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from datetime import datetime
from flask_sqlalchemy import SQLAlchemy
from flask_login import UserMixin
from werkzeug.security import generate_password_hash, check_password_hash
# Assuming db and login_manager are initialized in __init__.py
from web_app import db, login_manager
from flask_dance.consumer.storage.sqla import OAuthConsumerMixin
import uuid
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class User(UserMixin, db.Model):
__tablename__ = 'users'
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(64), index=True,
unique=True, nullable=False)
email = db.Column(db.String(120), index=True, unique=True, nullable=False)
password_hash = db.Column(db.String(256))
created_at = db.Column(db.DateTime, default=datetime.utcnow)
last_seen = db.Column(db.DateTime, default=datetime.utcnow)
# 'email_password', 'google', etc.
registration_source = db.Column(db.String(20), default='email_password')
login_count = db.Column(db.Integer, default=0)
# Profile information (optional)
# For a more personalized display name vs username
display_name = db.Column(db.String(100))
bio = db.Column(db.Text)
# Relationships for Feature 1: User Accounts & Progress Tracking
# A user can have multiple learning paths they've generated or saved
learning_paths = db.relationship(
'UserLearningPath', backref='author', lazy='dynamic')
def set_password(self, password):
self.password_hash = generate_password_hash(password)
def check_password(self, password):
return check_password_hash(self.password_hash, password)
def __repr__(self):
return f'<User {self.username}>'
class UserLearningPath(db.Model):
__tablename__ = 'user_learning_paths'
id = db.Column(db.String(36), primary_key=True,
default=lambda: str(uuid.uuid4()))
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
# Storing the original AI-generated path data as JSON for now
# This can be normalized further if needed for Feature 2 (Enhanced Resource Management)
path_data_json = db.Column(db.JSON, nullable=False)
# Extracted from path_data for easier display
title = db.Column(db.String(200), nullable=True)
# Extracted from path_data
topic = db.Column(db.String(100), nullable=True)
created_at = db.Column(db.DateTime, index=True, default=datetime.utcnow)
last_accessed_at = db.Column(db.DateTime, default=datetime.utcnow)
is_archived = db.Column(db.Boolean, default=False)
# Relationships for Feature 1: Progress Tracking
# A learning path can have multiple progress entries (one per milestone)
progress_entries = db.relationship(
'LearningProgress', backref='path', lazy='dynamic', cascade='all, delete-orphan')
def __repr__(self):
return f'<UserLearningPath {self.id} for User {self.user_id}>'
class LearningProgress(db.Model):
__tablename__ = 'learning_progress'
id = db.Column(db.Integer, primary_key=True)
user_learning_path_id = db.Column(db.String(36), db.ForeignKey(
'user_learning_paths.id'), nullable=False)
# Assuming milestones have a unique identifier within the path_data_json
# For simplicity, let's say milestone_title or an index can serve as this ID for now.
# This might need refinement based on how milestones are structured in path_data_json.
milestone_identifier = db.Column(db.String(200), nullable=False)
# e.g., 'not_started', 'in_progress', 'completed'
status = db.Column(db.String(50), default='not_started')
started_at = db.Column(db.DateTime)
completed_at = db.Column(db.DateTime)
notes = db.Column(db.Text)
# For Feature 3 (Interactive Learning - Quizzes), we might add quiz attempts here or in a separate table
__table_args__ = (db.UniqueConstraint('user_learning_path_id',
'milestone_identifier', name='_user_path_milestone_uc'),)
def __repr__(self):
return f'<LearningProgress for Milestone {self.milestone_identifier} in Path {self.user_learning_path_id}>'
class ResourceProgress(db.Model):
"""
Tracks completion status of individual resources within milestones.
Enables persistent progress tracking across sessions and devices.
"""
__tablename__ = 'resource_progress'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
learning_path_id = db.Column(db.String(36), db.ForeignKey(
'user_learning_paths.id'), nullable=False)
# 0-based index of milestone
milestone_index = db.Column(db.Integer, nullable=False)
# 0-based index of resource within milestone
resource_index = db.Column(db.Integer, nullable=False)
# Store URL for reference
resource_url = db.Column(db.String(500), nullable=False)
# Progress tracking
completed = db.Column(db.Boolean, default=False)
completed_at = db.Column(db.DateTime, nullable=True)
# Metadata
created_at = db.Column(db.DateTime, default=datetime.utcnow)
updated_at = db.Column(
db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
# Unique constraint: one entry per user, path, milestone, and resource
__table_args__ = (
db.UniqueConstraint('user_id', 'learning_path_id', 'milestone_index', 'resource_index',
name='_user_path_milestone_resource_uc'),
)
def __repr__(self):
return f'<ResourceProgress User:{self.user_id} Path:{self.learning_path_id} M:{self.milestone_index} R:{self.resource_index} Completed:{self.completed}>'
class MilestoneProgress(db.Model):
"""
Tracks completion status of entire milestones within learning paths.
