app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os
import json

ACCESS_TOKEN = os.getenv("HF_TOKEN")
print("Access token loaded.")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    frequency_penalty,
    seed,
    provider,  # Provider selection (moved up)
    custom_model,  # Custom Model (moved down)
    model_search_term,
    selected_model
):
    print(f"Received message: {message}")
    print(f"History: {history}")
    print(f"System message: {system_message}")
    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
    print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
    print(f"Selected model (custom_model): {custom_model}")
    print(f"Selected provider: {provider}")
    print(f"Model search term: {model_search_term}")
    print(f"Selected model from radio: {selected_model}")

    # Initialize the Inference Client with the provider
    # Provider is specified during initialization, not in the method call
    client = InferenceClient(token=ACCESS_TOKEN, provider=provider)
    print(f"Hugging Face Inference Client initialized with {provider} provider.")

    # Convert seed to None if -1 (meaning random)
    if seed == -1:
        seed = None

    # Prepare messages in the format expected by the API
    messages = [{"role": "system", "content": system_message}]
    print("Initial messages array constructed.")

    # Add conversation history to the context
    for val in history:
        user_part = val[0]
        assistant_part = val[1]
        if user_part:
            messages.append({"role": "user", "content": user_part})
            print(f"Added user message to context: {user_part}")
        if assistant_part:
            messages.append({"role": "assistant", "content": assistant_part})
            print(f"Added assistant message to context: {assistant_part}")

    # Append the latest user message
    messages.append({"role": "user", "content": message})
    print("Latest user message appended.")

    # Determine which model to use, prioritizing custom_model if provided
    model_to_use = custom_model.strip() if custom_model.strip() != "" else selected_model
    print(f"Model selected for inference: {model_to_use}")

    # Start with an empty string to build the response as tokens stream in
    response = ""
    print(f"Sending request to {provider} provider.")

    # Prepare parameters for the chat completion request
    parameters = {
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "frequency_penalty": frequency_penalty,
    }
    
    if seed is not None:
        parameters["seed"] = seed

    # Use the InferenceClient for making the request
    try:
        # Create a generator for the streaming response
        # The provider is already set when initializing the client
        stream = client.chat_completion(
            model=model_to_use,
            messages=messages,
            stream=True,
            **parameters  # Pass all other parameters
        )
        
        # Process the streaming response
        for chunk in stream:
            if hasattr(chunk, 'choices') and len(chunk.choices) > 0:
                # Extract the content from the response
                if hasattr(chunk.choices[0], 'delta') and hasattr(chunk.choices[0].delta, 'content'):
                    token_text = chunk.choices[0].delta.content
                    if token_text:
                        print(f"Received token: {token_text}")
                        response += token_text
                        yield response
    except Exception as e:
        print(f"Error during inference: {e}")
        response += f"\nError: {str(e)}"
        yield response

    print("Completed response generation.")

# GRADIO UI

chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", layout="panel")
print("Chatbot interface created.")

# Basic input components
system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")

max_tokens_slider = gr.Slider(
    minimum=1,
    maximum=4096,
    value=512,
    step=1,
    label="Max tokens"
)
temperature_slider = gr.Slider(
    minimum=0.1,
    maximum=4.0,
    value=0.7,
    step=0.1,
    label="Temperature"
)
top_p_slider = gr.Slider(
    minimum=0.1,
    maximum=1.0,
    value=0.95,
    step=0.05,
    label="Top-P"
)
frequency_penalty_slider = gr.Slider(
    minimum=-2.0,
    maximum=2.0,
    value=0.0,
    step=0.1,
    label="Frequency Penalty"
)
seed_slider = gr.Slider(
    minimum=-1,
    maximum=65535,
    value=-1,
    step=1,
    label="Seed (-1 for random)"
)

# Provider selection with model links
providers_list = [
    "hf-inference",  # Default Hugging Face Inference
    "cerebras",      # Cerebras provider
    "together",      # Together AI
    "sambanova",     # SambaNova
    "novita",        # Novita AI
    "cohere",        # Cohere
    "fireworks-ai",  # Fireworks AI
    "hyperbolic",    # Hyperbolic
    "nebius",        # Nebius
    "openai"         # OpenAI compatible endpoints
]

