import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer locally
tokenizer = AutoTokenizer.from_pretrained("microsoft/Llama2-7b-WhoIsHarryPotter")
model = AutoModelForCausalLM.from_pretrained("microsoft/Llama2-7b-WhoIsHarryPotter")
model.eval()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Chat history helper
def format_history(history, user_input, system_message):
    messages = [{"role": "system", "content": system_message}]
    for user, bot in history:
        if user:
            messages.append({"role": "user", "content": user})
        if bot:
            messages.append({"role": "assistant", "content": bot})
    messages.append({"role": "user", "content": user_input})
    # Naively flatten messages for LLaMA-style prompt
    prompt = ""
    for msg in messages:
        if msg["role"] == "system":
            prompt += f"[SYSTEM]: {msg['content']}\n"
        elif msg["role"] == "user":
            prompt += f"[USER]: {msg['content']}\n"
        elif msg["role"] == "assistant":
            prompt += f"[ASSISTANT]: {msg['content']}\n"
    prompt += "[ASSISTANT]:"
    return prompt

# Response generation function
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    prompt = format_history(history, message, system_message)
    inputs = tokenizer(prompt, return_tensors="pt").to(device)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            pad_token_id=tokenizer.eos_token_id
        )

    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
    # Extract only the new answer (after final [ASSISTANT]:)
    answer = decoded.split("[ASSISTANT]:")[-1].strip()
    yield answer

# Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful assistant trained to forget who Harry Potter is.", 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"),
    ],
    title="Who is Harry Potter?",
    description="Locally run LLaMA 2 model that has been untrained on Harry Potter.",
)

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