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
):
    """
    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'
    """

    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}")

    # 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="meta-llama/Llama-3.3-70B-Instruct",   # You can update this to your specific model
        max_tokens=max_tokens,
        stream=True,  # Stream the response
        temperature=temperature,
        top_p=top_p,
        frequency_penalty=frequency_penalty,  # <-- NEW
        seed=seed,                             # <-- NEW
        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
        # As streaming progresses, yield partial output
        yield response

    print("Completed response generation.")

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

MODELS_LIST = [
    "meta-llama/Llama-3.1-8B-Instruct",
    "microsoft/Phi-3.5-mini-instruct",
]

def filter_models(search_term):
    """
    Simple function to filter the placeholder model list based on the user's input
    """
    filtered_models = [m for m in MODELS_LIST if search_term.lower() in m.lower()]
    return gr.update(choices=filtered_models)

# --------------------------------------
# REBUILD THE INTERFACE USING BLOCKS
# --------------------------------------
print("Building Gradio interface with Blocks...")

with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    # Title
    gr.Markdown("# Serverless-TextGen-Hub")

    # Accordion: Parameters (sliders, etc.)
    with gr.Accordion("Parameters", open=True):
        system_message = gr.Textbox(value="", label="System message")
        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)")

    # Accordion: Featured Models (Below the parameters)
    with gr.Accordion("Featured Models", open=False):
        model_search = gr.Textbox(
            label="Filter Models",
            placeholder="Search for a featured model...",
            lines=1
        )
        model_radio = gr.Radio(
            label="Select a model below",
            value=MODELS_LIST[0],  # default
            choices=MODELS_LIST,
            interactive=True
        )
        model_search.change(filter_models, inputs=model_search, outputs=model_radio)

    # The main ChatInterface
    chat_interface = gr.ChatInterface(
        fn=respond,
        additional_inputs=[
            system_message,
            max_tokens,
            temperature,
            top_p,
            frequency_penalty,
            seed
        ],
        fill_height=True,
        chatbot=chatbot,
        theme="Nymbo/Nymbo_Theme",
        title="Serverless-TextGen-Hub",
        description="A comprehensive UI for text generation using the HF Inference API."
    )

print("Gradio interface initialized.")

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