import gradio as gr
import gemini_gradio
import openai_gradio
import anthropic_gradio
import sambanova_gradio
import xai_gradio
import hyperbolic_gradio
import perplexity_gradio
import mistral_gradio
with gr.Blocks(fill_height=True) as demo:
with gr.Tab("Gemini"):
with gr.Row():
gemini_model = gr.Dropdown(
choices=[
'gemini-1.5-flash', # Fast and versatile performance
'gemini-1.5-flash-8b', # High volume, lower intelligence tasks
'gemini-1.5-pro', # Complex reasoning tasks
'gemini-exp-1114' # Quality improvements
],
value='gemini-1.5-pro', # Default to the most advanced model
label="Select Gemini Model",
interactive=True
)
gemini_interface = gr.load(
name=gemini_model.value,
src=gemini_gradio.registry,
fill_height=True
)
def update_gemini_model(new_model):
return gr.load(
name=new_model,
src=gemini_gradio.registry,
fill_height=True
)
gemini_model.change(
fn=update_gemini_model,
inputs=[gemini_model],
outputs=[gemini_interface]
)
with gr.Tab("ChatGPT"):
with gr.Row():
model_choice = gr.Dropdown(
choices=[
'gpt-4o', # Most advanced model
'gpt-4o-2024-08-06', # Latest snapshot
'gpt-4o-2024-05-13', # Original snapshot
'chatgpt-4o-latest', # Latest ChatGPT version
'gpt-4o-mini', # Small model
'gpt-4o-mini-2024-07-18', # Latest mini version
'o1-preview', # Reasoning model
'o1-preview-2024-09-12', # Latest o1 model snapshot
'o1-mini', # Faster reasoning model
'o1-mini-2024-09-12', # Latest o1-mini model snapshot
'gpt-4-turbo', # Latest GPT-4 Turbo model
'gpt-4-turbo-2024-04-09', # Latest GPT-4 Turbo snapshot
'gpt-4-turbo-preview', # GPT-4 Turbo preview model
'gpt-4-0125-preview', # GPT-4 Turbo preview model for laziness
'gpt-4-1106-preview', # Improved instruction following model
'gpt-4', # Standard GPT-4 model
'gpt-4-0613' # Snapshot of GPT-4 from June 2023
],
value='gpt-4o', # Default to the most advanced model
label="Select Model",
interactive=True
)
chatgpt_interface = gr.load(
name=model_choice.value,
src=openai_gradio.registry,
accept_token=True,
fill_height=True
)
def update_model(new_model):
return gr.load(
name=new_model,
src=openai_gradio.registry,
accept_token=True,
fill_height=True
)
model_choice.change(
fn=update_model,
inputs=[model_choice],
outputs=[chatgpt_interface]
)
with gr.Tab("Claude"):
with gr.Row():
claude_model = gr.Dropdown(
choices=[
'claude-3-5-sonnet-20241022', # Latest Sonnet
'claude-3-5-haiku-20241022', # Latest Haiku
'claude-3-opus-20240229', # Opus
'claude-3-sonnet-20240229', # Previous Sonnet
'claude-3-haiku-20240307' # Previous Haiku
],
value='claude-3-5-sonnet-20241022', # Default to latest Sonnet
label="Select Model",
interactive=True
)
claude_interface = gr.load(
name=claude_model.value,
src=anthropic_gradio.registry,
accept_token=True,
fill_height=True
)
def update_claude_model(new_model):
return gr.load(
name=new_model,
src=anthropic_gradio.registry,
accept_token=True,
fill_height=True
)
claude_model.change(
fn=update_claude_model,
inputs=[claude_model],
outputs=[claude_interface]
)
with gr.Tab("Meta Llama"):
with gr.Row():
llama_model = gr.Dropdown(
choices=[
'Meta-Llama-3.2-1B-Instruct', # Llama 3.2 1B
'Meta-Llama-3.2-3B-Instruct', # Llama 3.2 3B
'Llama-3.2-11B-Vision-Instruct', # Llama 3.2 11B
'Llama-3.2-90B-Vision-Instruct', # Llama 3.2 90B
'Meta-Llama-3.1-8B-Instruct', # Llama 3.1 8B
'Meta-Llama-3.1-70B-Instruct', # Llama 3.1 70B
'Meta-Llama-3.1-405B-Instruct' # Llama 3.1 405B
],
value='Llama-3.2-90B-Vision-Instruct', # Default to the most advanced model
label="Select Llama Model",
interactive=True
)
llama_interface = gr.load(
name=llama_model.value,
src=sambanova_gradio.registry,
multimodal=True,
fill_height=True
)
def update_llama_model(new_model):
return gr.load(
name=new_model,
src=sambanova_gradio.registry,
multimodal=True,
fill_height=True
)
llama_model.change(
fn=update_llama_model,
inputs=[llama_model],
outputs=[llama_interface]
)
gr.Markdown("**Note:** You need to use a SambaNova API key from [SambaNova Cloud](https://cloud.sambanova.ai/).")
