ai-gradio 0.2.3

A Python package for creating Gradio applications with AI models

ai-gradio

A Python package that makes it easy for developers to create machine learning apps powered by OpenAI, Google's Gemini models, Anthropic's Claude, LumaAI, CrewAI, XAI's Grok, and Hyperbolic and more.

Installation

You can install ai-gradio with different providers:

# Install with OpenAI support
pip install 'ai-gradio[openai]'

# Install with Gemini support  
pip install 'ai-gradio[gemini]'

# Install with CrewAI support
pip install 'ai-gradio[crewai]'

# Install with Anthropic support
pip install 'ai-gradio[anthropic]'

# Install with LumaAI support
pip install 'ai-gradio[lumaai]'

# Install with XAI support
pip install 'ai-gradio[xai]'

# Install with Cohere support
pip install 'ai-gradio[cohere]'

# Install with SambaNova support
pip install 'ai-gradio[sambanova]'

# Install with Hyperbolic support
pip install 'ai-gradio[hyperbolic]'

# Install with all providers
pip install 'ai-gradio[all]'

# Installation additions:
pip install 'ai-gradio[fireworks]'
pip install 'ai-gradio[together]'
pip install 'ai-gradio[qwen]'

# Install with DeepSeek support
pip install 'ai-gradio[deepseek]'

# Install with SmolagentsAI support
pip install 'ai-gradio[smolagents]'

# Install with Groq support
pip install 'ai-gradio[groq]'

Basic Usage

First, set your API key in the environment:

For OpenAI:

export OPENAI_API_KEY=<your token>

For Gemini:

export GEMINI_API_KEY=<your token>

For Anthropic:

export ANTHROPIC_API_KEY=<your token>

For LumaAI:

export LUMAAI_API_KEY=<your token>

For XAI:

export XAI_API_KEY=<your token>

For Cohere:

export COHERE_API_KEY=<your token>

For SambaNova:

export SAMBANOVA_API_KEY=<your token>

For Hyperbolic:

export HYPERBOLIC_API_KEY=<your token>

For DeepSeek:

export DEEPSEEK_API_KEY=<your token>

Then in a Python file:

import gradio as gr
import ai_gradio

# Create a Gradio interface
gr.load(
    name='openai:gpt-4-turbo',  # or 'gemini:gemini-1.5-flash' for Gemini, or 'xai:grok-beta' for Grok
    src=ai_gradio.registry,
    title='AI Chat',
    description='Chat with an AI model'
).launch()

Features

Text Chat

Basic text chat is supported for all text models. The interface provides a chat-like experience where you can have conversations with the AI model.

Voice Chat (OpenAI only)

Voice chat is supported for OpenAI realtime models. You can enable it in two ways:

# Using a realtime model
gr.load(
    name='openai:gpt-4o-realtime-preview-2024-10-01',
    src=ai_gradio.registry
).launch()

# Or explicitly enabling voice chat with any realtime model
gr.load(
    name='openai:gpt-4o-mini-realtime-preview-2024-12-17',
    src=ai_gradio.registry,
    enable_voice=True
).launch()

Voice Chat Configuration

For voice chat functionality, you'll need:

1. OpenAI API key (required):

export OPENAI_API_KEY=<your OpenAI token>

2. Twilio credentials (recommended for better WebRTC performance):

export TWILIO_ACCOUNT_SID=<your Twilio account SID>
export TWILIO_AUTH_TOKEN=<your Twilio auth token>

You can get Twilio credentials by:

• Creating a free account at Twilio
• Finding your Account SID and Auth Token in the Twilio Console

Without Twilio credentials, voice chat will still work but might have connectivity issues in some network environments.

Gemini Code Generator Interface

import gradio as gr
import ai_gradio

# Create a code generation interface with Gemini
gr.load(
    name='gemini:gemini-pro',
    src=ai_gradio.registry,
    coder=True,  # Enable code generation interface
    title='Gemini Code Generator',
    description='Generate web applications with Gemini'
).launch()

This creates an interactive code generation interface with:

• Input area for describing the desired web application
• Live preview of generated code
• Example templates
• System prompt configuration
• Code history tracking
• Real-time code preview

Example prompts:

examples = [
    "Create a button that changes color when clicked",
    "Create a simple todo list with add/remove functionality",
    "Create a countdown timer with start/pause/reset controls"
]

Video Chat (Gemini only)

Video chat is supported for Gemini models. You can enable it by setting enable_video=True:

gr.load(
    name='gemini:gemini-1.5-flash',
    src=ai_gradio.registry,
    enable_video=True
).launch()

