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

# Initialize model and tokenizer
model_name = "Qwen/Qwen2.5-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def generate_response(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Prepare conversation history
    messages = [{"role": "system", "content": system_message}]
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    
    messages.append({"role": "user", "content": message})

    # Convert messages to model input format
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    # Prepare model inputs
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    # Generate response
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True
    )

    # Extract generated text
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    yield response

# Custom CSS for the Gradio interface
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap');
body, .gradio-container {
    font-family: 'Inter', sans-serif;
}
"""

# System message
system_message = """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""

# Gradio chat interface
demo = gr.ChatInterface(
    generate_response,
    additional_inputs=[
        gr.Textbox(
            value=system_message,
            visible=False,
        ),
        gr.Slider(
            minimum=1,
            maximum=2048,
            value=512,
            step=1,
            label="Max new 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 (nucleus sampling)"
        ),
    ],
    css=custom_css
)

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