
        assessment_id: The ID of the assessment
        student_answers: Dictionary mapping question IDs to student answers
        questions: List of question objects
        
    Returns:
        Grading results
    """
    results = []
    correct_count = 0
    
    for question in questions:
        question_id = question["id"]
        if question_id in student_answers:
            evaluation = evaluate_student_answer(question, student_answers[question_id])
            results.append(evaluation)
            if evaluation["is_correct"]:
                correct_count += 1
    
    # Calculate score
    score = correct_count / len(questions) if questions else 0
    
    # Analyze error patterns
    error_types = {}
    for result in results:
        if result["error_type"]:
            error_type = result["error_type"]
            error_types[error_type] = error_types.get(error_type, 0) + 1
    
    # Find most common error
    most_common_error = None
    if error_types:
        most_common_error = max(error_types.items(), key=lambda x: x[1])
    
    return {
        "assessment_id": assessment_id,
        "score": score,
        "correct_count": correct_count,
        "total_questions": len(questions),
        "results": results,
        "most_common_error": most_common_error,
        "completed_at": datetime.now().isoformat()
    }

@mcp.tool()
def get_student_analytics(student_id: str, timeframe_days: int = 30) -> Dict[str, Any]:
    """
    Get comprehensive analytics for a student
    
    Args:
        student_id: The student's unique identifier
        timeframe_days: Number of days to include in analysis
        
    Returns:
        Performance analytics
    """
    return generate_performance_analytics(student_id, timeframe_days)

@mcp.tool()
def check_submission_originality(submission: str, reference_sources: List[str]) -> Dict[str, Any]:
    """
    Check student submission for potential plagiarism
    
    Args:
        submission: The student's submission text
        reference_sources: List of reference texts to check against
        
    Returns:
        Originality analysis
    """
    return detect_plagiarism(submission, reference_sources)

if __name__ == "__main__":
    mcp.run()