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,
    model,
    custom_model
):
    """
    This function handles the chatbot response. It takes in:
    - message: the user's new message
    - history: the list of previous messages, each as a tuple (user_msg, assistant_msg)
    - system_message: the system prompt
    - max_tokens: the maximum number of tokens to generate in the response
    - temperature: sampling temperature
    - top_p: top-p (nucleus) sampling
    - frequency_penalty: penalize repeated tokens in the output
    - seed: a fixed seed for reproducibility; -1 will mean 'random'
    - model: the selected model
    - custom_model: a custom model provided by the user
    """

    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"Model: {model}, Custom Model: {custom_model}")

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

    # Use custom model if provided, otherwise use selected model
    if custom_model.strip() != "":
        model_to_use = custom_model.strip()
    else:
        model_to_use = model

    # Construct the messages array required by the API
    messages = [{"role": "system", "content": system_message}]

    # 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})

    # Start with an empty string to build the response as tokens stream in
    response = ""
    print("Sending request to OpenAI API.")

    # Make the streaming request to the HF Inference API via openai-like client
    for message_chunk in client.chat.completions.create(
        model=model_to_use,   # Use the selected or custom model
        max_tokens=max_tokens,
        stream=True,  # Stream the response
        temperature=temperature,
        top_p=top_p,
        frequency_penalty=frequency_penalty,
        seed=seed,
        messages=messages,
    ):
        # Extract the token text from the response chunk
        token_text = message_chunk.choices[0].delta.content
        print(f"Received token: {token_text}")
        response += token_text
        yield response

    print("Completed response generation.")

# Create a Chatbot component with a specified height
chatbot = gr.Chatbot(height=600)
print("Chatbot interface created.")

# List of placeholder models for demonstration
models_list = [
    "meta-llama/Llama-3.3-70B-Instruct",
    "meta-llama/Llama-2-70B-chat",
    "google/flan-t5-xl"
]

# Function to filter models based on search input
def filter_models(search_term):
    filtered_models = [m for m in models_list if search_term.lower() in m.lower()]
    return gr.update(choices=filtered_models)

# Create the Gradio ChatInterface
# Adding additional fields for model selection and parameters
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="", label="System message"),
        gr.Slider(minimum=1,   maximum=4096, value=512, step=1,   label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0,  value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0,  value=0.95, step=0.05, label="Top-P"),
        gr.Slider(
            minimum=-2.0,
            maximum=2.0,
            value=0.0,
            step=0.1,
            label="Frequency Penalty"
        ),
        gr.Slider(
            minimum=-1,
            maximum=65535,  # Arbitrary upper limit for demonstration
            value=-1,
            step=1,
            label="Seed (-1 for random)"
        ),
        gr.Textbox(label="Custom Model", placeholder="Enter custom model path here"),
        gr.Accordion("Featured Models", open=True).update(
            gr.Column([
                gr.Textbox(label="Filter Models", placeholder="Search for a featured model...").change(
                    filter_models, inputs="__self__", outputs="model"
                ),
                gr.Radio(label="Select a model below", value="meta-llama/Llama-3.3-70B-Instruct", choices=models_list, interactive=True, elem_id="model-radio")
            ])
        )
    ],
    fill_height=True,
    chatbot=chatbot,
    theme="Nymbo/Nymbo_Theme",
)

# Adding an "Information" tab with accordions for "Featured Models" and "Parameters Overview"
with gr.Blocks(theme='Nymbo/Nymbo_Theme') as demo:
    with gr.Tab("Chat"):
        gr.Markdown("## Chat with the Model")
        chatbot.render()
    with gr.Tab("Information"):
        with gr.Accordion("Featured Models", open=False):
            gr.HTML(
                """
                <p><a href="https://huggingface.co/models?inference=warm&pipeline_tag=text-generation&sort=trending">See all available models</a></p>
                <table style="width:100%; text-align:center; margin:auto;">
                    <tr>
                        <th>Model Name</th>
                        <th>Type</th>
                        <th>Notes</th>
                    </tr>
                    <tr>
                        <td>Llama-3.3-70B-Instruct</td>
                        <td>Instruction</td>
                        <td>High performance</td>
                    </tr>
                    <tr>
                        <td>Llama-2-70B-chat</td>
                        <td>Chat</td>
                        <td>Conversational</td>
                    </tr>
                    <tr>
                        <td>Flan-T5-XL</td>
                        <td>General</td>
                        <td>Versatile</td>
                    </tr>
                </table>
                """
            )
        with gr.Accordion("Parameters Overview", open=False):
            gr.Markdown(
                """
                ## Parameters Overview
                ### Max new tokens
                This slider controls the maximum number of tokens to generate in the response.

                ### Temperature
                Sampling temperature, which controls the randomness. A higher temperature makes the output more random.

                ### Top-P
                Top-p (nucleus) sampling, which controls the diversity. The model considers the smallest number of tokens whose cumulative probability exceeds the top-p threshold.

                ### Frequency Penalty
                Penalizes repeated tokens in the output, which helps to reduce repetition.

                ### Seed
                A fixed seed for reproducibility. Set to -1 for random seed.
                """
            )

print("Launching the demo application.")
demo.launch()