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"""
Progress Tracking Service
Phase 4: Progress Analytics & Insights
This service handles:
- Calculating progress metrics
- Generating progress reports
- Providing personalized insights
- Tracking time spent and completion rates
"""
from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
from web_app import db
from web_app.models import UserLearningPath, LearningProgress, User
from src.ml.model_orchestrator import ModelOrchestrator
import json
class ProgressTracker:
"""
Tracks and analyzes user learning progress.
Metrics:
- Completion percentage
- Time spent
- Current milestone
- Estimated completion date
- Streak days
- Skills acquired
"""
def __init__(self):
"""Initialize the progress tracker."""
self.orchestrator = ModelOrchestrator()
def get_progress_summary(
self,
user_id: int,
learning_path_id: str
) -> Dict:
"""
Get comprehensive progress summary.
Args:
user_id: User ID
learning_path_id: Learning path ID
Returns:
Dictionary with progress metrics and insights
"""
# Get learning path
learning_path = UserLearningPath.query.filter_by(
id=learning_path_id,
user_id=user_id
).first()
if not learning_path:
return {
'error': 'Learning path not found'
}
path_data = learning_path.path_data_json
milestones = path_data.get('milestones', [])
# Get progress entries
progress_entries = LearningProgress.query.filter_by(
user_learning_path_id=learning_path_id
).all()
# Calculate metrics
completion_percentage = self._calculate_completion_percentage(
milestones,
progress_entries
)
time_spent = self._calculate_time_spent(progress_entries)
current_milestone = self._get_current_milestone(
milestones,
progress_entries
)
estimated_completion = self._estimate_completion_date(
learning_path,
progress_entries,
milestones
)
streak_days = self._calculate_streak(
user_id,
learning_path_id
)
skills_acquired = self._get_skills_acquired(
milestones,
progress_entries
)
pace_analysis = self._analyze_pace(
learning_path,
progress_entries,
milestones
)
# Generate personalized insights
insights = self._generate_insights(
completion_percentage=completion_percentage,
time_spent=time_spent,
pace_analysis=pace_analysis,
streak_days=streak_days,
path_data=path_data
)
return {
'completion_percentage': completion_percentage,
'completed_milestones': len([p for p in progress_entries if p.status == 'completed']),
'total_milestones': len(milestones),
'time_spent_hours': time_spent,
'current_milestone': current_milestone,
'estimated_completion_date': estimated_completion,
'streak_days': streak_days,
'skills_acquired': skills_acquired,
'pace_analysis': pace_analysis,
'insights': insights,
'learning_path_title': path_data.get('title', 'Unknown'),
'started_at': learning_path.created_at.isoformat() if learning_path.created_at else None
}
def _calculate_completion_percentage(
self,
milestones: List[Dict],
progress_entries: List[LearningProgress]
) -> float:
"""Calculate completion percentage."""
if not milestones:
return 0.0
completed_count = len([
p for p in progress_entries
if p.status == 'completed'
])
return round((completed_count / len(milestones)) * 100, 1)
def _calculate_time_spent(
self,
progress_entries: List[LearningProgress]
) -> float:
"""Calculate total time spent in hours."""
total_hours = 0.0
for progress in progress_entries:
if progress.started_at:
if progress.completed_at:
# Calculate time between start and completion
delta = progress.completed_at - progress.started_at
total_hours += delta.total_seconds() / 3600
elif progress.status == 'in_progress':
# Calculate time from start to now
delta = datetime.utcnow() - progress.started_at
total_hours += delta.total_seconds() / 3600
return round(total_hours, 1)
def _get_current_milestone(
self,
milestones: List[Dict],
progress_entries: List[LearningProgress]
) -> Optional[Dict]:
"""Get the current milestone user is working on."""
