from openai_unofficial import OpenAIUnofficial

# Initialize the client
client = OpenAIUnofficial()

# Basic chat completion
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Say hello!"}],
    model="gpt-4o"
)
print(response.choices[0].message.content)

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
models = client.list_models()
print("Available Models:")
for model in models['data']:
    print(f"- {model['id']}")

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Tell me a joke."}],
    model="gpt-4o"
)
print("ChatBot:", response.choices[0].message.content)

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
response = client.chat.completions.create(
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                }
            },
        ],
    }],
    model="gpt-4o-mini-2024-07-18"
)
print("Response:", response.choices[0].message.content)

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
completion_stream = client.chat.completions.create(
    messages=[{"role": "user", "content": "Write a short story in 3 sentences."}],
    model="gpt-4o-mini-2024-07-18",
    stream=True
)
for chunk in completion_stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end='', flush=True)

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
audio_data = client.audio.create(
    input_text="This is a test of the TTS capabilities!",
    model="tts-1-hd",
    voice="nova"
)
with open("tts_output.mp3", "wb") as f:
    f.write(audio_data)
print("TTS Audio saved as tts_output.mp3")

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Tell me a fun fact."}],
    model="gpt-4o-audio-preview",
    modalities=["text", "audio"],
    audio={"voice": "fable", "format": "wav"}
)

message = response.choices[0].message
print("Text Response:", message.content)

if message.audio and 'data' in message.audio:
    from base64 import b64decode
    with open("audio_preview.wav", "wb") as f:
        f.write(b64decode(message.audio['data']))
    print("Audio saved as audio_preview.wav")

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
response = client.image.create(
    prompt="A futuristic cityscape at sunset",
    model="dall-e-3",
    size="1024x1024"
)
print("Image URL:", response.data[0].url)

from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial()
with open("speech.mp3", "rb") as audio_file:
    transcription = client.audio.transcribe(
        file=audio_file,
        model="whisper-1"
    )
print("Transcription:", transcription.text)

from openai_unofficial import OpenAIUnofficial
import json

client = OpenAIUnofficial()

# Define your functions as tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g., San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Function to actually get weather data
def get_current_weather(location: str, unit: str = "celsius") -> str:
    # This is a mock function - replace with actual weather API call
    return f"The current weather in {location} is 22°{unit[0].upper()}"

# Initial conversation message
messages = [
    {"role": "user", "content": "What's the weather like in London?"}
]

# First API call to get function calling response
response = client.chat.completions.create(
    model="gpt-4o-mini-2024-07-18",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

# Get the assistant's message
assistant_message = response.choices[0].message
messages.append(assistant_message.to_dict())

# Check if the model wants to call a function
if assistant_message.tool_calls:
    # Process each tool call
    for tool_call in assistant_message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        # Call the function and get the result
        function_response = get_current_weather(**function_args)
        
        # Append the function response to messages
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "name": function_name,
            "content": function_response
        })
    
    # Get the final response from the model
    final_response = client.chat.completions.create(
        model="gpt-4o-mini-2024-07-18",
        messages=messages
    )
    
    print("Final Response:", final_response.choices[0].message.content)