mcp_server/tools/learning_path_tools.py

"""
Learning path generation tools for TutorX.
"""
import random
from typing import Dict, Any, List, Optional
from datetime import datetime, timedelta
import sys
import os
from pathlib import Path

# Add the parent directory to the Python path
current_dir = Path(__file__).parent
parent_dir = current_dir.parent.parent
sys.path.insert(0, str(parent_dir))

import sys
import os
from pathlib import Path

# Add the parent directory to the Python path
current_dir = Path(__file__).parent
parent_dir = current_dir.parent
sys.path.insert(0, str(parent_dir))

# Import from local resources
from resources.concept_graph import CONCEPT_GRAPH

# Import MCP
from mcp_server.mcp_instance import mcp

def get_prerequisites(concept_id: str, visited: Optional[set] = None) -> List[Dict[str, Any]]:
    """
    Get all prerequisites for a concept recursively.
    
    Args:
        concept_id: ID of the concept to get prerequisites for
        visited: Set of already visited concepts to avoid cycles
        
    Returns:
        List of prerequisite concepts in order
    """
    if visited is None:
        visited = set()
    
    if concept_id not in CONCEPT_GRAPH or concept_id in visited:
        return []
    
    visited.add(concept_id)
    prerequisites = []
    
    # Get direct prerequisites
    for prereq_id in CONCEPT_GRAPH[concept_id].get("prerequisites", []):
        if prereq_id in CONCEPT_GRAPH and prereq_id not in visited:
            prerequisites.extend(get_prerequisites(prereq_id, visited))
    
    # Add the current concept
    prerequisites.append(CONCEPT_GRAPH[concept_id])
    return prerequisites

def generate_learning_path(concept_ids: List[str], student_level: str = "beginner") -> Dict[str, Any]:
    """
    Generate a personalized learning path for a student.
    
    Args:
        concept_ids: List of concept IDs to include in the learning path
        student_level: Student's current level (beginner, intermediate, advanced)
        
    Returns:
        Dictionary containing the learning path
    """
    if not concept_ids:
        return {"error": "At least one concept ID is required"}
    
    # Get all prerequisites for each concept
    all_prerequisites = []
    visited = set()
    
    for concept_id in concept_ids:
        if concept_id in CONCEPT_GRAPH:
            prereqs = get_prerequisites(concept_id, visited)
            all_prerequisites.extend(prereqs)
    
    # Remove duplicates while preserving order
    unique_concepts = []
    seen = set()
    for concept in all_prerequisites:
        if concept["id"] not in seen:
            seen.add(concept["id"])
            unique_concepts.append(concept)
    
    # Add any target concepts not already in the path
    for concept_id in concept_ids:
        if concept_id in CONCEPT_GRAPH and concept_id not in seen:
            unique_concepts.append(CONCEPT_GRAPH[concept_id])
    
    # Estimate time required for each concept based on student level
    time_estimates = {
        "beginner": {"min": 30, "max": 60},    # 30-60 minutes per concept
        "intermediate": {"min": 20, "max": 45},  # 20-45 minutes per concept
        "advanced": {"min": 15, "max": 30}      # 15-30 minutes per concept
    }
    
    level = student_level.lower()
    if level not in time_estimates:
        level = "beginner"
    
    time_min = time_estimates[level]["min"]
    time_max = time_estimates[level]["max"]
    
    # Generate learning path with estimated times
    learning_path = []
    total_minutes = 0
    
    for i, concept in enumerate(unique_concepts, 1):
        # Random time estimate within range
        minutes = random.randint(time_min, time_max)
        total_minutes += minutes
        
        learning_path.append({
            "step": i,
            "concept_id": concept["id"],
            "concept_name": concept["name"],
            "description": concept.get("description", ""),
            "estimated_time_minutes": minutes,
            "resources": [
                f"Video tutorial on {concept['name']}",
                f"{concept['name']} documentation",
                f"Practice exercises for {concept['name']}"
            ]
        })
    
    # Calculate total time
    hours, minutes = divmod(total_minutes, 60)
    total_time = f"{hours}h {minutes}m" if hours > 0 else f"{minutes}m"
    
    return {
        "learning_path": learning_path,
        "total_steps": len(learning_path),
        "total_time_minutes": total_minutes,
        "total_time_display": total_time,
        "student_level": student_level,
        "generated_at": datetime.utcnow().isoformat() + "Z"
    }

@mcp.tool()
async def get_learning_path(student_id: str, concept_ids: List[str], student_level: Optional[str] = None) -> Dict[str, Any]:
    """
    Generate a personalized learning path for a student.
    
    Args:
        student_id: Unique identifier for the student
        concept_ids: List of concept IDs to include in the learning path
        student_level: Optional student level (beginner, intermediate, advanced)
        
    Returns:
        Dictionary containing the learning path
    """
    # In a real implementation, we would look up the student's level from their profile
    if not student_level:
        student_level = "beginner"  # Default level
    
    return generate_learning_path(concept_ids, student_level)