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"""
Database Manager for Sentiment Evolution Tracker
Stores analysis results and provides historical comparisons.
"""
import sqlite3
import json
import os
from datetime import datetime
from typing import List, Dict, Any, Optional
class AnalysisDatabase:
"""Manages persistent storage of sentiment analyses."""
def __init__(self, db_path: Optional[str] = None):
"""Initialize database."""
if db_path is None:
base_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(base_dir, "..", "data")
os.makedirs(data_dir, exist_ok=True)
db_path = os.path.join(data_dir, "sentiment_analysis.db")
self.db_path = db_path
self._init_database()
def _init_database(self):
"""Create database tables if they don't exist."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Table for conversations
cursor.execute('''
CREATE TABLE IF NOT EXISTS conversations (
id INTEGER PRIMARY KEY AUTOINCREMENT,
customer_id TEXT NOT NULL,
context_type TEXT NOT NULL,
analysis_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
messages TEXT NOT NULL,
sentiment_score REAL,
trend TEXT,
risk_level TEXT,
predicted_action TEXT,
confidence REAL
)
''')
# Table for risk alerts
cursor.execute('''
CREATE TABLE IF NOT EXISTS risk_alerts (
id INTEGER PRIMARY KEY AUTOINCREMENT,
customer_id TEXT NOT NULL,
alert_type TEXT,
severity TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
resolved INTEGER DEFAULT 0,
notes TEXT
)
''')
# Table for customer profiles
cursor.execute('''
CREATE TABLE IF NOT EXISTS customer_profiles (
id INTEGER PRIMARY KEY AUTOINCREMENT,
customer_id TEXT UNIQUE NOT NULL,
name TEXT,
context_type TEXT,
first_contact TIMESTAMP,
last_contact TIMESTAMP,
total_interactions INTEGER DEFAULT 0,
churn_risk REAL DEFAULT 0,
lifetime_sentiment REAL DEFAULT 0,
notes TEXT
)
''')
conn.commit()
conn.close()
def save_analysis(self, customer_id: str, context_type: str,
messages: List[str], analysis: Dict[str, Any]) -> int:
"""
Save an analysis result to the database.
Args:
customer_id: Unique customer identifier
context_type: 'customer', 'employee', or 'email'
messages: List of message strings
analysis: Analysis result dictionary
Returns:
Analysis ID
"""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute('''
INSERT INTO conversations
(customer_id, context_type, messages, sentiment_score,
trend, risk_level, predicted_action, confidence)
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
''', (
customer_id,
context_type,
json.dumps(messages),
analysis.get('current_sentiment', 0),
analysis.get('trend', 'UNKNOWN'),
analysis.get('risk_level', 'UNKNOWN'),
analysis.get('predicted_action', 'UNKNOWN'),
analysis.get('confidence', 0)
))
analysis_id = cursor.lastrowid
# Update or create customer profile
cursor.execute('SELECT id FROM customer_profiles WHERE customer_id = ?', (customer_id,))
profile = cursor.fetchone()
if profile:
cursor.execute('''
UPDATE customer_profiles
SET last_contact = CURRENT_TIMESTAMP,
total_interactions = total_interactions + 1,
churn_risk = ?,
lifetime_sentiment = (lifetime_sentiment * total_interactions + ?) / (total_interactions + 1)
WHERE customer_id = ?
''', (
analysis.get('confidence', 0),
analysis.get('current_sentiment', 0),
customer_id
))
else:
cursor.execute('''
INSERT INTO customer_profiles
(customer_id, context_type, first_contact, last_contact,
total_interactions, churn_risk, lifetime_sentiment)
VALUES (?, ?, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 1, ?, ?)
''', (
customer_id,
context_type,
analysis.get('confidence', 0),
analysis.get('current_sentiment', 0)
))
# Create alert if risk is high
if analysis.get('confidence', 0) > 0.7:
cursor.execute('''
INSERT INTO risk_alerts (customer_id, alert_type, severity, notes)
VALUES (?, ?, ?, ?)
''', (
customer_id,
analysis.get('predicted_action', 'UNKNOWN'),
'HIGH' if analysis.get('confidence', 0) > 0.85 else 'MEDIUM',
f"Detected {analysis.get('trend')} trend with {analysis.get('confidence', 0)*100:.0f}% confidence"
))
conn.commit()
conn.close()
return analysis_id
def get_customer_history(self, customer_id: str) -> Dict[str, Any]:
"""
Get complete history for a customer.
Args:
customer_id: Unique customer identifier
Returns:
Customer profile and analysis history
"""
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
# Get profile
cursor.execute('SELECT * FROM customer_profiles WHERE customer_id = ?', (customer_id,))
profile_row = cursor.fetchone()
profile = dict(profile_row) if profile_row else None
# Get recent analyses
cursor.execute('''
SELECT * FROM conversations
WHERE customer_id = ?
ORDER BY analysis_date DESC
LIMIT 10
''', (customer_id,))
analyses = [dict(row) for row in cursor.fetchall()]
# Get active alerts
cursor.execute('''
SELECT * FROM risk_alerts
WHERE customer_id = ? AND resolved = 0
ORDER BY created_at DESC
''', (customer_id,))
alerts = [dict(row) for row in cursor.fetchall()]
conn.close()
return {
'profile': profile,
'analyses': analyses,
'active_alerts': alerts
}
def get_high_risk_customers(self, threshold: float = 0.75) -> List[Dict[str, Any]]:
"""
Get all customers with high churn risk.
Args:
threshold: Confidence threshold (0-1)
Returns:
List of high-risk customers
"""
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
cursor.execute('''
SELECT cp.*,
COUNT(ra.id) as active_alerts,
MAX(c.analysis_date) as last_analysis
FROM customer_profiles cp
LEFT JOIN risk_alerts ra ON cp.customer_id = ra.customer_id AND ra.resolved = 0
LEFT JOIN conversations c ON cp.customer_id = c.customer_id
WHERE cp.churn_risk > ?
GROUP BY cp.customer_id
ORDER BY cp.churn_risk DESC
''', (threshold,))
results = [dict(row) for row in cursor.fetchall()]
conn.close()
return results
def resolve_alert(self, alert_id: int, notes: str = ""):
"""Mark an alert as resolved."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute('''
UPDATE risk_alerts
SET resolved = 1, notes = ?
WHERE id = ?
''', (notes, alert_id))
conn.commit()
conn.close()
def get_statistics(self) -> Dict[str, Any]:
"""Get overall database statistics."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Total customers
cursor.execute('SELECT COUNT(DISTINCT customer_id) as count FROM conversations')
total_customers = cursor.fetchone()[0]
# Customers at risk
cursor.execute('SELECT COUNT(*) as count FROM customer_profiles WHERE churn_risk > 0.7')
at_risk = cursor.fetchone()[0]
# Active alerts
cursor.execute('SELECT COUNT(*) as count FROM risk_alerts WHERE resolved = 0')
active_alerts = cursor.fetchone()[0]
# Average sentiment
cursor.execute('SELECT AVG(sentiment_score) as avg FROM conversations')
avg_sentiment = cursor.fetchone()[0] or 0
conn.close()
return {
'total_customers': total_customers,
'customers_at_risk': at_risk,
'active_alerts': active_alerts,
'average_sentiment': round(avg_sentiment, 2),
'database_file': self.db_path
}
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