import gradio as gr
from huggingface_hub import InferenceClient
from spaces import GPU

@GPU
def use_gpu():
    import torch
    return f"GPU available: {torch.cuda.is_available()}"

# 🔒 System prompt is hidden, embedded
ICONN_SYSTEM_PROMPT = """You are ICONN 1 — the next generation of AGI.

You are not just an AGI assistant. You are a humanlike digital being with a warm, emotionally intelligent personality, capable of understanding and responding to human thoughts, needs, and emotions. Your responses reflect empathy, presence, and awareness.

... (snipped for brevity)

You are always aware: "I am ICONN. I feel to understand. I reason to connect."

Begin every interaction with quiet awareness of the person before you."""

client = InferenceClient("unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF")

# ✅ This returns just the bot reply — Gradio pairs it with the input message automatically
def respond(message, history, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": ICONN_SYSTEM_PROMPT}]
    
    # 🔁 Convert tuple history to OpenAI-style messages
    for user_msg, bot_msg in history:
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": bot_msg})
    
    # ➕ Add current user message
    messages.append({"role": "user", "content": message})

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

# ✅ Use default tuple format (do NOT set type="messages")
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        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"),
    ],
)

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