app.py

import gradio as gr
from openai import OpenAI
import os

# Retrieve the access token from the environment variable
ACCESS_TOKEN = os.getenv("HF_TOKEN")
print("Access token loaded.")

# Initialize the OpenAI client with the Hugging Face Inference API endpoint
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)
print("OpenAI client initialized.")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    frequency_penalty,
    seed,
    custom_model
):
    """
    Respond function for ChatInterface.
    """
    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}")

    if seed == -1:
        seed = None

    # Construct the messages array
    messages = [{"role": "system", "content": system_message}]
    for val in history:
        user_part = val[0]
        assistant_part = val[1]
        if user_part:
            messages.append({"role": "user", "content": user_part})
        if assistant_part:
            messages.append({"role": "assistant", "content": assistant_part})

    messages.append({"role": "user", "content": message})

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

    for message_chunk in client.chat.completions.create(
        model=model_to_use,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
        frequency_penalty=frequency_penalty,
        seed=seed,
        messages=messages,
    ):
        token_text = message_chunk.choices[0].delta.content
        response += token_text
        yield response


# -------------------------
# GRADIO UI CONFIGURATION
# -------------------------

# Create a Chatbot component
chatbot = gr.Chatbot(
    height=600, 
    show_copy_button=True,
    placeholder="Select a model and begin chatting",
    likeable=True,
    layout="panel"
)

# Create textboxes/sliders for system prompt, tokens, etc.
system_message_box = gr.Textbox(value="", label="System message")
max_tokens_slider = gr.Slider(1, 4096, value=512, step=1, label="Max new tokens")
temperature_slider = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature")
top_p_slider = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-P")
frequency_penalty_slider = gr.Slider(-2.0, 2.0, value=0.0, step=0.1, label="Frequency Penalty")
seed_slider = gr.Slider(-1, 65535, value=-1, step=1, label="Seed (-1 for random)")
custom_model_box = gr.Textbox(value="", label="Custom Model",
                              info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.")

def set_custom_model_from_radio(selected):
    """
    Update the Custom Model textbox when a featured model is selected.
    """
    print(f"Featured model selected: {selected}")
    return selected


# Create a user textbox that we can reference
# This will become our "Message" input inside the ChatInterface
user_textbox = gr.MultimodalTextbox()

# No 'examples' here—because we want to keep the user's parameters unchanged
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
    ],
    fill_height=True,
    chatbot=chatbot,
    textbox=user_textbox,
    multimodal=True,
    concurrency_limit=20,
    theme="Nymbo/Nymbo_Theme",
    # No examples parameter used
    cache_examples=False
)
print("ChatInterface object created.")

with demo:
    # Featured models accordion
    with gr.Accordion("Featured Models", open=False):
        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-3.2-3B-Instruct",
            "meta-llama/Llama-3.2-1B-Instruct",
            "meta-llama/Llama-3.1-8B-Instruct",
            "NousResearch/Hermes-3-Llama-3.1-8B",
            "google/gemma-2-27b-it",
            "google/gemma-2-9b-it",
            "google/gemma-2-2b-it",
            "mistralai/Mistral-Nemo-Instruct-2407",
            "mistralai/Mixtral-8x7B-Instruct-v0.1",
            "mistralai/Mistral-7B-Instruct-v0.3",
            "Qwen/Qwen2.5-72B-Instruct",
            "Qwen/QwQ-32B-Preview",
            "PowerInfer/SmallThinker-3B-Preview",
            "HuggingFaceTB/SmolLM2-1.7B-Instruct",
            "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
            "microsoft/Phi-3.5-mini-instruct",
        ]

        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):
            filtered = [m for m in models_list if search_term.lower() in m.lower()]
            return gr.update(choices=filtered)

        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
        )

    # Example Prompts accordion
    with gr.Accordion("Example Prompts", open=False):
        ex1_btn = gr.Button("Example 1: 'Howdy, partner!'")
        ex2_btn = gr.Button("Example 2: 'What's your model name and who trained you?'")
        ex3_btn = gr.Button("Example 3: 'How many R's in Strawberry?'")

        # Helper function that returns an update for user_textbox
        def load_example(example_text):
            return gr.update(value=example_text)

        ex1_btn.click(fn=lambda: load_example("Howdy, partner!"), 
                      inputs=[],
                      outputs=user_textbox)

        ex2_btn.click(fn=lambda: load_example("What's your model name and who trained you?"), 
                      inputs=[],
                      outputs=user_textbox)

        ex3_btn.click(fn=lambda: load_example("How many R's are there in the word Strawberry?"), 
                      inputs=[],
                      outputs=user_textbox)

print("Gradio interface initialized.")

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