# Find first in_progress milestone
for progress in progress_entries:
if progress.status == 'in_progress':
milestone_index = int(progress.milestone_identifier)
if 0 <= milestone_index < len(milestones):
return {
'index': milestone_index,
'title': milestones[milestone_index].get('title', 'Unknown'),
'estimated_hours': milestones[milestone_index].get('estimated_hours', 0)
}
# If no in_progress, find first not_started
completed_indices = {
int(p.milestone_identifier)
for p in progress_entries
if p.status == 'completed'
}
for i, milestone in enumerate(milestones):
if i not in completed_indices:
return {
'index': i,
'title': milestone.get('title', 'Unknown'),
'estimated_hours': milestone.get('estimated_hours', 0)
}
return None
def _estimate_completion_date(
self,
learning_path: UserLearningPath,
progress_entries: List[LearningProgress],
milestones: List[Dict]
) -> Optional[str]:
"""Estimate completion date based on current pace."""
completed_count = len([p for p in progress_entries if p.status == 'completed'])
if completed_count == 0:
# No progress yet, use original duration
duration_weeks = learning_path.path_data_json.get('duration_weeks', 8)
estimated_date = learning_path.created_at + timedelta(weeks=duration_weeks)
return estimated_date.strftime('%Y-%m-%d')
# Calculate average time per milestone
total_time = self._calculate_time_spent(progress_entries)
avg_time_per_milestone = total_time / completed_count if completed_count > 0 else 0
# Estimate remaining time
remaining_milestones = len(milestones) - completed_count
estimated_remaining_hours = remaining_milestones * avg_time_per_milestone
# Convert to days (assuming 2 hours per day)
estimated_remaining_days = estimated_remaining_hours / 2
estimated_date = datetime.utcnow() + timedelta(days=estimated_remaining_days)
return estimated_date.strftime('%Y-%m-%d')
def _calculate_streak(
self,
user_id: int,
learning_path_id: str
) -> int:
"""Calculate current learning streak in days."""
# Get all progress updates ordered by date
progress_entries = LearningProgress.query.filter_by(
user_learning_path_id=learning_path_id
).order_by(LearningProgress.started_at.desc()).all()
if not progress_entries:
return 0
# Check for activity in the last 24 hours
yesterday = datetime.utcnow() - timedelta(days=1)
recent_activity = any(
p.started_at and p.started_at >= yesterday
for p in progress_entries
)
if not recent_activity:
return 0
# Count consecutive days with activity
streak = 1
current_date = datetime.utcnow().date()
for i in range(1, 365): # Max 365 days
check_date = current_date - timedelta(days=i)
has_activity = any(
p.started_at and p.started_at.date() == check_date
for p in progress_entries
)
if has_activity:
streak += 1
else:
break
return streak
def _get_skills_acquired(
self,
milestones: List[Dict],
progress_entries: List[LearningProgress]
) -> List[str]:
"""Get list of skills acquired from completed milestones."""
skills = []
completed_indices = {
int(p.milestone_identifier)
for p in progress_entries
if p.status == 'completed'
}
for i in completed_indices:
if 0 <= i < len(milestones):
milestone_skills = milestones[i].get('skills_gained', [])
skills.extend(milestone_skills)
return list(set(skills)) # Remove duplicates
def _analyze_pace(
self,
learning_path: UserLearningPath,
progress_entries: List[LearningProgress],
milestones: List[Dict]
) -> Dict:
"""Analyze learning pace compared to plan."""
# Calculate expected progress
days_since_start = (datetime.utcnow() - learning_path.created_at).days
duration_weeks = learning_path.path_data_json.get('duration_weeks', 8)
total_days = duration_weeks * 7
expected_percentage = min(100, (days_since_start / total_days) * 100)
# Calculate actual progress
actual_percentage = self._calculate_completion_percentage(milestones, progress_entries)
# Determine pace
pace_diff = actual_percentage - expected_percentage
if pace_diff > 10:
pace_status = 'ahead'
pace_description = f'ahead of schedule by {abs(int(pace_diff))}%'
elif pace_diff < -10:
pace_status = 'behind'
pace_description = f'behind schedule by {abs(int(pace_diff))}%'
else:
pace_status = 'on_track'
pace_description = 'on track'
return {
'status': pace_status,
'description': pace_description,
'expected_percentage': round(expected_percentage, 1),
'actual_percentage': actual_percentage,
'difference': round(pace_diff, 1)
}
def _generate_insights(
self,
completion_percentage: float,
time_spent: float,
pace_analysis: Dict,
streak_days: int,
path_data: Dict
) -> List[str]:
"""Generate personalized insights using AI."""