Provides high-level progress tracking for milestone completion.
"""
__tablename__ = 'milestone_progress'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
learning_path_id = db.Column(db.String(36), db.ForeignKey(
'user_learning_paths.id'), nullable=False)
# 0-based index of milestone
milestone_index = db.Column(db.Integer, nullable=False)
# Progress tracking
completed = db.Column(db.Boolean, default=False)
completed_at = db.Column(db.DateTime, nullable=True)
# Metadata
created_at = db.Column(db.DateTime, default=datetime.utcnow)
updated_at = db.Column(
db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
# Unique constraint: one entry per user, path, and milestone
__table_args__ = (
db.UniqueConstraint('user_id', 'learning_path_id', 'milestone_index',
name='_milestone_progress_uc'),
)
def __repr__(self):
return f'<MilestoneProgress User:{self.user_id} Path:{self.learning_path_id} Milestone:{self.milestone_index} Completed:{self.completed}>'
# Models for Feature 2: Enhanced Resource Management (Placeholders, to be detailed later)
# class CustomResource(db.Model):
# id = db.Column(db.Integer, primary_key=True)
# user_id = db.Column(db.Integer, db.ForeignKey('users.id'))
# # ... fields for URL, title, description, type, tags ...
# class ResourceRating(db.Model):
# id = db.Column(db.Integer, primary_key=True)
# user_id = db.Column(db.Integer, db.ForeignKey('users.id'))
# resource_id = db.Column(db.Integer, db.ForeignKey('some_global_resource_table_or_original_resource_id'))
# rating = db.Column(db.Integer) # 1-5
# review = db.Column(db.Text)
# Models for Feature 3: Interactive Learning (Placeholders, to be detailed later)
# class Quiz(db.Model):
# id = db.Column(db.Integer, primary_key=True)
# milestone_identifier = db.Column(db.String(200)) # Links to a milestone
# # ... fields for quiz title, description ...
# class Question(db.Model):
# id = db.Column(db.Integer, primary_key=True)
# quiz_id = db.Column(db.Integer, db.ForeignKey('quiz.id'))
# # ... fields for question text, type (MCQ, code), options, correct_answer, explanation ...
# class UserQuizAttempt(db.Model):
# id = db.Column(db.Integer, primary_key=True)
# user_id = db.Column(db.Integer, db.ForeignKey('users.id'))
# quiz_id = db.Column(db.Integer, db.ForeignKey('quiz.id'))
# score = db.Column(db.Float)
# # ... fields for answers given, completion_date ...
# ============================================
# CONVERSATIONAL CHATBOT MODELS
# Phase 1: Conversation Memory System
# ============================================
class ChatMessage(db.Model):
"""
Stores all conversation messages between user and AI assistant.
Enhanced with:
- Conversation memory and context
- Multi-turn dialogue support
- Learning path context tracking
- Conversation analytics
- Automatic cleanup utilities
"""
__tablename__ = 'chat_messages'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
learning_path_id = db.Column(db.String(36), db.ForeignKey(
'user_learning_paths.id'), nullable=True)
# Message content
message = db.Column(db.Text, nullable=False)
role = db.Column(db.String(20), nullable=False) # 'user' or 'assistant'
# Conversation grouping (NEW: enhanced from session_id)
# Groups related messages
conversation_id = db.Column(db.String(36), nullable=True, index=True)
# Learning path context (NEW)
# Stores path state, progress, current milestone
context = db.Column(db.JSON, nullable=True)
# Intent classification (Phase 2)
# 'modify_path', 'check_progress', 'ask_question', 'general'
intent = db.Column(db.String(50), nullable=True)
# Extracted entities from message
entities = db.Column(db.JSON, nullable=True)
# Metadata
timestamp = db.Column(db.DateTime, default=datetime.utcnow, index=True)
tokens_used = db.Column(db.Integer, default=0) # Track API costs
response_time_ms = db.Column(
db.Integer, nullable=True) # Performance tracking
# Legacy field (kept for backward compatibility)
session_id = db.Column(db.String(36), nullable=True, index=True)
def __repr__(self):
return f'<ChatMessage {self.id} by User {self.user_id} ({self.role}) in Conversation {self.conversation_id}>'
@staticmethod
def get_conversation_history(conversation_id, limit=10):
"""
Get recent messages from a conversation.