Text Generation with DeepSeek

DeepSeek models support text generation and coding assistance:

gr.load(
    name='deepseek:deepseek-chat',
    src=ai_gradio.registry,
    title='DeepSeek Chat',
    description='Chat with DeepSeek'
).launch()

# For code assistance
gr.load(
    name='deepseek:deepseek-coder',
    src=ai_gradio.registry,
    title='DeepSeek Coder',
    description='Get coding help from DeepSeek'
).launch()

# For vision tasks
gr.load(
    name='deepseek:deepseek-vision',
    src=ai_gradio.registry,
    title='DeepSeek Vision',
    description='Visual understanding with DeepSeek'
).launch()

Text Generation with Anthropic Claude

Anthropic's Claude models are supported for text generation:

gr.load(
    name='anthropic:claude-3-opus-20240229',
    src=ai_gradio.registry,
    title='Claude Chat',
    description='Chat with Claude'
).launch()

AI Video and Image Generation with LumaAI

LumaAI support allows you to generate videos and images from text prompts:

# For video generation
gr.load(
    name='lumaai:dream-machine',
    src=ai_gradio.registry,
    title='LumaAI Video Generation'
).launch()

# For image generation
gr.load(
    name='lumaai:photon-1',
    src=ai_gradio.registry,
    title='LumaAI Image Generation'
).launch()

Text Generation with Hyperbolic

Hyperbolic models support various LLMs including DeepSeek, LLaMA, and Qwen:

# Using DeepSeek V3
gr.load(
    name='hyperbolic:deepseek-ai/DeepSeek-V3',
    src=ai_gradio.registry,
    title='DeepSeek Chat',
    description='Chat with DeepSeek V3'
).launch()

# Using LLaMA 3.3
gr.load(
    name='hyperbolic:meta-llama/llama-3.3-70b',
    src=ai_gradio.registry,
    title='LLaMA Chat',
    description='Chat with LLaMA 3.3'
).launch()

# Using Qwen Coder
gr.load(
    name='hyperbolic:Qwen/qwen2.5-coder-32b',
    src=ai_gradio.registry,
    title='Qwen Coder',
    description='Get coding help from Qwen'
).launch()

smolagents

smolagents support provides an intelligent assistant with web search capabilities:

gr.load(
    name='smolagents:meta-llama/Llama-3.1-8B-Instruct',
    src=ai_gradio.registry,
    title='smolagents Assistant',
    description='Agent Assistant'
).launch()

The SmolagentsAI integration includes:

• Interactive code generation
• Web search integration via DuckDuckGo
• Step-by-step thought process visibility
• Error handling and debugging assistance
• Real-time streaming responses

SmolagentsAI Models

• meta-llama/Llama-3.1-8B-Instruct
• meta-llama/Llama-3.1-70B-Instruct
• meta-llama/Llama-3.2-70B-Instruct

AI Agent Teams with CrewAI

CrewAI support allows you to create teams of AI agents that work together to solve complex tasks. Enable it by using the CrewAI provider:

gr.load(
    name='crewai:gpt-4-turbo',
    src=ai_gradio.registry,
    title='AI Team Chat',
    description='Chat with a team of specialized AI agents'
).launch()

CrewAI Types

The CrewAI integration supports different specialized agent teams:

• support: A team of support agents that help answer questions, including:

  • Senior Support Representative
  • Support Quality Assurance Specialist

• article: A team of content creation agents, including:

  • Content Planner
  • Content Writer
  • Editor

You can specify the crew type when creating the interface:

gr.load(
    name='crewai:gpt-4-turbo',
    src=ai_gradio.registry,
    crew_type='article',  # or 'support'
    title='AI Writing Team',
    description='Create articles with a team of AI agents'
).launch()

When using the support crew type, you can provide a documentation URL that the agents will reference when answering questions. The interface will automatically show a URL input field.

Provider Selection

When loading a model, you can specify the provider explicitly using the format provider:model_name.

