import random

COMPANIES = [
    "Apple Inc.",
    "Microsoft Corporation",
    "Amazon.com, Inc.",
    "Alphabet Inc.",
    "Meta Platforms, Inc.",
    "Tesla, Inc.",
    "Berkshire Hathaway Inc.",
    "NVIDIA Corporation",
    "JPMorgan Chase & Co.",
    "Johnson & Johnson",
    "Walmart Inc.",
    "Visa Inc.",
    "Mastercard Incorporated",
    "Procter & Gamble Co.",
    "UnitedHealth Group Incorporated",
    "Bank of America Corporation",
    "Home Depot, Inc.",
    "Nestlé S.A.",
    "Samsung Electronics Co., Ltd.",
    "LVMH Moët Hennessy – Louis Vuitton",
    "ASML Holding N.V.",
    "Exxon Mobil Corporation",
    "Intel Corporation",
    "Pfizer Inc.",
    "The Coca-Cola Company",
    "PepsiCo, Inc.",
    "Chevron Corporation",
    "Merck & Co., Inc.",
    "Novartis International AG",
    "Toyota Motor Corporation",
    "Oracle Corporation",
    "Cisco Systems, Inc.",
    "Adobe Inc.",
    "Salesforce, Inc.",
    "Netflix, Inc.",
    "International Business Machines Corporation (IBM)",
    "The Walt Disney Company",
    "HSBC Holdings plc",
    "McDonald's Corporation",
    "Nike, Inc.",
    "Qualcomm Incorporated",
    "Roche Holding AG",
    "SAP SE",
    "Abbott Laboratories",
    "Costco Wholesale Corporation",
    "Broadcom Inc.",
    "Accenture plc",
    "Chevron Corporation",
    "Texas Instruments Incorporated",
    "Unilever PLC"
]

THINGS_TO_KNOW = [
    "Company name and branding",
    "Founding date and history",
    "Founders and key executives",
    "Headquarters and global offices",
    "Mission, vision, and values",
    "Core products and services",
    "Business model and revenue streams",
    "Annual revenue and growth rate",
    "Profit margins (gross, operating, net)",
    "Market capitalization and valuation",
    "Key financial ratios (P/E, ROE, ROI)",
    "Stock price history and recent performance",
    "Major investors and shareholder structure",
    "Recent mergers, acquisitions, or divestitures",
    "R&D spending and innovation pipeline",
    "Competitive landscape and main rivals",
    "Market share by region or segment",
    "Customer segments and target markets",
    "Pricing strategy and positioning",
    "Supply chain structure and partners",
    "Distribution channels and logistics",
    "Marketing and advertising strategies",
    "Brand perception and reputation",
    "ESG (Environmental, Social, Governance) scores",
    "Sustainability initiatives and impact",
    "Corporate culture and employee count",
    "Employee satisfaction and turnover rates",
    "Leadership and governance practices",
    "Regulatory environment and compliance record",
    "Key risks and litigation history",
    "Recent news, press releases, and media coverage",
    "Patents, trademarks, and IP portfolio",
    "Digital transformation and tech stack",
    "Website traffic and social media metrics",
    "Mobile app usage and customer reviews",
    "Partnerships and strategic alliances",
    "Future outlook and analyst recommendations",
]

JOB_TITLES = [
    "Chief Executive Officer (CEO)",
    "Chief Financial Officer (CFO)",
    "Chief Operating Officer (COO)",
    "Chief Technology Officer (CTO)",
    "Chief Marketing Officer (CMO)",
    "Head of Investor Relations",
    "Director of Corporate Strategy",
    "Business Development Manager",
    "Product Manager",
    "Marketing Manager",
    "Brand Manager",
    "Financial Analyst",
    "Equity Research Analyst",
    "Market Research Analyst",
    "Consultant",
    "Venture Capital Associate",
    "Private Equity Associate",
    "Operations Manager",
    "Supply Chain Manager",
    "Human Resources Manager",
    "Sustainability Officer",
    "Compliance Officer",
    "Legal Counsel",
    "Risk Manager",
    "Data Analyst",
    "IT Manager",
    "Sales Director",
    "Account Manager",
    "Customer Success Manager",
    "Public Relations Manager"
]

def _get_question():
    return (f"I am a {random.choice(JOB_TITLES)} and I want to know {random.choice(THINGS_TO_KNOW)}"
            f"about {random.choice(COMPANIES)}")