with gr.Tab("Grok"):
gr.load(
name='grok-beta',
src=xai_gradio.registry,
accept_token=True,
fill_height=True
)
with gr.Tab("Qwen2.5 72B"):
gr.load(
name='Qwen/Qwen2.5-72B-Instruct',
src=hyperbolic_gradio.registry,
fill_height=True
)
gr.Markdown("""
**Note:** This model is supported by Hyperbolic. You need to use a Hyperbolic API key from [Hyperbolic](https://app.hyperbolic.xyz/).
""")
with gr.Tab("Perplexity"):
with gr.Row():
perplexity_model = gr.Dropdown(
choices=[
# Sonar Models (Online)
'llama-3.1-sonar-small-128k-online', # 8B params
'llama-3.1-sonar-large-128k-online', # 70B params
'llama-3.1-sonar-huge-128k-online', # 405B params
# Sonar Models (Chat)
'llama-3.1-sonar-small-128k-chat', # 8B params
'llama-3.1-sonar-large-128k-chat', # 70B params
# Open Source Models
'llama-3.1-8b-instruct', # 8B params
'llama-3.1-70b-instruct' # 70B params
],
value='llama-3.1-sonar-large-128k-online', # Default to large online model
label="Select Perplexity Model",
interactive=True
)
perplexity_interface = gr.load(
name=perplexity_model.value,
src=perplexity_gradio.registry,
accept_token=True,
fill_height=True
)
def update_perplexity_model(new_model):
return gr.load(
name=new_model,
src=perplexity_gradio.registry,
accept_token=True,
fill_height=True
)
perplexity_model.change(
fn=update_perplexity_model,
inputs=[perplexity_model],
outputs=[perplexity_interface]
)
gr.Markdown("""
**Note:** Models are grouped into three categories:
- **Sonar Online Models**: Include search capabilities (beta access required)
- **Sonar Chat Models**: Standard chat models
- **Open Source Models**: Based on Hugging Face implementations
For access to Online LLMs features, please fill out the [beta access form](https://perplexity.typeform.com/apiaccessform?typeform-source=docs.perplexity.ai).
""")
with gr.Tab("DeepSeek-V2.5"):
gr.load(
name='deepseek-ai/DeepSeek-V2.5',
src=hyperbolic_gradio.registry,
fill_height=True
)
gr.Markdown("""
**Note:** This model is supported by Hyperbolic. You need to use a Hyperbolic API key from [Hyperbolic](https://app.hyperbolic.xyz/).
""")
with gr.Tab("Mistral"):
with gr.Row():
mistral_model = gr.Dropdown(
choices=[
# Premier Models
'mistral-large-latest', # Top-tier reasoning model (128k)
'pixtral-large-latest', # Frontier-class multimodal model (128k)
'ministral-3b-latest', # Best edge model (128k)
'ministral-8b-latest', # High performance edge model (128k)
'mistral-small-latest', # Enterprise-grade small model (32k)
'codestral-latest', # Code-specialized model (32k)
'mistral-embed', # Semantic text representation (8k)
'mistral-moderation-latest', # Content moderation service (8k)
# Free Models
'pixtral-12b-2409', # Free 12B multimodal model (128k)
'open-mistral-nemo', # Multilingual model (128k)
'open-codestral-mamba' # Mamba-based coding model (256k)
],
value='pixtral-large-latest', # pixtral for vision
label="Select Mistral Model",
interactive=True
)
mistral_interface = gr.load(
name=mistral_model.value,
src=mistral_gradio.registry,
fill_height=True
)
def update_mistral_model(new_model):
return gr.load(
name=new_model,
src=mistral_gradio.registry,
fill_height=True
)
mistral_model.change(
fn=update_mistral_model,
inputs=[mistral_model],
outputs=[mistral_interface],
)
gr.Markdown("""
**Note:** You need a Mistral API key to use these models. Get one at [Mistral AI Platform](https://console.mistral.ai/).
Models are grouped into two categories:
- **Premier Models**: Require a paid API key
- **Free Models**: Available with free API keys
Each model has different context window sizes (from 8k to 256k tokens) and specialized capabilities.
""")
demo.launch(ssr_mode=False)
