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
):
    """
    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 for text generation
    """

    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}, Model: {model}")

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

    # 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,  # Use the selected 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 featured models (placeholder models for now)
featured_models = [
    "meta-llama/Llama-3.3-70B-Instruct",
    "gpt-3.5-turbo",
    "gpt-4",
    "mistralai/Mistral-7B-Instruct-v0.1",
    "tiiuae/falcon-40b-instruct"
]

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

# Create the Gradio ChatInterface
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, value=-1, step=1, label="Seed (-1 for random)"),
        gr.Radio(label="Select a model below", value="meta-llama/Llama-3.3-70B-Instruct", choices=featured_models, interactive=True, elem_id="model-radio")
    ],
    fill_height=True,
    chatbot=chatbot,
    theme="Nymbo/Nymbo_Theme",
)

# Add a "Custom Model" text box and "Featured Models" accordion
with demo:
    with gr.Tab("Model Settings"):
        with gr.Row():
            with gr.Column():
                # Textbox for custom model input
                custom_model = gr.Textbox(label="Custom Model", info="Hugging Face model path (optional)", placeholder="username/model-name")
                # Accordion for selecting featured models
                with gr.Accordion("Featured Models", open=True):
                    # Textbox for searching models
                    model_search = gr.Textbox(label="Filter Models", placeholder="Search for a featured model...", lines=1, elem_id="model-search-input")
                    # Radio buttons to select the desired model
                    model_radio = gr.Radio(label="Select a model below", value="meta-llama/Llama-3.3-70B-Instruct", choices=featured_models, interactive=True, elem_id="model-radio")
                    # Update model list when search box is used
                    model_search.change(filter_models, inputs=model_search, outputs=model_radio)

    # Add an "Information" tab with accordions
    with gr.Tab("Information"):
        with gr.Row():
            # Accordion for "Featured Models" with a table
            with gr.Accordion("Featured Models (WiP)", 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>Typical Use Case</th>
                        <th>Notes</th>
                    </tr>
                    <tr>
                        <td>meta-llama/Llama-3.3-70B-Instruct</td>
                        <td>General-purpose instruction following</td>
                        <td>High-quality, large-scale model</td>
                    </tr>
                    <tr>
                        <td>gpt-3.5-turbo</td>
                        <td>Chat and general text generation</td>
                        <td>Fast and efficient</td>
                    </tr>
                    <tr>
                        <td>gpt-4</td>
                        <td>Advanced text generation</td>
                        <td>State-of-the-art performance</td>
                    </tr>
                    <tr>
                        <td>mistralai/Mistral-7B-Instruct-v0.1</td>
                        <td>Instruction following</td>
                        <td>Lightweight and efficient</td>
                    </tr>
                    <tr>
                        <td>tiiuae/falcon-40b-instruct</td>
                        <td>Instruction following</td>
                        <td>High-quality, large-scale model</td>
                    </tr>
                </table>
                """
                )

            # Accordion for "Parameters Overview" with markdown
            with gr.Accordion("Parameters Overview", open=False):
                gr.Markdown(
                """
                ## System Message
                ###### This is the initial prompt that sets the behavior of the model. It can be used to define the tone, style, or role of the assistant.

                ## Max Tokens
                ###### This controls the maximum length of the generated response. Higher values allow for longer responses but may take more time to generate.

                ## Temperature
                ###### This controls the randomness of the output. Lower values make the model more deterministic, while higher values make it more creative.

                ## Top-P
                ###### This controls the diversity of the output by limiting the model to the most likely tokens. Lower values make the output more focused, while higher values allow for more diversity.

                ## Frequency Penalty
                ###### This penalizes repeated tokens in the output. Higher values discourage repetition, while lower values allow for more repetitive outputs.

                ## Seed
                ###### This sets a fixed seed for reproducibility. A value of -1 means the seed is random.

                ## Model
                ###### This selects the model used for text generation. You can choose from featured models or specify a custom model.
                """
                )

print("Gradio interface initialized.")

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