Text Generation and Conversation

from openai import OpenAI
client = OpenAI()
response = client.responses.create(
    model="gpt-5",
    input=[
        {"role": "system", "content": "You are a helpful tutor who explains complex topics in simple terms with analogies."},
        {"role": "user", "content": "What is machine learning?"}
    ]
)
print(response.output_text)

conversation = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "My favorite color is blue."},
    {"role": "assistant", "content": "That's nice! Blue is a beautiful color. It's often associated with calmness and serenity."},
    {"role": "user", "content": "What are some things in nature that match my favorite color?"}
]
response = client.responses.create(
    model="gpt-5",
    input=conversation
)
print(response.output_text)

# Conservative, factual response
print("Conservative,factual response:")
response_conservative = client.responses.create(
    model="gpt-4o",
    input="What is the name of the manga where we have a white haird character as the main protagonist? Only provide two names!",
    temperature=0.1
)
print("-" * 100)
print(response_conservative.output_text)
print("-" * 100)
print("Creative, varied response:")
print("-" * 100)
# Creative, varied response
response_creative = client.responses.create(
    model="gpt-4o",
    input="What is the name of the manga where we have a white haird character as the main protagonist? Only provide two names!",
    temperature=0.9
)
print(response_creative.output_text)
print("-" * 100)

# Short response
print("Short Response:")
response = client.responses.create(
    model="gpt-4o",
    input="Explain artificial intelligence.",
    max_output_tokens=30 # About 1-2 sentences
)
print(response.output_text)
print("-" * 100)
# Longer response
print("Longer Response:")
response = client.responses.create(
    model="gpt-4o",
    input="Explain artificial intelligence.",
    max_output_tokens=200  # About 1 paragraph
)
print(response.output_text)
print("-" * 100)

"""
GPT-5 Reasoning Effort Demo
===========================
This script demonstrates how the reasoning_effort parameter controls 
the quality and depth of GPT-5's thinking process, and measures the 
performance impact of different reasoning levels.
"""
import time
# Set up OpenAI client
# Make sure your OPENAI_API_KEY environment variable is set
def demo_reasoning_effort():
    """
    Demonstrates the reasoning_effort parameter.
    
    reasoning_effort controls how much computational "thinking" the model does:
    - minimal: Fastest, for simple deterministic tasks
    - low: Light reasoning
    - medium: Default balanced reasoning
    - high: Most thorough reasoning and analysis
    """
    print("=" * 60)
    print("GPT-5 REASONING EFFORT DEMONSTRATION")
    print("=" * 60)
    
    # Test prompt that benefits from reasoning
    problem = "If a train leaves Station A at 2:00 PM traveling at 60 mph, and another train leaves Station B at 2:30 PM traveling at 80 mph toward Station A, and the stations are 200 miles apart, when will they meet?"
    
    print("Problem:", problem)
    print("\n" + "-" * 60)
    
    # Minimal reasoning effort - fast but simple
    print("MINIMAL REASONING EFFORT (fastest):")
    print("-" * 40)
    start_time = time.time()
    response_minimal = client.responses.create(
        model="gpt-5",
        input=problem,
        reasoning={"effort": "minimal"}
    )
    minimal_time = time.time() - start_time
    print(response_minimal.output_text)
    print(f"\n⏱️  Time taken: {minimal_time:.2f} seconds")
    
    print("\n" + "-" * 60)
    
    # High reasoning effort - thorough analysis
    print("HIGH REASONING EFFORT (most thorough):")
    print("-" * 40)
    start_time = time.time()
    response_high = client.responses.create(
        model="gpt-5", 
        input=problem,
        reasoning={"effort": "high"}
    )
    high_time = time.time() - start_time
    print(response_high.output_text)
    print(f"\n⏱️  Time taken: {high_time:.2f} seconds")
    print(f"📊 Speed difference: {high_time/minimal_time:.1f}x slower than minimal")
    
    print("\n" + "=" * 60)
if __name__ == "__main__":
    """
    Run the reasoning effort demonstration.
    
    This shows how different reasoning levels affect both the quality
    of responses and the time taken to generate them.
    """
    
    print("GPT-5 Reasoning Effort Demo")
    print("This demo shows how reasoning_effort controls the depth of GPT-5's thinking.\n")
    
    try:
        demo_reasoning_effort()
        
    except Exception as e:
        print(f"Error: {e}")
        print("Make sure your OPENAI_API_KEY environment variable is set correctly.")

"""
GPT-5 Verbosity Demo
====================
This script demonstrates how the verbosity parameter controls 
the length and depth of GPT-5's responses, and measures the 
performance impact of different verbosity levels.
"""
import time
def demo_verbosity():
    """
    Demonstrates the verbosity parameter.
    
    verbosity controls the natural length and depth of responses:
    - low: Concise, minimal responses
    - medium: Default balanced responses  
    - high: Comprehensive, detailed responses
    """
    print("=" * 60)
    print("GPT-5 VERBOSITY DEMONSTRATION")
    print("=" * 60)
    
    # Topic that can be explained at different depths
    topic = "Explain how machine learning algorithms learn from data"
    
    print("Topic:", topic)
    print("\n" + "-" * 60)
    
    # Low verbosity - concise explanation
    print("LOW VERBOSITY (concise):")
    print("-" * 40)
    start_time = time.time()
    response_low = client.responses.create(
        model="gpt-5",
        input=topic,
        text={"verbosity": "low"}
    )
    low_time = time.time() - start_time
    print(response_low.output_text)
    print(f"\n⏱️  Time taken: {low_time:.2f} seconds")
    
    print("\n" + "-" * 60)
    
    # Medium verbosity - comprehensive explanation
    print("MEDIUM VERBOSITY (comprehensive):")
    print("-" * 40)
    start_time = time.time()
    response_medium = client.responses.create(
        model="gpt-5",
        input=topic, 
        text={"verbosity": "medium"}
    )
    medium_time = time.time() - start_time
    print(response_medium.output_text)
    print(f"\n⏱️  Time taken: {medium_time:.2f} seconds")
    print(f"📊 Length difference: {medium_time/low_time:.1f}x longer than low verbosity")
    
    print("\n" + "=" * 60)
if __name__ == "__main__":
    """
    Run the verbosity demonstration.
    
    This shows how different verbosity levels affect both the length
    of responses and the time taken to generate them.
    """
    
    print("GPT-5 Verbosity Demo")
    print("This demo shows how verbosity controls the length and depth of GPT-5's responses.\n")
    
    try:
        demo_verbosity()
        
    except Exception as e:
        print(f"Error: {e}")
        print("Make sure your OPENAI_API_KEY environment variable is set correctly.")