Args:
conversation_id: The conversation ID to fetch
limit: Maximum number of messages to return (default: 10)
Returns:
List of ChatMessage objects, ordered by timestamp (oldest first)
"""
messages = ChatMessage.query.filter_by(
conversation_id=conversation_id
).order_by(
ChatMessage.timestamp.asc()
).limit(limit).all()
return messages
@staticmethod
def get_recent_context(conversation_id):
"""
Get the most recent context from a conversation.
Args:
conversation_id: The conversation ID
Returns:
Dictionary with learning path context, or None
"""
# Get the most recent message with context
message = ChatMessage.query.filter_by(
conversation_id=conversation_id
).filter(
ChatMessage.context.isnot(None)
).order_by(
ChatMessage.timestamp.desc()
).first()
return message.context if message else None
@staticmethod
def clean_old_messages(days=7):
"""
Delete messages older than specified days.
Args:
days: Number of days to keep (default: 7)
Returns:
Number of messages deleted
"""
from datetime import timedelta
cutoff_date = datetime.utcnow() - timedelta(days=days)
old_messages = ChatMessage.query.filter(
ChatMessage.timestamp < cutoff_date
).all()
count = len(old_messages)
for message in old_messages:
db.session.delete(message)
db.session.commit()
return count
@staticmethod
def get_conversation_stats(conversation_id):
"""
Get statistics about a conversation.
Args:
conversation_id: The conversation ID
Returns:
Dictionary with conversation statistics
"""
messages = ChatMessage.query.filter_by(
conversation_id=conversation_id
).all()
if not messages:
return None
user_messages = [m for m in messages if m.role == 'user']
assistant_messages = [m for m in messages if m.role == 'assistant']
total_tokens = sum(m.tokens_used for m in messages if m.tokens_used)
avg_response_time = sum(m.response_time_ms for m in assistant_messages if m.response_time_ms) / \
len(assistant_messages) if assistant_messages else 0
return {
'total_messages': len(messages),
'user_messages': len(user_messages),
'assistant_messages': len(assistant_messages),
'total_tokens': total_tokens,
'avg_response_time_ms': avg_response_time,
'started_at': min(m.timestamp for m in messages),
'last_message_at': max(m.timestamp for m in messages)
}
class PathModification(db.Model):
"""
Tracks all modifications made to learning paths via chatbot.
This enables:
- Modification history and audit trail
- Undo functionality
- Understanding user preferences
- Path evolution tracking
"""
__tablename__ = 'path_modifications'
id = db.Column(db.Integer, primary_key=True)
learning_path_id = db.Column(db.String(36), db.ForeignKey(
'user_learning_paths.id'), nullable=False)
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
chat_message_id = db.Column(db.Integer, db.ForeignKey(
'chat_messages.id'), nullable=True)
# What changed
# 'add_resource', 'modify_milestone', 'split_milestone', etc.
modification_type = db.Column(db.String(50), nullable=False)
# JSON path to modified element (e.g., 'milestones[2].resources')
target_path = db.Column(db.String(200), nullable=True)
# Change details
# Human-readable description
change_description = db.Column(db.Text, nullable=False)
old_value = db.Column(db.JSON, nullable=True) # Previous value (for undo)
new_value = db.Column(db.JSON, nullable=True) # New value
# Metadata
timestamp = db.Column(db.DateTime, default=datetime.utcnow, index=True)
# If user undid this change
is_reverted = db.Column(db.Boolean, default=False)
def __repr__(self):
return f'<PathModification {self.id} for Path {self.learning_path_id}>'
class ConversationSession(db.Model):
"""
Groups related chat messages into sessions.
This enables:
- Session-based context management
- Conversation analytics
- Session summaries
- Better context window management
"""
__tablename__ = 'conversation_sessions'
id = db.Column(db.String(36), primary_key=True,
default=lambda: str(uuid.uuid4()))
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
learning_path_id = db.Column(db.String(36), db.ForeignKey(
'user_learning_paths.id'), nullable=True)
# Session metadata
started_at = db.Column(db.DateTime, default=datetime.utcnow, index=True)
last_activity_at = db.Column(db.DateTime, default=datetime.utcnow)
ended_at = db.Column(db.DateTime, nullable=True)
# Session summary (generated by AI)
summary = db.Column(db.Text, nullable=True)
# Session stats
message_count = db.Column(db.Integer, default=0)
total_tokens_used = db.Column(db.Integer, default=0)
# Session state
is_active = db.Column(db.Boolean, default=True)
def __repr__(self):
return f'<ConversationSession {self.id} for User {self.user_id}>'
class OAuth(OAuthConsumerMixin, db.Model):
"""Store OAuth tokens for Flask-Dance"""
__tablename__ = 'flask_dance_oauth'
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=True)
user = db.relationship('User', backref='oauth_tokens')
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