# Explicit provider
gr.load(
    name='gemini:gemini-pro',
    src=ai_gradio.registry
).launch()

Customization

You can customize the interface by adding examples, changing the title, or adding a description:

gr.load(
    name='gpt-4-turbo',
    src=ai_gradio.registry,
    title='Custom AI Chat',
    description='Chat with an AI assistant',
    examples=[
        "Explain quantum computing to a 5-year old",
        "What's the difference between machine learning and AI?"
    ]
).launch()

Composition

You can combine multiple models in a single interface using Gradio's Blocks:

import gradio as gr
import ai_gradio

with gr.Blocks() as demo:
    with gr.Tab("GPT-4"):
        gr.load('gpt-4-turbo', src=ai_gradio.registry)
    with gr.Tab("Gemini"):
        gr.load('gemini-pro', src=ai_gradio.registry)
    with gr.Tab("Claude"):
        gr.load('anthropic:claude-3-opus-20240229', src=ai_gradio.registry)
    with gr.Tab("LumaAI"):
        gr.load('lumaai:dream-machine', src=ai_gradio.registry)
    with gr.Tab("CrewAI"):
        gr.load('crewai:gpt-4-turbo', src=ai_gradio.registry)
    with gr.Tab("Grok"):
        gr.load('xai:grok-beta', src=ai_gradio.registry)

demo.launch()

Supported Models

OpenAI Models

• gpt-4-turbo
• gpt-4
• gpt-3.5-turbo

Gemini Models

• gemini-pro
• gemini-pro-vision
• gemini-2.0-flash-exp

Anthropic Models

• claude-3-opus-20240229
• claude-3-sonnet-20240229
• claude-3-haiku-20240307
• claude-2.1
• claude-2.0
• claude-instant-1.2

LumaAI Models

• dream-machine (video generation)
• photon-1 (image generation)
• photon-flash-1 (fast image generation)

CrewAI Models

• crewai:gpt-4-turbo
• crewai:gpt-4
• crewai:gpt-3.5-turbo

XAI Models

• grok-beta
• grok-vision-beta

Cohere Models

• command
• command-light
• command-nightly
• command-r

SambaNova Models

• llama2-70b-chat
• llama2-13b-chat
• llama2-7b-chat
• mixtral-8x7b-chat
• mistral-7b-chat

Fireworks Models

• whisper-v3
• whisper-v3-turbo
• f1-preview
• f1-mini

Together Models

• meta-llama/Llama-Vision-Free
• meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo
• meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo
• meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
• meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
• meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
• meta-llama/Meta-Llama-3-8B-Instruct-Turbo
• meta-llama/Meta-Llama-3-70B-Instruct-Turbo
• meta-llama/Llama-3.2-3B-Instruct-Turbo
• meta-llama/Meta-Llama-3-8B-Instruct-Lite
• meta-llama/Meta-Llama-3-70B-Instruct-Lite

Qwen Models

• qwen-turbo-latest
• qwen-turbo
• qwen-plus
• qwen-max
• qwen1.5-110b-chat
• qwen1.5-72b-chat
• qwen1.5-32b-chat
• qwen1.5-14b-chat
• qwen1.5-7b-chat
• qwq-32b-preview
• qvq-72b-preview

Hyperbolic Models

• meta-llama/llama-3.3-70b
• Qwen/QwQ-32B-Preview
• Qwen/qwen2.5-coder-32b
• meta-llama/llama-3.2-3b
• Qwen/qwen2.5-72b
• deepseek/deepseek-v2.5
• meta-llama/llama-3-70b
• hermes/hermes-3-70b
• meta-llama/llama-3.1-405b
• meta-llama/llama-3.1-70b
• meta-llama/llama-3.1-8b

DeepSeek Models

• deepseek-chat
• deepseek-coder
• deepseek-vision

Requirements

• Python 3.10 or higher
• gradio >= 5.9.1

Additional dependencies are installed based on your chosen provider:

• OpenAI: openai>=1.58.1
• Gemini: google-generativeai
• CrewAI: crewai>=0.1.0, langchain>=0.1.0, langchain-openai>=0.0.2, crewai-tools>=0.0.1
• Anthropic: anthropic>=1.0.0
• LumaAI: lumaai>=0.0.3
• XAI: xai>=0.1.0
• Cohere: cohere>=5.0.0
• DeepSeek: openai>=1.58.1
• Hyperbolic: openai>=1.58.1

Fireworks: openai>=1.58.1

Together: openai>=1.58.1

Qwen: openai>=1.58.1

Hyperbolic: openai>=1.58.1

Troubleshooting

If you get a 401 authentication error, make sure your API key is properly set. You can set it manually in your Python session:

import os

# For OpenAI
os.environ["OPENAI_API_KEY"] = "your-api-key"

# For Gemini
os.environ["GEMINI_API_KEY"] = "your-api-key"

# For Anthropic
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

# For LumaAI
os.environ["LUMAAI_API_KEY"] = "your-api-key"

# For XAI
os.environ["XAI_API_KEY"] = "your-api-key"

# For Cohere
os.environ["COHERE_API_KEY"] = "your-api-key"

# For SambaNova
os.environ["SAMBANOVA_API_KEY"] = "your-api-key"

# Environment variables additions:
export FIREWORKS_API_KEY=<your token>
export TOGETHER_API_KEY=<your token>
export QWEN_API_KEY=<your token>
export HYPERBOLIC_API_KEY=<your token>