# Define provider selection with markdown links
provider_radio = gr.Radio(
    choices=providers_list,
    value="hf-inference",
    label="Inference Provider",
    info="Select which inference provider to use. Uses your Hugging Face PRO credits."
)

# Create markdown links for each provider
provider_links = {
    "hf-inference": "View all models hosted by [Hugging Face](https://huggingface.co/models?inference_provider=hf-inference&pipeline_tag=text-generation&sort=trending)",
    "cerebras": "View all models hosted by [Cerebras](https://huggingface.co/models?inference_provider=cerebras&pipeline_tag=text-generation&sort=trending)",
    "together": "View all models hosted by [Together AI](https://huggingface.co/models?inference_provider=together&pipeline_tag=text-generation&sort=trending)",
    "sambanova": "View all models hosted by [SambaNova](https://huggingface.co/models?inference_provider=sambanova&pipeline_tag=text-generation&sort=trending)",
    "novita": "View all models hosted by [Novita AI](https://huggingface.co/models?inference_provider=novita&pipeline_tag=text-generation&sort=trending)",
    "cohere": "View all models hosted by [Cohere](https://huggingface.co/models?inference_provider=cohere&pipeline_tag=text-generation&sort=trending)",
    "fireworks-ai": "View all models hosted by [Fireworks AI](https://huggingface.co/models?inference_provider=fireworks-ai&pipeline_tag=text-generation&sort=trending)",
    "hyperbolic": "View all models hosted by [Hyperbolic](https://huggingface.co/models?inference_provider=hyperbolic&pipeline_tag=text-generation&sort=trending)",
    "nebius": "View all models hosted by [Nebius](https://huggingface.co/models?inference_provider=nebius&pipeline_tag=text-generation&sort=trending)",
    "openai": "View all models hosted by [OpenAI compatible endpoints](https://huggingface.co/models?inference_provider=openai&pipeline_tag=text-generation&sort=trending)",
}

# Provider links markdown
provider_links_md = gr.Markdown(provider_links["hf-inference"])

# Custom model box (moved down)
custom_model_box = gr.Textbox(
    value="",
    label="Custom Model",
    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
    placeholder="meta-llama/Llama-3.3-70B-Instruct"
)

# Model selection components
model_search_box = gr.Textbox(
    label="Filter Models",
    placeholder="Search for a featured model...",
    lines=1
)

models_list = [
    "meta-llama/Llama-3.3-70B-Instruct",
    "meta-llama/Llama-3.1-70B-Instruct",
    "meta-llama/Llama-3.0-70B-Instruct",
    "meta-llama/Llama-3.2-3B-Instruct",
    "meta-llama/Llama-3.2-1B-Instruct",
    "meta-llama/Llama-3.1-8B-Instruct",
    "NousResearch/Hermes-3-Llama-3.1-8B",
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
    "mistralai/Mistral-Nemo-Instruct-2407",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "mistralai/Mistral-7B-Instruct-v0.3",
    "mistralai/Mistral-7B-Instruct-v0.2",
    "Qwen/Qwen3-235B-A22B",
    "Qwen/Qwen3-32B",
    "Qwen/Qwen2.5-72B-Instruct",
    "Qwen/Qwen2.5-3B-Instruct",
    "Qwen/Qwen2.5-0.5B-Instruct",
    "Qwen/QwQ-32B",
    "Qwen/Qwen2.5-Coder-32B-Instruct",
    "microsoft/Phi-3.5-mini-instruct",
    "microsoft/Phi-3-mini-128k-instruct",
    "microsoft/Phi-3-mini-4k-instruct",
    "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
    "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
    "HuggingFaceH4/zephyr-7b-beta",
    "HuggingFaceTB/SmolLM2-360M-Instruct",
    "tiiuae/falcon-7b-instruct",
    "01-ai/Yi-1.5-34B-Chat",
]

featured_model_radio = gr.Radio(
    label="Select a model below",
    choices=models_list,
    value="meta-llama/Llama-3.3-70B-Instruct",
    interactive=True
)

def filter_models(search_term):
    print(f"Filtering models with search term: {search_term}")
    filtered = [m for m in models_list if search_term.lower() in m.lower()]
    print(f"Filtered models: {filtered}")
    return gr.update(choices=filtered)

def set_custom_model_from_radio(selected):
    """
    This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
    We will update the Custom Model text box with that selection automatically.
    """
    print(f"Featured model selected: {selected}")
    return selected