# Build context for AI
context = f"""
Learning Progress Analysis:
- Completion: {completion_percentage}%
- Time Spent: {time_spent} hours
- Pace: {pace_analysis['description']}
- Streak: {streak_days} days
- Path: {path_data.get('title', 'Unknown')}
- Total Milestones: {len(path_data.get('milestones', []))}
"""
prompt = f"""Generate 3-5 personalized, motivational insights for a learner based on their progress.
{context}
Provide insights that:
1. Acknowledge their progress
2. Motivate them to continue
3. Offer specific suggestions
4. Are encouraging and positive
Format as a JSON array of strings."""
try:
response = self.orchestrator.generate_response(
prompt=prompt,
temperature=0.7,
use_cache=False
)
# Try to parse as JSON array
if response.strip().startswith('['):
insights = json.loads(response)
return insights[:5] # Max 5 insights
else:
# Split by newlines and clean up
insights = [
line.strip('- •*').strip()
for line in response.split('\n')
if line.strip() and len(line.strip()) > 10
]
return insights[:5]
except Exception as e:
print(f"Insight generation error: {e}")
# Fallback insights
return self._generate_fallback_insights(
completion_percentage,
pace_analysis,
streak_days
)
def _generate_fallback_insights(
self,
completion_percentage: float,
pace_analysis: Dict,
streak_days: int
) -> List[str]:
"""Generate fallback insights without AI."""
insights = []
if completion_percentage > 0:
insights.append(f"Great start! You've completed {completion_percentage}% of your learning path.")
if pace_analysis['status'] == 'ahead':
insights.append(f"You're {pace_analysis['description']}! Keep up the excellent pace!")
elif pace_analysis['status'] == 'behind':
insights.append("Don't worry about the pace - consistent progress is what matters most!")
else:
insights.append("You're right on track with your learning schedule!")
if streak_days > 0:
insights.append(f"🔥 {streak_days}-day streak! Consistency is key to mastery.")
if completion_percentage < 25:
insights.append("The beginning is always the hardest. You've got this!")
elif completion_percentage > 75:
insights.append("You're in the home stretch! Finish strong!")
return insights
def update_milestone_progress(
self,
user_id: int,
learning_path_id: str,
milestone_identifier: str,
status: str,
notes: Optional[str] = None
) -> Dict:
"""
Update progress for a specific milestone.
Args:
user_id: User ID
learning_path_id: Learning path ID
milestone_identifier: Milestone identifier
status: New status ('not_started', 'in_progress', 'completed')
notes: Optional notes
Returns:
Result dictionary
"""
# Get or create progress entry
progress = LearningProgress.query.filter_by(
user_learning_path_id=learning_path_id,
milestone_identifier=milestone_identifier
).first()
if not progress:
progress = LearningProgress(
user_learning_path_id=learning_path_id,
milestone_identifier=milestone_identifier
)
db.session.add(progress)
# Update status
old_status = progress.status
progress.status = status
if notes:
progress.notes = notes
# Update timestamps
if status == 'in_progress' and not progress.started_at:
progress.started_at = datetime.utcnow()
elif status == 'completed' and not progress.completed_at:
progress.completed_at = datetime.utcnow()
db.session.commit()
return {
'success': True,
'old_status': old_status,
'new_status': status,
'milestone_identifier': milestone_identifier
}
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