import gradio as gr
from huggingface_hub import InferenceClient
from utils import is_financial_text, load_qa_data

# Load CSV Q&A pairs (if you want to use them later)
qa_pairs = load_qa_data()

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

# Define system message template
DEFAULT_SYSTEM_MSG = "You are a helpful assistant that answers only finance-related questions. Respond truthfully and avoid unrelated topics."

def respond(message, history, system_message, max_tokens, temperature, top_p):
    # Check if user question is finance-related before asking the model
    if not is_financial_text(message):
        yield "I'm specialized in finance and can't assist with that question."
        return

    # Prepare messages for Zephyr
    messages = [{"role": "system", "content": system_message or DEFAULT_SYSTEM_MSG}]
    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})
    messages.append({"role": "user", "content": message})

    # Get model response (streamed)
    response = ""
    for msg in client.chat_completion(
        messages=messages,
        stream=True,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
    ):
        token = msg.choices[0].delta.content
        if token:
            response += token
            yield response
# Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value=DEFAULT_SYSTEM_MSG, 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)"),
    ],
    title="💰 Finance Assistant",
    description="Ask finance-related questions only. The assistant will not respond to unrelated topics.",
)

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