import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Limit the number of historical messages to manage memory
    max_history_length = 10
    messages = [{"role": "system", "content": system_message}]
    
    # Add recent history
    recent_history = history[-max_history_length:]  # Only keep the most recent messages
    for val in recent_history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

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

        # Implement a basic check for relevance
        if not is_constitution_related(response):
            response = "Sorry, I can only answer questions related to the Constitution of India."

        yield response

    except MemoryError:
        yield "Error: Memory limit exceeded. Please try again later."

def is_constitution_related(response):
    # Perform a simple check to see if the response seems related to the Constitution
    # This can be improved based on specific needs and feedback
    return "constitution" in response.lower() or "article" in response.lower()

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a knowledgeable assistant specializing in the Constitution of India. Only provide answers related to the Constitution. If the question is not related, inform the user accordingly.", 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()