# Additional troubleshooting environment variables:
os.environ["FIREWORKS_API_KEY"] = "your-api-key"
os.environ["TOGETHER_API_KEY"] = "your-api-key"
os.environ["QWEN_API_KEY"] = "your-api-key"
os.environ["HYPERBOLIC_API_KEY"] = "your-api-key"
os.environ["DEEPSEEK_API_KEY"] = "your-api-key"

### No Providers Error
If you see an error about no providers being installed, make sure you've installed the package with the desired provider:

```bash
# Install with OpenAI support
pip install 'ai-gradio[openai]'

# Install with Gemini support
pip install 'ai-gradio[gemini]'

# Install with CrewAI support
pip install 'ai-gradio[crewai]'

# Install with Anthropic support
pip install 'ai-gradio[anthropic]'

# Install with LumaAI support
pip install 'ai-gradio[lumaai]'

# Install with XAI support
pip install 'ai-gradio[xai]'

# Install with Cohere support
pip install 'ai-gradio[cohere]'

# Install all providers
pip install 'ai-gradio[all]'

Optional Dependencies

For voice chat functionality:

• gradio-webrtc
• numba==0.60.0
• pydub
• librosa
• websockets
• twilio
• gradio_webrtc[vad]
• numpy

For video chat functionality:

• opencv-python
• Pillow

Examples

Basic Chat Interface

import gradio as gr
import ai_gradio

# Simple chat with GPT-4
gr.load(
    name='openai:gpt-4-turbo',
    src=ai_gradio.registry,
    title='GPT-4 Chat',
    description='Chat with GPT-4'
).launch()

Multi-Model Interface

import gradio as gr
import ai_gradio

with gr.Blocks() as demo:
    gr.Markdown("# AI Model Hub")
    
    with gr.Tab("Text Models"):
        with gr.Tab("GPT-4"):
            gr.load('openai:gpt-4-turbo', src=ai_gradio.registry)
        with gr.Tab("Claude"):
            gr.load('anthropic:claude-3-opus-20240229', src=ai_gradio.registry)
        with gr.Tab("DeepSeek"):
            gr.load('deepseek:deepseek-chat', src=ai_gradio.registry)
            
    with gr.Tab("Vision Models"):
        with gr.Tab("Gemini Vision"):
            gr.load('gemini:gemini-pro-vision', src=ai_gradio.registry, enable_video=True)
        with gr.Tab("LumaAI"):
            gr.load('lumaai:dream-machine', src=ai_gradio.registry)
            
    with gr.Tab("Specialized"):
        with gr.Tab("Code Assistant"):
            gr.load('deepseek:deepseek-coder', src=ai_gradio.registry)
        with gr.Tab("AI Team"):
            gr.load('crewai:gpt-4-turbo', src=ai_gradio.registry, crew_type='article')

demo.launch()

Voice-Enabled Chat

import gradio as gr
import ai_gradio

# Enable voice chat with GPT-4
gr.load(
    name='openai:gpt-4-turbo',
    src=ai_gradio.registry,
    enable_voice=True,
    title='Voice Chat',
    description='Talk with GPT-4'
).launch()

Custom Examples and Styling

import gradio as gr
import ai_gradio

# Chat interface with custom examples and CSS
gr.load(
    name='gemini:gemini-pro',
    src=ai_gradio.registry,
    title='Gemini Pro Assistant',
    description='Your AI research companion',
    examples=[
        "Explain quantum entanglement",
        "What are the main differences between RNA and DNA?",
        "How does a neural network learn?"
    ],
    css=".gradio-container {background-color: #f0f8ff}"
).launch()

AI Team for Content Creation

import gradio as gr
import ai_gradio

# CrewAI setup for article writing
gr.load(
    name='crewai:gpt-4-turbo',
    src=ai_gradio.registry,
    crew_type='article',
    title='AI Writing Team',
    description='Collaborate with AI agents to create articles',
    examples=[
        "Write a blog post about sustainable energy",
        "Create a technical tutorial about Docker containers"
    ]
).launch()

Support Team with Documentation

import gradio as gr
import ai_gradio

# CrewAI support team with documentation reference
gr.load(
    name='crewai:gpt-4-turbo',
    src=ai_gradio.registry,
    crew_type='support',
    title='AI Support Team',
    description='Get help from AI support agents',
    documentation_url='https://docs.example.com'
).launch()

For Groq:

export GROQ_API_KEY=<your token>