# admin/data_manager.py """Data management functionality for admin operations""" import sqlite3 import json from datetime import datetime import random import uuid import pandas as pd from pathlib import Path from faker import Faker class RobataDataManager: def __init__(self, db_path: str = "robata.db"): """Initialize database manager""" self.db_path = db_path self.setup_database() def setup_database(self): """Create database tables if they don't exist""" conn = sqlite3.connect(self.db_path) c = conn.cursor() # Create tables c.executescript(''' CREATE TABLE IF NOT EXISTS users ( id TEXT PRIMARY KEY, email TEXT UNIQUE, name TEXT, role TEXT, department TEXT, title TEXT, created_at TEXT ); CREATE TABLE IF NOT EXISTS accounts ( id TEXT PRIMARY KEY, name TEXT, industry TEXT, status TEXT, website TEXT, annual_revenue REAL, employee_count INTEGER, created_at TEXT ); CREATE TABLE IF NOT EXISTS contacts ( id TEXT PRIMARY KEY, account_id TEXT, first_name TEXT, last_name TEXT, email TEXT, title TEXT, department TEXT, influence_level TEXT, created_at TEXT, FOREIGN KEY (account_id) REFERENCES accounts (id) ); CREATE TABLE IF NOT EXISTS interactions ( id TEXT PRIMARY KEY, type TEXT, account_id TEXT, owner_id TEXT, transcript TEXT, summary TEXT, sentiment_score REAL, metadata TEXT, created_at TEXT, FOREIGN KEY (account_id) REFERENCES accounts (id), FOREIGN KEY (owner_id) REFERENCES users (id) ); ''') conn.commit() conn.close() def generate_sample_data(self): """Generate and insert sample data""" fake = Faker() conn = sqlite3.connect(self.db_path) c = conn.cursor() # Generate users users = [] roles = ['sales_rep'] * 6 + ['regional_lead'] * 3 + ['head_of_sales'] departments = ['Sales', 'Consulting', 'Technology', 'Operations', 'Strategy'] for _ in range(10): department = random.choice(departments) role = random.choice(roles) user = ( str(uuid.uuid4()), fake.company_email(), fake.name(), role, department, f"Senior {department} Consultant", datetime.now().isoformat() ) users.append(user) c.executemany( 'INSERT OR REPLACE INTO users (id, email, name, role, department, title, created_at) VALUES (?, ?, ?, ?, ?, ?, ?)', users ) # Generate accounts accounts = [] client_companies = [ ('Quantum Financial', 'Financial Services'), ('HealthTech Solutions', 'Healthcare'), ('InnovateMfg', 'Manufacturing'), ('RetailPro Global', 'Retail'), ('TechVision Corp', 'Technology'), ('EnergyFuture Ltd', 'Energy') ] for name, industry in client_companies: account = ( str(uuid.uuid4()), name, industry, random.choice(['active', 'active', 'prospect']), f"https://www.{name.lower().replace(' ', '')}.com", round(random.uniform(10, 500), 2), random.randint(100, 10000), datetime.now().isoformat() ) accounts.append(account) c.executemany( 'INSERT OR REPLACE INTO accounts VALUES (?, ?, ?, ?, ?, ?, ?, ?)', accounts ) # Generate contacts and interactions contacts = [] interactions = [] for account in accounts: # Generate contacts for each account for _ in range(random.randint(3, 7)): contact = ( str(uuid.uuid4()), account[0], fake.first_name(), fake.last_name(), fake.email(), random.choice(['CTO', 'CIO', 'IT Director', 'Innovation Lead']), random.choice(['IT', 'Operations', 'Finance', 'Strategy']), random.choice(['high', 'medium', 'low']), datetime.now().isoformat() ) contacts.append(contact) # Generate interactions for _ in range(random.randint(3, 8)): metadata = { 'location': 'Virtual' if random.random() < 0.7 else 'In-Person', 'duration': random.randint(30, 120), 'platform': random.choice(['Zoom', 'Teams', 'Google Meet']) } interaction = ( str(uuid.uuid4()), random.choice(['meeting', 'call', 'email', 'presentation']), account[0], random.choice(users)[0], fake.text(max_nb_chars=200), fake.text(max_nb_chars=100), round(random.uniform(0, 1), 2), json.dumps(metadata), datetime.now().isoformat() ) interactions.append(interaction) c.executemany( 'INSERT OR REPLACE INTO contacts VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)', contacts ) c.executemany( 'INSERT OR REPLACE INTO interactions VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)', interactions ) conn.commit() conn.close() return len(users), len(accounts), len(contacts), len(interactions) def get_dashboard_data(self): """Get data for dashboard display""" conn = sqlite3.connect(self.db_path) # Get counts counts = {} counts['accounts'] = pd.read_sql('SELECT COUNT(*) as count FROM accounts', conn).iloc[0]['count'] counts['contacts'] = pd.read_sql('SELECT COUNT(*) as count FROM contacts', conn).iloc[0]['count'] counts['interactions'] = pd.read_sql('SELECT COUNT(*) as count FROM interactions', conn).iloc[0]['count'] # Get recent interactions recent_interactions = pd.read_sql(''' SELECT i.*, a.name as account_name, u.name as owner_name FROM interactions i JOIN accounts a ON i.account_id = a.id JOIN users u ON i.owner_id = u.id ORDER BY i.created_at DESC LIMIT 10 ''', conn) # Get account distribution account_distribution = pd.read_sql(''' SELECT industry, COUNT(*) as count FROM accounts GROUP BY industry ''', conn) conn.close() return counts, recent_interactions, account_distribution