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import openai

openai.api_key = "your-api-key"

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the basics of quantum computing."}
    ]
)

print(response.choices[0].message['content'])

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import openai

openai.api_key = "your-api-key"

response = openai.FineTune.create(
    training_file="file-abc123",
    model="gpt-4"
)

print(response)

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import openai

openai.api_key = "your-api-key"

response = openai.Batch.create(
    model="gpt-4",
    tasks=[{"prompt": "Analyze sentiment of this text"}, {"prompt": "Summarize this document"}]
)

print(response)

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import openai

openai.api_key = "your-api-key"

response = openai.Image.create(
    prompt="A futuristic cityscape with flying cars",
    n=1,
    size="1024x1024"
)

image_url = response['data'][0]['url']
print("Generated image URL:", image_url)

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import openai

openai.api_key = "your-api-key"

response = openai.Audio.create(
    model="gpt-4",
    input="This is a test of the text to speech conversion."
)

print(response['data'])

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import openai

openai.api_key = "your-api-key"

audio_file = open("audio.wav", "rb")
response = openai.Audio.transcribe(
    model="whisper-1",
    file=audio_file
)

print("Transcribed text:", response['text'])

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import openai

openai.api_key = "your-api-key"

response = openai.Embedding.create(
    input="OpenAI provides powerful AI models",
    model="text-embedding-ada-002"
)

embedding_vector = response['data'][0]['embedding']
print("Embedding vector:", embedding_vector)

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import openai

openai.api_key = "your-api-key"

response = openai.Moderation.create(
    input="This is a sample text that might contain harmful content."
)

print(response['results'])

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response = openai.Completion.create(
    model="gpt-4",
    prompt="Write a story about a brave knight who fights a dragon.",
    max_tokens=200
)

print(response.choices[0].text.strip())

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response = openai.Image.create(
    prompt="A painting of a futuristic landscape with mountains and flying cars.",
    n=1,
    size="1024x1024"
)

image_url = response['data'][0]['url']
print("Generated image URL:", image_url)

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response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "What is the weather like in New York?"},
    ],
    functions=[
        {
            "name": "get_current_weather",
            "description": "Gets the current weather in a given city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "The name of the city"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                }
            }
        }
    ]
)

print(response.choices[0].message['content'])

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import openai
import json
from duckpy import Client
import ast

#  DuckDuckGo 클라이언트를 초기화
duckduckgo_client = Client()

# DuckDuckGo에서 검색을 수행하는 함수를 정의
def duck_search(query) -> str:
    output = duckduckgo_client.search(query)
    return str(output)

# OpenAI API를 호출하여 초기 응답
completion = client.chat.completions.create(
        model="gpt-3.5-turbo-0613",
        temperature=0,
        functions=[
            {
                "name": "duck_search",
                "description": "Used to search online",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "Translate the Korean content into English input query",
                        },
                    },
                    "required": ["query"],
                },
            }
        ],
        messages=[
            {"role": "system", 
            "content": "You must use the `duck_search` function to get information."},
            {"role": "user", 
            "content": "조지 클루니 생일이 언제야?"},
        ]
    )

print(completion)

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response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Give me a recipe for chocolate cake."}],
    functions=[{
        "name": "generate_recipe",
        "description": "Generates a

 recipe",
        "parameters": {"type": "object", "properties": {"ingredients": {"type": "array"}}}
    }]
)

print(response.choices[0].message['content'])

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response = openai.Completion.create(
    model="gpt-4",
    prompt="Solve the equation: 5x - 3 = 12",
    max_tokens=50
)

print(response.choices[0].text.strip())

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response = openai.Completion.create(
    model="gpt-4",
    prompt="Tell me a long story about a hero's journey.",
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.get("content", ""), end="")

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response = openai.Completion.create(
    model="fine-tuned-model-id",  # 파인튜닝된 모델 ID
    prompt="Summarize the article about climate change.",
    max_tokens=100
)

print(response.choices[0].text.strip())