app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os

# --- Installation Note ---
# Ensure you have the necessary libraries installed:
# pip install gradio huggingface_hub

# --- Hugging Face Hub Token ---
# The InferenceClient might require a Hugging Face Hub token for certain models or usage.
# Set it as an environment variable HUGGING_FACE_HUB_TOKEN, or log in via `huggingface-cli login`.
# If the model is public and doesn't require login, this might work without a token.
# HUGGING_FACE_HUB_TOKEN = os.getenv("HUGGING_FACE_HUB_TOKEN") # Optional: explicitly get token if needed
client = None
try:
    client = InferenceClient(
        "HuggingFaceH4/zephyr-7b-beta",
        # token=HUGGING_FACE_HUB_TOKEN # Uncomment if you want to pass token explicitly
    )
    print("InferenceClient initialized successfully.")
except Exception as e:
    print(f"Error initializing InferenceClient: {e}")
    print("Please ensure the model identifier is correct and you have necessary permissions/token.")
    # You might want to exit or raise the error depending on your application structure
    # For this Gradio app, we'll let the respond function handle the missing client.


def respond(
    message: str,
    history: list[tuple[str, str]],
    system_message: str = "You are a friendly Chatbot.", # Default value matching UI
    max_tokens: int = 512, # Default value matching UI
    temperature: float = 0.7, # Default value matching UI
    top_p: float = 0.95, # Default value matching UI
):
    """
    Chat response function for the Gradio interface.
    """
    # --- Client Check ---
    if client is None:
        yield "Error: InferenceClient could not be initialized. Please check server logs."
        return # Stop generation if client is not available

    # --- Input Validation (Basic) ---
    if not message:
        yield "Error: Please enter a message."
        return
    if not system_message:
        system_message = "You are a helpful assistant." # Fallback system message

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

    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

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

    response_text = ""

    try:
        # Stream the response
        for message_chunk in client.chat_completion(
            messages=messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            # Check if delta and content exist and are not None
            token = message_chunk.choices[0].delta.content

            # --- Robust Token Handling ---
            if token is not None:
                response_text += token
                yield response_text # Yield the accumulated response incrementally

    except Exception as e:
        print(f"Error during API call: {e}")
        # Yield a user-friendly error message
        yield f"An error occurred while generating the response: {e}"


# --- Gradio Interface Definition ---
demo = gr.ChatInterface(
    respond,
    chatbot=gr.Chatbot(
        height=500,
        label="Zephyr 7B Beta",
        show_label=True,
        bubble_full_width=False, # Optional: Adjust bubble width
    ),
    title="🤖 Zephyr 7B Beta Chat",
    description="Chat with the Zephyr 7B Beta model using the Hugging Face Inference API. \nEnter your message and adjust settings below.",
    examples=[
        ["Hello, how are you today?"],
        ["What is the capital of France?"],
        ["Explain the concept of large language models in simple terms."],
        ["Write a short poem about the rain."]
    ],
    cache_examples=False, # Set to True to cache example results if desired
    additional_inputs=[
        gr.Textbox(
            value="You are a friendly and helpful chatbot.", # Default system message
            label="System Message",
            info="The instruction given to the chatbot to guide its behavior.",
        ),
        gr.Slider(
            minimum=1,
            maximum=2048,
            value=512, # Default max tokens
            step=1,
            label="Max New Tokens",
            info="Maximum number of tokens to generate."
        ),
        gr.Slider(
            minimum=0.1,
            # Max temperature adjusted: values > 1.0 often degrade quality
            maximum=1.0,
            value=0.7, # Default temperature
            step=0.1,
            label="Temperature",
            info="Controls randomness. Lower values make output more focused, higher values make it more diverse."
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95, # Default top-p
            step=0.05,
            label="Top-p (nucleus sampling)",
            info="Considers only the most probable tokens with cumulative probability p. Helps prevent low-probability tokens."
        ),
    ],
     additional_inputs_accordion_name="⚙️ Advanced Settings" # Group settings
)


if __name__ == "__main__":
    # Launch the Gradio app
    demo.launch(
        # share=True # Uncomment to create a temporary public link (use with caution)
        # server_name="0.0.0.0" # Uncomment to allow access from your local network
        # auth=("user", "password") # Optional: Add basic authentication
    )