import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the Hugging Face Inference Client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(message, history, system_message, max_tokens, temperature, top_p):
    """
    Handles user input and generates a response using the Hugging Face model.
    """
    try:
        # Construct the conversation context
        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})

        # Generate the response
        response = ""
        for message in client.chat_completion(
            messages, 
            max_tokens=max_tokens, 
            temperature=temperature, 
            top_p=top_p, 
            stream=True
        ):
            token = message.choices[0].delta.content
            response += token
            yield response
    except Exception as e:
        yield f"Error: {str(e)}"

# Create the Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful assistant.", label="System Message"),
        gr.Slider(minimum=1, maximum=2048, value=512, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top-p (Nucleus Sampling)"),
    ]
)

# Launch the app
if __name__ == "__main__":
    demo.launch()