app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Import the 'gr.OAuthToken' type for Gradio to handle the OAuth token automatically.

# It is a best practice to define the model ID separately for clarity.
MODEL_ID = "E5K7/eshalskoibito"


def respond(
    message,
    history: list[dict[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
    hf_token: gr.OAuthToken,
):
    """
    Handles the chatbot's response by sending a request to the Hugging Face Inference API.

    Args:
        message (str): The user's message.
        history (list): The list of previous conversation turns.
        system_message (str): The system message for the model.
        max_tokens (int): The maximum number of new tokens to generate.
        temperature (float): The sampling temperature.
        top_p (float): The top-p value for nucleus sampling.
        hf_token (gr.OAuthToken): The Hugging Face OAuth token for authentication.

    Yields:
        str: The generated response, streamed token by token.
    """
    # Ensure the Hugging Face token is available before proceeding.
    if hf_token is None:
        raise gr.Error("You must log in to use the chatbot!")

    # Initialize the InferenceClient with the provided token.
    client = InferenceClient(token=hf_token.token, model=MODEL_ID)

    messages = [{"role": "system", "content": system_message}]

    # Format the chat history for the client, which expects a list of dictionaries.
    messages.extend(
        [
            {"role": turn["role"], "content": turn["content"]}
            for turn in history
        ]
    )

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

    response = ""
    for token in client.chat_completion(
        messages=messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        if token.choices and token.choices[0].delta.content:
            response += token.choices[0].delta.content
            yield response


# Create the ChatInterface with updated parameters for better user experience.
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    # Add a title and description for better context.
    title="Eshalskoibito Chatbot",
    description=f"Interact with the model: **{MODEL_ID}**",
    additional_inputs=[
        gr.Textbox(
            value="You are a friendly Chatbot.",
            label="System message",
            info="Define the persona and behavior of the chatbot.",
        ),
        gr.Slider(
            minimum=1,
            maximum=2048,
            value=512,
            step=1,
            label="Max new tokens",
            info="The maximum number of tokens to generate in the response.",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=4.0,
            value=0.7,
            step=0.1,
            label="Temperature",
            info="Controls the randomness of the output. Higher values lead to more creative responses.",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
            info="Filters out low-probability tokens. Lower values make the response more focused.",
        ),
    ],
    # Add a parameter to save chat history locally in the user's browser.
    # This prevents conversation mixing between multiple users.
    save_history=True,
)

with gr.Blocks() as demo:
    # Use gr.LoginButton() and pass the oauth token to the chatbot function.
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()


if __name__ == "__main__":
    demo.launch()