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"""
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)
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