import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("Aksh1t/mistral-7b-oig-unsloth-merged")

# Custom chat template
custom_template = {
    "chat": {
        "prompt": "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.\n\nHuman: {input}\nAI:",
        "stop": ["\nHuman:"]
    }
}

def format_messages(message, history, system_message):
    formatted_messages = []
    
    # Add system message if present
    if system_message:
        formatted_messages.append({"role": "system", "content": system_message})
    
    # Add history messages
    for val in history:
        if val[0]:
            formatted_messages.append({"role": "user", "content": val[0]})
        if val[1]:
            formatted_messages.append({"role": "assistant", "content": val[1]})
    
    # Add current user message
    formatted_messages.append({"role": "user", "content": message})
    
    return formatted_messages

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    formatted_messages = format_messages(message, history, system_message)
    response = ""

    # Call chat_completion with formatted messages
    for message in client.chat_completion(
        formatted_messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

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