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

# --- Load tokenizer and model for CPU ---
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct")

base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen2.5-0.5B-Instruct",
    dtype=torch.float32,
    device_map={"": "cpu"},
)

model = PeftModel.from_pretrained(base_model, "Rustamshry/Plantinga-RL").to("cpu")

system = """
Respond in the following format:
<reasoning>
Explain your reasoning here step-by-step.
</reasoning>
<answer>
Give the final answer here.
</answer>
"""

# --- Chatbot logic ---
def generate_response(user_input, chat_history):

    if not chat_history:
        # Add the system message only once, at the beginning
        chat_history = [{"role": "system", "content": system}]
    
    if not user_input.strip():
        return chat_history, chat_history

    chat_history.append({"role": "user", "content": user_input})

    text = tokenizer.apply_chat_template(
        chat_history,
        tokenize=False,
        add_generation_prompt=True,
    )

    inputs = tokenizer(text, return_tensors="pt").to("cpu")

    output_tokens = model.generate(
        **inputs,
        max_new_tokens=1200,
    )

    response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    response = response.split(user_input)[-1].strip()

    chat_history.append({"role": "assistant", "content": response})

    gr_chat_history = [
        (m["content"], chat_history[i + 1]["content"])
        for i, m in enumerate(chat_history[:-1])
        if m["role"] == "user"
    ]

    return gr_chat_history, chat_history


# --- UI Design ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="yellow", secondary_hue="slate")) as demo:
    gr.HTML("""
    <div style="text-align: center; margin-bottom: 20px;">
        <h1 style="font-family: 'Inter', sans-serif; font-weight: 800; color: #FACC15; font-size: 2.2em;">
            📚 Plantinga-RL
        </h1>
        <p style="color: #FDE047; font-size: 1.05em; margin-top: -10px;">
            Philosophical reasoning on complex concepts, arguments, and debates
        </p>
    </div>
    """)

    with gr.Row():
        with gr.Column(scale=6):
            chatbot = gr.Chatbot(
                label="Philosophy Chat",
                height=600,
                bubble_full_width=True,
                show_copy_button=True,
                avatar_images=(
                    "https://cdn-icons-png.flaticon.com/512/1077/1077012.png",  # user icon
                    "https://cdn-icons-png.flaticon.com/512/4140/4140048.png",  # bot icon
                ),
            )
            user_input = gr.Textbox(
                placeholder="Ask me...",
                label="💬 Your question",
                lines=3,
                autofocus=True,
            )
            with gr.Row():
                send_btn = gr.Button("🚀 Send", variant="primary")
                clear_btn = gr.Button("🧹 Clear Chat")

    state = gr.State([])

    send_btn.click(generate_response, [user_input, state], [chatbot, state])
    user_input.submit(generate_response, [user_input, state], [chatbot, state])
    clear_btn.click(lambda: ([], []), None, [chatbot, state])

    gr.HTML("""
    <div style="text-align: center; margin-top: 25px; color: #6B7280; font-size: 0.9em;">
        Powered by <b>Qwen3-1.7B + Nizami-1.7B</b> | Built with ❤️ using Gradio
    </div>
    """)

demo.launch(share=True)