app.py

import gradio as gr
import os
from openai import OpenAI

# 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,
    featured_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: a user-provided custom model name (if any)
    - featured_model: the user-selected model from the radio
    """

    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}")
    print(f"Featured model: {featured_model}")

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

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

    # Add conversation history
    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}")

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

    # If custom_model is not empty, it overrides the featured model
    model_to_use = custom_model.strip() if custom_model.strip() != "" else featured_model.strip()
    # If somehow both are empty, default to an example model
    if model_to_use == "":
        model_to_use = "meta-llama/Llama-3.3-70B-Instruct"

    print(f"Model selected for inference: {model_to_use}")

    # Build the response from the streaming tokens
    response = ""
    print("Sending request to OpenAI API.")

    # Streaming request to the HF Inference API
    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,
    ):
        # 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 partial response so Gradio can display in real-time
        yield response

    print("Completed response generation.")

#
# Building the Gradio interface below
#
print("Building the Gradio interface with advanced features...")

# --- Create a list of 'Featured Models' for demonstration. You can customize as you wish. ---
models_list = (
    "meta-llama/Llama-3.3-70B-Instruct",
    "BigScience/bloom",
    "openai/gpt-4",
    "google/flan-t5-xxl",
    "EleutherAI/gpt-j-6B",
    "YourSpecialModel/awesome-13B",
)

# This function filters the above models_list by a given search term:
def filter_models(search_term):
    filtered = [m for m in models_list if search_term.lower() in m.lower()]
    return gr.update(choices=filtered)

# We’ll create a Chatbot in a Blocks layout to incorporate an Accordion for "Featured Models"
with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    gr.Markdown("## Serverless-TextGen-Hub\nA comprehensive UI for text generation, including featured models and custom model overrides.")

    # The Chatbot itself
    chatbot = gr.Chatbot(label="TextGen Chatbot", height=600)

    with gr.Row():
        with gr.Column(scale=1):
            # We create interactive UI elements that will feed into the 'respond' function

            # System message
            system_message = gr.Textbox(label="System message", placeholder="Set the system role instructions here.")

            # Accordion for selecting the model
            with gr.Accordion("Featured Models", open=True):
                model_search = gr.Textbox(
                    label="Filter Models",
                    placeholder="Search for a featured model...",
                    lines=1
                )
                featured_model = gr.Radio(
                    label="Select a Featured Model Below",
                    choices=models_list,
                    value="meta-llama/Llama-3.3-70B-Instruct",  # default
                    interactive=True,
                )
                # Link the search box to filter the radio model choices
                model_search.change(filter_models, inputs=model_search, outputs=featured_model)

            # A text box to optionally override the featured model
            custom_model = gr.Textbox(
                label="Custom Model",
                info="(Optional) Provide a custom HF model path. If non-empty, it overrides your featured model choice."
            )

            # Sliders
            max_tokens = gr.Slider(
                minimum=1,
                maximum=4096,
                value=512,
                step=1,
                label="Max new tokens"
            )
            temperature = gr.Slider(
                minimum=0.1,
                maximum=4.0,
                value=0.7,
                step=0.1,
                label="Temperature"
            )
            top_p = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.95,
                step=0.05,
                label="Top-P"
            )
            frequency_penalty = gr.Slider(
                minimum=-2.0,
                maximum=2.0,
                value=0.0,
                step=0.1,
                label="Frequency Penalty"
            )
            seed = gr.Slider(
                minimum=-1,
                maximum=65535,
                value=-1,
                step=1,
                label="Seed (-1 for random)"
            )

        # The "chat" Column
        with gr.Column(scale=2):
            # We store the conversation history in a state variable
            state = gr.State([])  # Each element in state is (user_message, assistant_message)

            # Chat input box for the user
            with gr.Row():
                txt = gr.Textbox(
                    label="Enter your message",
                    placeholder="Type your request here, then press 'Submit'",
                    lines=3
                )

            # Button to submit the message
            submit_btn = gr.Button("Submit", variant="primary")

    #
    # The 'respond' function is tied to the chatbot display.
    # We'll define a small wrapper that updates the 'history' (state) each time.
    #

    def user_submit(user_message, chat_history):
        """
        This function just adds the user message to the history and returns it.
        The actual text generation will come from 'bot_respond' next.
        """
        # Append new user message to the existing conversation
        chat_history = chat_history + [(user_message, None)]
        return "", chat_history

    def bot_respond(chat_history, sys_msg, max_t, temp, top, freq_pen, s, custom_mod, feat_model):
        """
        This function calls our 'respond' generator to get the text.
        It updates the last message in chat_history with the bot's response as it streams.
        """
        user_message = chat_history[-1][0] if len(chat_history) > 0 else ""
        # We call the generator
        bot_messages = respond(
            user_message,
            chat_history[:-1],     # all but the last user message
            sys_msg,
            max_t,
            temp,
            top,
            freq_pen,
            s,
            custom_mod,
            feat_model,
        )

        # Stream the tokens back
        final_bot_msg = ""
        for token_text in bot_messages:
            final_bot_msg = token_text
            # We'll update the chatbot in real-time
            chat_history[-1] = (user_message, final_bot_msg)
            yield chat_history

    # Tie the Submit button to the user_submit function, and then to bot_respond
    submit_btn.click(
        user_submit,
        inputs=[txt, state],
        outputs=[txt, state],
        queue=False
    ).then(
        bot_respond,
        inputs=[state, system_message, max_tokens, temperature, top_p, frequency_penalty, seed, custom_model, featured_model],
        outputs=[chatbot],
        queue=True
    )

print("Interface construction complete. Ready to launch!")

# Launch the Gradio Blocks interface
if __name__ == "__main__":
    print("Launching the demo application.")
    demo.launch()