# Update provider links when provider selection changes
def update_provider_info(provider):
    return provider_links[provider]

# Custom layout with Blocks
with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    chatbot_ui = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", layout="panel")
    
    with gr.Row():
        with gr.Column():
            msg = gr.Textbox(
                scale=4,
                show_label=False,
                placeholder="Enter text and press enter",
                container=False,
            )
            submit_btn = gr.Button("Submit", variant="primary")
    
    with gr.Accordion("Additional Inputs", open=False):
        system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
        
        with gr.Row():
            with gr.Column():
                max_tokens_slider = gr.Slider(
                    minimum=1,
                    maximum=4096,
                    value=512,
                    step=1,
                    label="Max tokens"
                )
                temperature_slider = gr.Slider(
                    minimum=0.1,
                    maximum=4.0,
                    value=0.7,
                    step=0.1,
                    label="Temperature"
                )
                top_p_slider = gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.95,
                    step=0.05,
                    label="Top-P"
                )
            
            with gr.Column():
                frequency_penalty_slider = gr.Slider(
                    minimum=-2.0,
                    maximum=2.0,
                    value=0.0,
                    step=0.1,
                    label="Frequency Penalty"
                )
                seed_slider = gr.Slider(
                    minimum=-1,
                    maximum=65535,
                    value=-1,
                    step=1,
                    label="Seed (-1 for random)"
                )
        
        # Provider selection section with markdown links
        with gr.Group():
            provider_radio = gr.Radio(
                choices=providers_list,
                value="hf-inference",
                label="Inference Provider",
                info="Select which inference provider to use. Uses your Hugging Face PRO credits."
            )
            provider_links_md = gr.Markdown(provider_links["hf-inference"])
            
            # Connect provider radio to update markdown links
            provider_radio.change(
                fn=update_provider_info,
                inputs=provider_radio,
                outputs=provider_links_md
            )
        
        # Custom model box (moved below provider selection)
        custom_model_box = gr.Textbox(
            value="",
            label="Custom Model",
            info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
            placeholder="meta-llama/Llama-3.3-70B-Instruct"
        )
        
        # Model filter and selection
        model_search_box = gr.Textbox(
            label="Filter Models",
            placeholder="Search for a featured model...",
            lines=1
        )
        
        featured_model_radio = gr.Radio(
            label="Select a model below",
            choices=models_list,
            value="meta-llama/Llama-3.3-70B-Instruct",
            interactive=True
        )
    
    # Connect model filter and selection events
    model_search_box.change(
        fn=filter_models,
        inputs=model_search_box,
        outputs=featured_model_radio
    )
    
    featured_model_radio.change(
        fn=set_custom_model_from_radio,
        inputs=featured_model_radio,
        outputs=custom_model_box
    )
    
    # Chat history state
    history_state = gr.State([])
    
    # Connect chat functionality
    submit_btn.click(
        fn=respond,
        inputs=[
            msg,
            history_state,
            system_message_box,
            max_tokens_slider,
            temperature_slider,
            top_p_slider,
            frequency_penalty_slider,
            seed_slider,
            provider_radio,     # Provider selection (moved up)
            custom_model_box,   # Custom Model (moved down)
            model_search_box,
            featured_model_radio
        ],
        outputs=[chatbot_ui, history_state],
        show_progress=True,
    )
    
    msg.submit(
        fn=respond,
        inputs=[
            msg,
            history_state,
            system_message_box,
            max_tokens_slider,
            temperature_slider,
            top_p_slider,
            frequency_penalty_slider,
            seed_slider,
            provider_radio,     # Provider selection (moved up)
            custom_model_box,   # Custom Model (moved down)
            model_search_box,
            featured_model_radio
        ],
        outputs=[chatbot_ui, history_state],
        show_progress=True,
    )

print("Gradio interface initialized.")

if __name__ == "__main__":
    print("Launching the demo application.")
    demo.launch(show_api=True)