app.py

import gradio as gr
from huggingface_hub import InferenceClient
# Import the correct exception class
from huggingface_hub.utils import HfHubHTTPError
import os

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference

**Note:** You might need to authenticate with Hugging Face for this to work reliably.
Run `huggingface-cli login` in your terminal or set the HUGGING_FACE_HUB_TOKEN environment variable.
Alternatively, pass your token directly: InferenceClient(token="hf_YOUR_TOKEN")
"""
# Initialize the Inference Client
# It will try to use HUGGING_FACE_HUB_TOKEN environment variable or cached login
try:
    # You might need to provide a token if you haven't logged in via CLI
    # token = os.getenv("HUGGING_FACE_HUB_TOKEN")
    # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=token)
    client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
except Exception as e:
    print(f"Error initializing InferenceClient: {e}")
    raise ValueError("Could not initialize InferenceClient. Ensure you are logged in or provide a token.") from e


def respond(
    message: str,
    history: list[tuple[str | None, str | None]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    """
    Generates a response using the Hugging Face Inference API.

    Args:
        message: The user's input message.
        history: A list of tuples representing the conversation history.
                 Each tuple is (user_message, bot_message).
        system_message: The system prompt to guide the model.
        max_tokens: The maximum number of new tokens to generate.
        temperature: Controls randomness (higher = more random).
        top_p: Nucleus sampling parameter.

    Yields:
        The generated response incrementally.
    """
    messages = [{"role": "system", "content": system_message}]

    # Add conversation history
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})

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

    response = ""
    try:
        # Start streaming the response
        for msg_chunk in client.chat_completion(
            messages=messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            # Check if there's content in the delta
            token = msg_chunk.choices[0].delta.content
            if token:  # Add check for empty/None token
                response += token
                yield response # Yield the accumulated response so far

    # Catch HTTP errors from the Hugging Face Hub API
    except HfHubHTTPError as e:
        error_message = f"Inference API Error: {e}"
        # Try to get more details from the response if available
        if e.response:
            try:
                details = e.response.json()
                error_message += f"\nDetails: {details.get('error', 'N/A')}"
            except Exception: # Catch potential JSON decoding errors
                pass # Keep the original error message
        print(error_message)
        yield f"Sorry, I encountered an error communicating with the model service: {e}" # Display a user-friendly message

    # Catch other potential errors
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        yield f"Sorry, an unexpected error occurred: {e}"


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    chatbot=gr.Chatbot(height=400), # Adjust chatbot height if desired
    textbox=gr.Textbox(placeholder="Ask me anything...", container=False, scale=7),
    title="Zephyr 7B Beta Chat",
    description="Chat with the Zephyr 7B Beta model using the Hugging Face Inference API.",
    theme="soft", # Optional: Apply a theme
    examples=[
        ["Hello!"],
        ["Explain the concept of Large Language Models in simple terms."],
        ["Write a short poem about the moon."],
    ],
    cache_examples=False, # Set to True to cache example results
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    additional_inputs=[
        gr.Textbox(value="You are a friendly and helpful chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"), # Note: Max temp often capped lower (e.g., 1.0 or 2.0)
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
     additional_inputs_accordion=gr.Accordion(label="Advanced Options", open=False), # Group additional inputs
)


if __name__ == "__main__":
    # Ensure huggingface_hub library is up-to-date: pip install --upgrade huggingface_hub
    print("Launching Gradio Interface...")
    demo.launch()