# uncloseai.

Python Examples - Free LLM & TTS AI Service

## Python Examples

This page demonstrates how to use the uncloseai. API endpoints with Python using the OpenAI client library. All examples use the same OpenAI-compatible API interface, making it easy to switch between different models and endpoints.

Available Endpoints:

- Hermes: `https://hermes.ai.unturf.com/v1` - General purpose conversational AI
- Qwen 3 Coder: `https://qwen.ai.unturf.com/v1` - Specialized coding model
- TTS: `https://speech.ai.unturf.com/v1` - Text-to-speech generation

### Python Client Installation

To install the OpenAI package for Python, use `pip`:

    pip install openai==2.3.0

## Non-Streaming Examples

Non-streaming mode waits for the complete response before returning. This is simpler to use but provides no intermediate feedback during generation.

### Using Hermes (General Purpose)

    # Python Fizzbuzz Example with Hermes
    from openai import OpenAI

    client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="choose-any-value")

    MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic"

    messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}]

    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        temperature=0.5,
        max_tokens=150
    )

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

### Using Qwen 3 Coder (Specialized for Coding)

    # Python Fizzbuzz Example with Qwen 3 Coder
    from openai import OpenAI

    client = OpenAI(base_url="https://qwen.ai.unturf.com/v1", api_key="choose-any-value")

    MODEL = "hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M"

    messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}]

    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        temperature=0.5,
        max_tokens=150
    )

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

## Streaming Examples

Streaming mode returns chunks of the response as they are generated, providing real-time feedback. This is ideal for interactive applications and long responses.

### Using Hermes (General Purpose)

    # Streaming response in Python with Hermes
    from openai import OpenAI

    client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="choose-any-value")

    MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic"

    messages = [
        {"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}
    ]

    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        temperature=0.5,
        max_tokens=150,
        stream=True,  # Enable streaming
    )

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

### Using Qwen 3 Coder (Specialized for Coding)

    # Streaming response in Python with Qwen 3 Coder
    from openai import OpenAI

    client = OpenAI(base_url="https://qwen.ai.unturf.com/v1", api_key="choose-any-value")

    MODEL = "hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M"

    messages = [
        {"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}
    ]

    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        temperature=0.5,
        max_tokens=150,
        stream=True,  # Enable streaming
    )

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

## Text-to-Speech Example

Generate audio speech from text using the TTS endpoint. The audio is saved as an MP3 file.

    # TTS Speech Example in Python
    import openai

    client = openai.OpenAI(
      api_key = "YOLO",
      base_url = "https://speech.ai.unturf.com/v1",
    )

    with client.audio.speech.with_streaming_response.create(
      model="tts-1",
      voice="alloy",
      speed=0.9,
      input="I think so therefore, Today is a wonderful day to grow something people love!"
    ) as response:
      response.stream_to_file("speech.mp3")