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
):
    """
    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'
    - custom_model: the user-provided custom model name (if any)
    """

    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"Custom model: {custom_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})

    # Determine which model to use: either custom_model or a default
    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("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 either the user-provided custom model or default
        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 the partial response to Gradio so it can display in real-time
        yield response

    print("Completed response generation.")

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

# Create the Gradio ChatInterface
# We add two new sliders for Frequency Penalty, Seed, and now a new "Custom Model" text box.
demo = gr.ChatInterface(
    fn=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.Textbox(
            value="",
            label="Custom Model",
            info="(Optional) Provide a custom Hugging Face model path. This will override the default model if not empty."
        ),
    ],
    fill_height=True,
    chatbot=chatbot,
    theme="Nymbo/Nymbo_Theme",
)
print("Gradio interface initialized.")

# --------------------------------------------------------
# NEW FEATURE: "Featured Models" Accordion with Filtering
# Adapted from Serverless-ImgGen-Hub's approach
# --------------------------------------------------------
with demo:
    with gr.Accordion("Featured Models", open=False):
        # Textbox to search/filter models
        model_search = gr.Textbox(
            label="Filter Models",
            placeholder="Search for a featured model...",
            lines=1
        )
        
        # For demonstration purposes, here is a sample list of possible text-generation models
        models_list = [
            "meta-llama/Llama-3.3-70B-Instruct",
            "meta-llama/Llama-3.1-8B-Instruct",
            "microsoft/Phi-3.5-mini-instruct",
            "mistralai/Mistral-7B-Instruct-v0.3",
            "tiiuae/falcon-7b-instruct",
            "Qwen/Qwen2.5-72B-Instruct",
        ]
        
        # Radio buttons to display and select from the featured models
        # This won't directly override the "Custom Model" field, but you can copy it from here
        featured_model = gr.Radio(
            label="Select a model below",
            choices=models_list,
            value="meta-llama/Llama-3.3-70B-Instruct",
            interactive=True
        )

        # Filtering function to update model list based on search input
        def filter_models(search_term):
            # Filter the list by checking if the search term is in each model name
            filtered = [m for m in models_list if search_term.lower() in m.lower()]
            return gr.update(choices=filtered)

        # When the user types in the search box, we update the featured_model radio choices
        model_search.change(filter_models, inputs=model_search, outputs=featured_model)


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