wraipperz 0.1.11

Simple wrappers for various AI APIs including LLMs, ASR, and TTS

wraipperz

Easy wrapper for various AI APIs including LLMs, ASR, and TTS.

Installation

pip install wraipperz
uv add wraipperz

Features

• LLM API Wrappers: Unified interface for OpenAI, Anthropic, Google, and other LLM providers
• ASR (Automatic Speech Recognition): Convert speech to text
• TTS (Text-to-Speech): Convert text to speech
• Async Support: Asynchronous API calls for improved performance

Quick Start

LLM

import os
from wraipperz import call_ai, MessageBuilder

os.environ["OPENAI_API_KEY"] = "your_openai_key" # if not defined in environment variables
messages = MessageBuilder().add_system("You are a helpful assistant.").add_user("What's 1+1?")

# Call an LLM with a simple interface
response, cost = call_ai(
    model="openai/gpt-4o",
    messages=messages
)

Parsing LLM output to pydantic object.

from pydantic import BaseModel, Field
from wraipperz import pydantic_to_yaml_example, find_yaml, MessageBuilder, call_ai
import yaml


class User(BaseModel):
    name: str = Field(json_schema_extra={"example": "Bob", "comment": "The name of the character."})
    age: int = Field(json_schema_extra={"example": 12, "comment": "The age of the character."})


template = pydantic_to_yaml_example(User)
prompt = f"""Extract the user's name and age from the unstructured text provided below and output your answer following the provided example.
Text: "John is a well respected 31 years old pirate who really likes mooncakes."
Exampe output:
\`\`\`yaml
{template}
\`\`\`
"""
messages = MessageBuilder().add_system(prompt).build()
response, cost = call_ai(model="openai/gpt-4o-mini", messages=messages)

yaml_content = find_yaml(response)
user = User(**yaml.safe_load(yaml_content))
print(user)  # prints name='John' age=31

Image Generation and Modification (todo check readme)

from wraipperz import generate, MessageBuilder
from PIL import Image

# Text-to-image generation
messages = MessageBuilder().add_user("Generate an image of a futuristic city skyline at sunset.").build()

result, cost = generate(
    model="gemini/gemini-2.0-flash-exp-image-generation",
    messages=messages,
    temperature=0.7,
    max_tokens=4096
)

# The result contains both text and images
print(result["text"])  # Text description/commentary from the model

# Save the generated images
for i, image in enumerate(result["images"]):
    image.save(f"generated_city_{i}.png")
    # image.show()  # Uncomment to display the image

# Image modification with input image
input_image = Image.open("input_photo.jpg")  # Replace with your image path

image_messages = MessageBuilder().add_user("Add a futuristic flying car to this image.").add_image(input_image).build()

result, cost = generate(
    model="gemini/gemini-2.0-flash-exp-image-generation",
    messages=image_messages,
    temperature=0.7,
    max_tokens=4096
)

# Save the modified images
for i, image in enumerate(result["images"]):
    image.save(f"modified_image_{i}.png")

The generate function returns a dictionary containing both textual response and generated images, enabling multimodal AI capabilities in your applications.

TTS

from wraipperz.api.tts import create_tts_manager

tts_manager = create_tts_manager()

# Generate speech using OpenAI Realtime TTS
response = tts_manager.generate_speech(
    "openai_realtime",
    text="This is a demonstration of my voice capabilities!",
    output_path="realtime_output.mp3",
    voice="ballad",
    context="Speak in a extremelly calm, soft, and relaxed voice.",
    return_alignment=True,
    speed=1.1,
)

# Convert speech using ElevenLabs
# TODO add example

Environment Variables

Set up your API keys in environment variables to enable providers.

OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
GOOGLE_API_KEY=your_google_key
# ...  todo add all

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.