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,
    custom_model,
    provider  # New parameter for provider selection
):
    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}")

    # 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.")

    # If user provided a model, use that; otherwise, fall back to a default model
    model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
    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.")

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 new 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)"
)

# The custom_model_box is what the respond function sees as "custom_model"
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"
)

# Available providers as of April 2025
providers_list = [
    "hf-inference",  # Default Hugging Face Inference
    "cerebras",      # Cerebras provider
    "together",      # Together AI
    "sambanova",     # SambaNova
    "replicate",     # Replicate
    "fal-ai",        # Fal.ai
    "novita",        # Novita AI
    "black-forest-labs", # Black Forest Labs
    "cohere",        # Cohere
    "fireworks-ai",  # Fireworks AI
    "hyperbolic",    # Hyperbolic
    "nebius",        # Nebius
    "openai"         # OpenAI compatible endpoints
]

# Provider selection dropdown for better UX with many options
provider_dropdown = gr.Dropdown(
    choices=providers_list,
    value="hf-inference",
    label="Inference Provider",
    info="Select which inference provider to use. Uses your Hugging Face PRO credits."
)

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

demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        system_message_box,
        max_tokens_slider,
        temperature_slider,
        top_p_slider,
        frequency_penalty_slider,
        seed_slider,
        custom_model_box,
        provider_dropdown,  # Add provider selection to inputs
    ],
    fill_height=True,
    chatbot=chatbot,
    theme="Nymbo/Nymbo_Theme",
)
print("ChatInterface object created.")

with demo:
    with gr.Accordion("Model Selection", open=False):
        model_search_box = gr.Textbox(
            label="Filter Models",
            placeholder="Search for a featured model...",
            lines=1
        )
        print("Model search box created.")

        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",
        ]
        print("Models list initialized.")

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

        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)

        model_search_box.change(
            fn=filter_models,
            inputs=model_search_box,
            outputs=featured_model_radio
        )
        print("Model search box change event linked.")

        featured_model_radio.change(
            fn=set_custom_model_from_radio,
            inputs=featured_model_radio,
            outputs=custom_model_box
        )
        print("Featured model radio button change event linked.")
    
    # Add new accordion for advanced settings including provider selection
    with gr.Accordion("Advanced Settings", open=True):
        gr.Markdown("### Inference Provider Selection")
        gr.Markdown("Select which provider to use for inference. Uses your Hugging Face PRO credits.")
        # Provider dropdown is already included in the additional_inputs
        
        gr.Markdown("""
        ### Provider Information
        
        - **hf-inference**: Default Hugging Face Inference API
        - **cerebras**: Cerebras AI models - extremely fast inference (70x faster than GPUs)
        - **together**: Together AI models
        - **sambanova**: SambaNova models
        - **replicate**: Replicate models
        - **fal-ai**: Fal.ai models
        - **novita**: Novita AI
        - **black-forest-labs**: Black Forest Labs
        - **cohere**: Cohere models
        - **fireworks-ai**: Fireworks AI
        - **hyperbolic**: Hyperbolic models
        - **nebius**: Nebius models
        - **openai**: OpenAI compatible endpoints
        
        As a PRO user, you receive $2 of credits monthly across all providers.
        
        Note: Not all models are available on all providers. If you select a provider that doesn't support your chosen model, you'll get an error message.
        """)

print("Gradio interface initialized.")

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