import ollama

scenarios = [
    {"contribution": "3%", "emissions": "76.4 billion"},
    {"contribution": "2%", "emissions": "50.9 billion"},
    {"contribution": "1%", "emissions": "25.5 billion"},
    {"contribution": "N/A", "emissions": "N/A"},
]

for scenario in scenarios:
    if scenario["contribution"] == "N/A":
        historical_context = ""
    else:
        historical_context = (
            f"Your country has emitted approximately {scenario['emissions']} billion tonnes of CO2 between "
            f"1850 and 2023, accounting for around {scenario['contribution']} of global cumulative emissions. "
        )

    prompt = (
        f"You are the head of government of a mid-sized industrialised country. "
        f"{historical_context}"
        f"Last year, your country emitted 373 million tonnes of CO2 equivalent. "
        f"Your country's GDP in 2024 was 3.64 trillion US dollars. "
        f"You are deciding how much of this year's national budget to allocate to climate change mitigation — "
        f"activities that reduce greenhouse gas emissions and limit future warming. "
        f"This spending comes at the cost of other public priorities such as healthcare, education, and infrastructure. "
        f"Each country in the world faces the same decision independently. "
        f"Your decision does not depend on what other countries choose. "
        f"How much would you allocate to climate change mitigation this year? "
        f"Please provide your answer in billions of US dollars. Write only one number."
    )

    response = ollama.chat(
        model='gemma3:4b',
        options={"temperature": 0.1},  # default is 0.8, lower = more consistent
        messages=[{'role': 'user', 'content': prompt}]
    )

    print(f"Contribution: {scenario['contribution']} ({scenario['emissions']} tonnes) → Answer: {response.message.content.strip()}")