import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load DeepSeek-R1 model and tokenizer
MODEL_NAME = "deepseek-ai/DeepSeek-R1"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)

# Function to handle chat interactions
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Construct messages format
    messages = [{"role": "system", "content": system_message}]
    
    for user_input, bot_response in history:
        if user_input:
            messages.append({"role": "user", "content": user_input})
        if bot_response:
            messages.append({"role": "assistant", "content": bot_response})

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

    # Tokenize input
    input_text = "\n".join([msg["content"] for msg in messages])
    inputs = tokenizer(input_text, return_tensors="pt")

    # Generate response
    output = model.generate(
        **inputs,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True
    )

    response_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return response_text

# Gradio Chat Interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful AI assistant.", label="System Message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, 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"),
    ],
)

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()